Download Contrast enhancement in photoacoustic imaging: a multi

Contrast enhancement in
photoacoustic imaging:
a multi-dimensional approach
A thesis submitted in partial
fulfillment for the degree of
Master of Science
Author:
Pieter Kruizinga
Supervisor:
Dr. Koen van Dongen
October 7, 2010
Graduation Committee
Prof. dr. ir. P.M. van den Berg
Laboratory of Acoustical Imaging and Sound Control
Department of Imaging Science and Technology
Faculty of Applied Sciences
Delft University of Technology
Dr. A.J.L. Adam
Optics Research Group
Department of Imaging Science and Technology
Faculty of Applied Sciences
Delft University of Technology
Dr. ir. D.J. Verschuur
Laboratory of Acoustical Imaging and Sound Control
Department of Imaging Science and Technology
Faculty of Applied Sciences
Delft University of Technology
Dr. K.W.A. van Dongen
Laboratory of Acoustical Imaging and Sound Control
Department of Imaging Science and Technology
Faculty of Applied Sciences
Delft University of Technology
“While we look not at the things which are seen, but at the
things which are not seen”
Bible, 2 Corinthians 4:18 (King James Version)
Abstract
Photoacoustic imaging is a relative new imaging technique that refers to
the generation of acoustic waves upon the absorption of light. When the
deposition of this energy is sufficiently short, an adiabatic thermo-elastic
expansion takes place whereby acoustic waves are generated. These acoustic
waves will propagate away from the source and can be recorded and
stored to construct an image. Despite its straightforward nature, there
are some intrinsic deficiencies related to this technique. In this thesis
three of these deficiencies are treated and possible solutions are presented.
The first concerns the problems that arise when measuring photoacoustic
signals resulting from small sources. In chapter two, analytical expressions
are derived and the usage of increasing laser pulse duration to overcome
amplitude mismatch is analyzed. The third chapter deals with the stability
of photoacoustic contrast agents under high laser fluence exposure. A new
method is presented to enhance the stability of these agents. The fourth
chapter is devoted to the processing of photoacoustic signals. Herein a
new denoising technique is presented which is compared to the commonly
used averaging method. Furthermore, this thesis bundles the results of ten
months of research at the Ultrasound Imaging and Therapeutics Research
Laboratory of the University of Texas at Austin, USA.
Acknowledgements
There are only a few official occasions in life where one is allowed to
acknowledge the fact that one is not alone. This is one of those. Koen,
without you, no single word in this document could have seen the light.
Thanks for the critical comments and your fair judgment(s). Ton, thanks
for picking up the phone two years ago. Gijs, the past guarantees a future.
Stas, remember that Leffe Blond we enjoyed? Thanks for a wonderful
year and allowing me to ‘cook along in your kitchen’. It is true that
sometimes a word says more than a sentence. Therefore; Mohammad,
friend and MMUS-bbq, Seungsoo, ultrasound and discuss, Jimmy cheese,
Jason Suburban, Sangpil non-linearity, Yun-Sheng science, Bo PA, Valli
experiments, Andrei inventing, Salavat chess, Kim discipline, Erika
icecream, Iulia, high heels, Tera correction Katie, Doug, Min, Seung
Yun, Soon Yoon, Alex, Pratixa, Chris joy. The last word summarizes
a wonderful year with beautiful people and interesting research.
I would like to thank my mom and dad for taking care of me from diaper
to university and beyond. It was probably not always an easy job, but you
did it without any reserve. In light of that, it would be silly to not mention
my wonderful sisters, who I deeply love. Also thanks to my family in law,
especially for having the guts to visit us in deep Texas. But there is one
person that followed me all along; Grethe, the joy in my life, without your
support and love, this whole undertaking would have been totally useless. I
thank you for all the care you gave, the miles we traveled, the conversations
that never ended, the joy we felt and the moments we experienced, always
together; I love you. Above all I want to acknowledge my heavenly Father
for His Being, His Presence and His Care. Working in science allows glimpses
of His creation. Thank You for giving me that opportunity.
Through all these people I whole heartedly underscore the philosophical
understanding of ubuntu.
Abbreviations
1D, 2D and 3D one, two and three-dimensional
PAI photoacoustic imaging
PA photoacoustic
Vis visible wavelength range (400 − 700 nm)
NIR near infra-red wavelength range (700 − 1100 nm)
A-line acoustic line
SNR signal-to-noise ratio
MNP metallic nanoparticle
PEG poly-ethyleneglycol
CTAB cetyltrimethyl-ammoniumbromide
TEM transmission electron microscopy
MCS Monte Carlo simulation
SCW signal content windowing
Contents
Abstract
1
Acknowledgements
2
Abbreviations
3
1 Introduction
6
1.1
Photoacoustics in medical imaging . . . . . . . . . . . . . . .
8
1.2
Thesis overview . . . . . . . . . . . . . . . . . . . . . . . . . .
13
2 Signal generation: changing laser pulse duration
14
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
2.2
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
2.3
Materials and Methods
. . . . . . . . . . . . . . . . . . . . .
25
2.4
Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
2.5
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
2.6
Conclusion
36
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 Metallic contrast agents: improving stability
38
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38
3.2
Materials and Methods
. . . . . . . . . . . . . . . . . . . . .
40
3.3
Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
3.4
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
44
3.5
Conclusion
46
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
4 Signal processing: denoising scanlines
48
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
4.2
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
4.3
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
5
Contents
4.4
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
61
4.5
Conclusion
63
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 Discussion and conclusions
64
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
5.2
General discussion . . . . . . . . . . . . . . . . . . . . . . . .
64
5.3
Future prospects . . . . . . . . . . . . . . . . . . . . . . . . .
66
5.4
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
Published papers
68
References
69
1
Introduction
Medical imaging, as a research field, encompasses all the techniques that
are employed to create images of the human body for clinical evaluation.
The ultimate goal of this field is to produce the ‘best’ possible image that
meets the demands of every specific clinical case. The word ‘best’ here may
entail a lot of different things. However, in the field of medical imaging the
terms ‘contrast’ and ‘resolution’ are probably the ones most used to describe
what is ‘good’ and what is ‘bad’. An imaging technique, for instance, that
provides good contrast and high resolution of the anatomical structure of the
human body is radiography or x-ray (high energetic photons) imaging. Here
an image is a two-dimensional (2D) projection of the x-ray absorbing objects
that are in between the x-ray source and the detector. Anatomical structures
that have a relative high density (from here, density = mass/volume), such
as bones, can easily be imaged with this technique. Additionally, with the
use of computed tomography (CT) multiple 2D projections can be turned
into a three-dimensional (3D) image.
A downside of x-ray imaging is the limitation of providing contrast between
different structures that have similar density. Here magnetic resonance
imaging (MRI) proves useful. MRI is an imaging technique that is able
to map the magnetic properties of atomic nuclei. It does so by aligning
the hydrogen atoms to a magnetic field after which this alignment is
disrupted by an additional electromagnetic field. Different tissue types can
be discerned by monitoring the behavior of the hydrogen atoms during
these processes of alignment and de-alignment. Thus, MRI provides
contrast based on hydrogen composition of the tissue. Besides providing 3D
anatomical information, this technique can also be used to provide functional
(physiological processes) information of the human body.
Another type of medical imaging technique is radionuclide imaging. Here
small radioactive sources are delivered to the human body after which
the radiation from these sources is detected with external detectors. The
7
Introduction
imaging source now is located within the body, similar to MRI. The great
advantage of this type of imaging is that the sources can be designed to
localize tumors. The contrast mechanism of this technique is based on
mapping source radiation. This imaging principle can also be used to
produce 3D images.
At the other side of the spectrum is ultrasound imaging. This modality
uses high frequency (1 − 50 MHz) acoustical waves to produce images of
the human body. The contrast given by ultrasound imaging is based on
the reflection and scattering of ultrasonic waves by acoustic impedance
mismatches of different tissue types. An image is formed by the transmission
and detection of acoustic waves. The time between transmission and
echo along with the intensity and location of the echo are the parameters
from which an image can be constructed. This technique features some
resemblance to x-ray imaging in terms of e.g. imaging densities. However,
the geometrical setting and the possibility of mapping the ‘time of flight’ of
each acoustic wave allows for 3D imaging where the source and detector can
be positioned on the same imaging plane.
These four types of medical imaging techniques cover a whole spectrum of
possible images that can be made to suit e.g. the demands of the clinician
to make a good diagnosis. But everything comes with a price. All of
these techniques have their advantages, but moreover their disadvantages.
X-ray imaging, for example, provides clear contrast when there are density
differences but also exposes the human body to potentially harmful levels
of radiation. MRI can make beautiful high-contrast images, but the costs
of these images are relatively high and the image acquisition time does
not allow much room for ‘real time’ imaging [1]. Equally, radio-nuclide
imaging can be very useful but the contrast is very poor and the side effects
of radioactive radiation can be very severe. Ultrasound imaging on the
other hand is very safe in terms of side effects, is low in costs, and can
achieve real-time imaging. However, the contrast between different soft
tissue structures can be very low and therefore functional information is
hard to obtain.
A possible addition to the spectrum would be a technique that can
compensate for the downsides of ultrasound imaging and not forfeit too much
of the advantages this technique already has. Photoacoustic imaging (PAI)
is one of these contenders. It provides contrast between tissue structures
based on the optical absorption of pulsed laser light. This technique has
high potential in terms of providing functional information, high resolution,
fast scanning speed, and low costs.
Photoacoustics in medical imaging
1.1
8
Photoacoustics in medical imaging
Photoacoustics (PA) also known as “optoacoustics”, is a technique that
maps acoustic signals resulting from thermo-elastic expansions upon the
absorption of fast modulated (laser) light. The firsts scientific observation
of the photoacoustic effect (that light can produce sound) dates back to A.G.
Bell in the late 1800s [2]. However, it was not until the late 1970s that the
research in this phenomenon really took off. This increasing interest had to
do with the development of lasers and electronics for acoustic detection and
recording.
The imaging domain for PAI can be understood as being bi-partite. The
first is the optical sub-domain. Here, laser light incident on the object of
interest travels, via various mechanisms, through the object and encounters
regions where optical absorption is dominant. In this case, where the light
is absorbed, heat will be generated. Now, when the laser pulse is sufficiently
short and there is no time for the heat to dissipate in the medium resulting
in a disturbed equilibrium, which brings us to the other sub-domain; called
acoustics.
Since there is a local increase of heat, and no volume change yet, there
must be a local increase of pressure (according to the “ideal gas law”).
This pressure, due to the disturbed equilibrium, hence will propagate out
of the volume of high pressure to the volume of relative lower pressure.
This propagating of pressure equals an acoustic pressure wave which has a
certain speed, amplitude, and direction. Since this acoustic pressure wave
has an origin in time and space and attributes certain properties of direction
and speed, the photoacoustic source can be located in a 3D space. This is
the basic imaging principle of PAI. In the next three paragraphs, these two
sub-domains (optical and acoustic) will be treated separately followed by an
overview of the possible imaging applications of PAI.
Optical domain For the purpose of this thesis, light is referred to
as ‘electromagnetic radiation with a wavelength ranging from the visible
part (Vis) of the spectrum, 400 − 700 nm, to the near infra-red (NIR),
which is 700 − 1100 nm’. The two processes that dominate in light
interacting with tissue are ‘scattering’ and ‘absorption’. The strength of
these interactions depends heavily on the wavelength of the light and the
molecular constituents of the interacting tissue.
9
Introduction
Having many interaction processes make exact propagation of light through
tissue very hard to predict. There are several Monte Carlo simulations
(MCS) that can model the distribution of photon energy in tissue, but
the solution will be never exact. Two useful examples of MCS models
can be found in [3, 4]. An actual MCS program can be downloaded
from [5]. However a more practical analytical approximation of light
transport through tissue is given by the diffusion theory. Here the effective
attenuation a of light is approximated per unit length d with use of Beer’s
law (a = 1 − e−µeff d ) and the effective attenuation coefficient. The effective
attenuation coefficient µeff can be approximated by:
p
µeff = 3µa (µa + µs (1 − g)),
(1.1)
where µa is the absorption coefficient, µs the scattering coefficient and g
the anisotropy factor, which is around 0.9 for tissue in the Vis-to-NIR
wavelength range [6, 7]. Note that µa and µs are both wavelength dependent.
For detailed information about optical properties of tissue and exact
numbers see [6, 8, 9]. In figure 1.1 a few absorption and extinction spectra of
tissues are shown. The lower absorption region of 700 - 900 nm is in medical
imaging often referred to as the ‘optical window’. An interesting paper on
optical properties of skin and penetration dept is given by Bashkatov et al.
[10]. The optical properties of blood are extensively studied by the group
of Müller; MCS for the optical properties of blood can be found in [11],
whereas actual experimental data can be found in [12]. Unlike other optical
techniques, the resolution of PAI does not suffer much from the scattering of
photons. Scattering might even support a homogenous photon distribution
which can be beneficial for effective PA wave generation. The limiting factor
that PAI shares with other optical techniques is the low penetration depth
of light in tissue. Nevertheless PAI only requires a ‘one-directional’ light
delivery and can therefore image much deeper than most of the other optical
techniques, which need some type of reflection. Therefore penetration depths
of several centimeters into biological tissue can be reached [14].
Acoustic domain The acoustic domain becomes of interest when the
photoacoustic source emerges after light absorption. The photoacoustic
source then obeys the acoustic wave equation in three dimensions:
2
1 ∂2P
∂
∂2
∂2
1 ∂2P
2
∇ P− 2 2 =
+
+
−
= −Q,
(1.2)
v ∂t
∂x2 ∂y 2 ∂z 2
v 2 ∂t2
where v is the speed of sound, P pressure, t time and Q denotes the
photoacoustic source. The spatial coordinates are given by x, y and z. The
actual origin and behavior of the photoacoustic source will be treated with
Photoacoustics in medical imaging
10
Figure 1.1: Optical absorption µa and extinction µa + µs coefficients
for blood, tissue, muscle and skin. Data obtained from [13].
more detail in the introduction and theory section of the coming chapter. For
now, only the propagation through the acoustic domain will be of interest.
Despite the validity of the nonlinear behavior of acoustic waves, the linear
model gives for most cases a fair approximation. The linear model assumes
the medium to be an ideal homogeneous liquid which is infinitely continuous.
According to equation (1.2) every acoustic pressure-wave is uniquely defined
in space and time, and by the notion of the acoustic speed:
v2 =
1
Kρ
(1.3)
every wave also attributes the property of wavelength and frequency. Here
K (m2 /N ) denotes the compressibility of the medium and ρ (kg/m3 ) is the
density of the medium. An acoustic pressure wave is a longitudinal wave
that propagates via compression and decompression of the medium. The
interaction of an acoustic wave with the medium can be loosely categorized
in: reflection, refraction, attenuation by scattering and attenuation by
absorption. The first two, reflection and refraction, are dependent on the
impedance differences at the interface of two media. The characteristic
acoustic impedance Z is defined by
Z = v ρ.
(1.4)
11
Introduction
The ratio of the acoustic energy that is reflected and refracted upon
impedance mismatch depends on the angle of incidence and the difference
in Z. When the acoustic inhomogeneities are smaller than the acoustic
wavelength, a part of the acoustic energy may be scattered in random
directions and therefore causes the signal to attenuate. The last interaction,
attenuation by acoustic absorption, refers to all processes associated with
the loss of acoustic energy during propagation that are not related to
reflection, refraction, or scattering. In table 1.1 an overview of several
acoustic coefficients regarding the propagation of acoustic waves in air, water
and various tissues is given.
Material
Air
Water
Blood
Fat
Muscle
Skull bone
Soft tissue
Speed of
propagation
(m/s)
330
1480
1570
1450
1590
4000
1540
Characteristic
impedance
(106 kg m−2 s−1 )
0.0004
1.48
1.61
1.38
1.70
7.80
1.63
Attenuation
coefficient α at
1 MHz (dB/cm)
1.2
0.002
0.2
0.6
1.5-3.5
13
0.6
Frequency
dependence
of α
f2
f2
f 1,2
f
f
f2
f
Table 1.1: Acoustic properties of air, water and various tissues. Values obtained
from [15].
Utilizing the linear model of acoustic wave propagation, the impedance
differences within a medium can be determined and imaged. The imaging
principle relies on the backprojection of acoustic recordings that consist
of the transmission, reflection, and detection of acoustic pulses. The
resolution of the acoustical imaging technique depends on the signature of
the transmission pulse, the quality of the recording (e.g. sampling frequency
and noise) and the imaging method (e.g. phased array). In PAI however,
the imaging principle does not rely on the reflection of an acoustic pulse but
rather on the detection of an acoustic wave resulting from a photoacoustic
source that emerges from the overlap of the optical with the acoustic domain.
Photoacoustics imaging applications
Photoacoustics can be
employed for numerous subjects of (bio)medical imaging. Probably the most
obvious case is the imaging of blood vessels. Since blood is a strong absorber,
blood vessels are ideal imaging structures. PAI of subcutaneous vasculature
of the human wrist has been done by Kolkman et al. [16]. Related is the
interest in small vasculature and vascular growth (angiogenesis) that plays
Photoacoustics in medical imaging
12
an important part in the formation and development of tumors. This topic
in particular received a lot of research attention. Interesting work has been
done by Lao, Ku and Siphanto et al. [17, 18, 19]. All of these studies are
done on small animals. Since the PA signal resulting from blood depends on
the probing wavelength, the oxygenation of blood can be determined (see
figure 1.1). Fruitful work, including high quality images, is done by Zang
et al. [20]. A feasibility study of this subject is done by Sivaramakrishnan et
al. [21]. Another interesting parameter of blood that can be imaged is the
glucose concentration in blood. Work on this subject was done by Bednov
et al. [22] and by Zhao [23].
Not only the outer vascular structure is of interest, also the inner vessel
wall can be imaged using intravascular photoacoustic imaging (IVPA). This
relatively new field cooperates well with the already, in use, clinical imaging
modality of intravascular ultrasound (IVUS). The IVUS images are overlaid
with the IVPA images resulting in an image that provides structural and
functional information. Work by Sethuraman et al. showed the additional
value of spectroscopic IVPA, whereas Wang et al. deployed metallic contrast
agents to image important structures of the inner vessel wall [24, 25]. A pure
tissue characterization of post mortem arterial tissue with PAI was done by
Beard et al. [26].
Two, close to clinical applications, are the photoacoustic imaging of
port-wine stains and imaging of the female breast in the case of tumor
detection. The latter application is of interest due to lack of contrast and
harmful side effects of other imaging techniques, such as x-ray imaging. A
proto-type breast scanning device has been build and tested on real patients
by the team of Manohar et al. in The Netherlands and by Pramanik et
al. in the United States of America [27, 28, 29]. The imaging of port-wine
stains have been investigated by Viator et al. and Kolkman et al. [30, 31].
The clinical acceptance of these imaging devices is relatively high due to the
non-invasiveness and harmless level of light and ultrasound radiation.
PAI can also be utilized to image foreign objects, as is shown by Su et al. PA
images were produced from stents, embedded in vascular tissue, using the
IVPA technique, and images from metal needles buried in porcine tissue [32,
33]. This new development might be interesting since these foreign objects
will provide very high contrast due to stark optical absorption at every
possible wavelength. A similar out-of the-box option is the determination
of temperature. Using PAI Shah et al. proved that the local temperature of
tissue could be deducted from the PA amplitude, which is linearly dependent
on the temperature [34].
13
1.2
Introduction
Thesis overview
The principles of photoacoustic wave generation and imaging are rather
straight forward and have proven to be successful. However there are still
imperfections and limitations of this imaging technique. This thesis is
about three of these limitations. As the title: ‘Contrast enhancement in
photoacoustic imaging’ of this thesis suggests, the herein treated limitations
are related to the contrast aspect of PAI. It is clear that the contrast
factor is of immanent importance to the valuation of every medical imaging
technique; improvement of contrast will lead to ‘better images’ and a possible
‘better diagnosis’. Furthermore, the subtitle: ‘multi-dimensional approach’
suggests a plural improvement to the contrast given by conventional PAI.
The first dimension refers to the enhancement of contrast in the case of
small objects imaged with band-limited ultrasound transducers. The second
dimension refers to the enhancement of contrast using specialized contrast
agents. The last dimension concerns the enhancement of signal-to-noise
ratio of recorded scanlines which ultimately give contrast in photoacoustic
imaging.
First, in the coming chapter, the issue of PA signal amplitude and its
source-size dependency will be addressed. By changing the laser pulse
duration more information about the photoacoustic source can be obtained.
A new technique to stretch the laser pulse duration will be introduced and
several experiments utilizing this technique will be discussed. The results
indicate that by varying the laser pulse duration more information of the
PA source can be obtained. The subsequent, third chapter is based on work
presented in earlier publications done in close collaboration with the lead
author: Yun-Sheng Chen of the University of Texas at Austin. This chapter
will deal with the issue of the stability of metallic contrast agents under high
laser light exposure in the case of PAI. In this chapter, the synthesis and
usage of silica-coated gold nanorods will be discussed. These new particles
will be evaluated on the basis of optical stability and photoacoustic signal
stability under high energy laser light exposure. It will show that these
particles are considerably more stable than normal uncoated gold-nanorods.
In chapter four the method of averaging multiple recordings to increase the
signal-to-noise ratio (SNR) will be discussed and a new alternative approach
will be presented. This new approach will be evaluated on the basis of SNR
improvement with artificial data and real in-vivo data of a mouse tumor.
The new signal-content-windowing (SCW) method will prove to be superior
over normal averaging. This thesis will close with an overall discussion of the
major findings presented in this thesis. The possible advantages, utilization
and impact of these findings will also be part of the discussion.
2
Signal generation: changing laser
pulse duration
2.1
Introduction
Photoacoustic imaging provides contrast based on the relationship between
absorption of electromagnetic energy, adiabatic thermoelastic expansion,
and the magnitude of the resulting acoustic (pressure) waves. The acoustic
waves propagate radially outward from the source, and can be recorded and
mapped onto a 2D or 3D coordinate system. The amplitude of each recorded
photoacoustic signal is indicative of the local optical absorption, while the
spatial origin of the signal can be determined by the travel-time of each
signal after light delivery. The acoustic wave that is generated upon light
absorption obeys the following wave equation:
∇2 P −
1 ∂2P
−β ∂H
=
.
v 2 ∂t2
Cp ∂t
(2.1)
The left side of equation 2.1 indicates the normal wave equation where c
is the speed of sound, P pressure and t time. The right side describes the
photoacoustic source, where β is the thermal expansion coefficient, Cp is the
specific heat at constant pressure and H is the amount of heat generated
following light absorption [35]. In the case of a medium dominated by
absorption, H can be appreciated as the optical absorption µa times the light
fluence rate F (H = µa F ). Equation 2.1 is only valid when the laser pulse
is sufficiently short relative to the propagation of acoustic energy through
the source. This is often referred to as the stress confinement condition.
The photoacoustic wave equation formalized above can be considered as the
main formula used for the construction of photoacoustic images, whereby,
a linear relation between optical absorption µa and the measured acoustic
amplitude is assumed. However, this assumption is not valid for all cases
15
Signal generation: changing laser pulse duration
and may lead to misleading results and incorrect interpretation of images.
This can be attributed in part to the frequency-dependent characteristics of
photoacoustic signals, which contains information relating to the size and
shape of the absorber, the speed of sound in the medium, the geometry
of illumination, as well as the laser pulse shape and duration [7, 36]. To
illustrate this dependency, photoacoustic signals measured with a broadband
(0.25-20 MHz) hydrophone (Onda Corp. HGL-0200) can be examined. The
first signal was generated by a section of a black rubber band approximately
1 mm in diameter. The second signal was generated from a human hair
approximately 80 µm in diameter. The signals and associated frequency
spectra are plotted in Figure 2.1. Upon inspection of Figure 2.1, the
differences in the shape and accompanying frequency spectra of these two
signals is apparent. While both sources produce an N-shape typical of
a photoacoustic signature, the temporal information contained in each
frequency spectrum is quite different. In this particular case, this is most
likely the result of the difference in the sizes of the objects and the speed of
sound within the object. However, had these two signals been received with
a more band-limited ultrasound transducer, these differences would have
been less discernible. First, it is likely that the size of the object would be
untraceable due to the band-limitation. Second, prediction of the optical
absorption would be confounded by frequency miss-matching between the
transducer and the photoacoustic signal.
The effects of a band-limited transducer can be exemplified by figure 2.2.
In this case, a phantom consisting of two nylon filaments with equal optical
and mechanical properties but with different diameters were scanned with
a 40 MHz single element transducer (2.5 French, Atlantis SR Plus, Boston
Scientific, Inc). The filaments were separated by a distance of 2 mm and
a cross section was scanned with a step size of 10 µm. Furthermore, every
scanline consisted of 16 averages, with the distance between the transducer
element and the filaments approximately 3 mm. Finally, the sample was
illuminated at 180◦ angle with respect to the ultrasound transduction.
By examining the plots in figure 2.3, it is apparent that the duration
of the transducer’s impulse response is too short to capture both peaks
of the characteristic photoacoustic N-shape, thus restricting the ability to
correctly size the absorber. Another obvious difference in both photoacoustic
representations is the difference in amplitude of both filaments. Here,
one of the key problems for the photoacoustic imaging technique arises:
the size of the photoacoustically active object contributes to the measured
photoacoustic amplitude.
Introduction
16
Figure 2.1: Two photoacoustic signals and their frequency content. Top left; the
photoacoustic signal obtained from a black rubber of band (± 1 mm). Top right; the
frequency spectrum. Bottom left; the photoacoustic signal obtained from a ± 80 µm
black hair. Bottom right; the associated frequency spectrum.
In principle, photoacoustic imaging is a high resolution imaging technique.
However, the resolution-limitation is often set by the bandwidth and
damping of the acoustic transduction. Even in the case presented previously,
where a relative broadband high frequency transducer is used and acoustic
inhomogeneities are absent, this limitation becomes apparent.
17
Signal generation: changing laser pulse duration
Figure 2.2: Photoacoustic scan of two filaments of different size. Top left; a
schematic cross section of the two filaments. Top right; a maximum plot of the
absolute photoacoustic signal across each scanline. Bottom; 2D color map of the
absolute Hilbert transform of the acquired scanlines.
2.1.1
Problem statement and chapter overview
In summary, photoacoustic signals are distinct in shape and amplitude
depending on a unique set of parameters. Unlike conventional ultrasound,
where the detected wave matches the frequency characteristics of the
transducer, photoacoustic signals do not, a priori, match the transducer
characteristics. Therefore, band limitation can make it difficulty to correctly
determine the true optical absorption coefficient in a photoacoustic image.
The question becomes whether it is possible to overcome this deficiency, or
what other information can be used to determine if the estimated optical
absorption equals the true optical absorption.
This chapter examines whether changing the laser pulse duration will
provide more information about the true nature of the PA source. First,
an analytical solution for the cylindrical PA source will be derived.
Subsequently, the influence of laser pulse duration and optical absorption
for the measured PA amplitude will be discussed. In order to change the
laser pulse duration, a new optical delay system will be presented. With
the use of this system, a phantom consisting of seven small filaments will
be imaged. The resulting amplitudes will be compared to the model and
analyzed by their laser-pulse-duration dependency.
Theory
2.2
18
Theory
The body of literature on photoacoustics has increased rapidly since the
last decade, particularly as it applies to the field of biomedical imaging.
Several review papers dealing with this application are given by Xu and
Wang [14] and Oraevsky et al. [7]. Papers that specifically deal with the
light-tissue interaction in the photoacoustic case can be found in Jacques [37]
and in Paltauf and Dyer [38]. Pioneering work in this field is done by
Tam and a good paper by him can be found in [39]. A solid book on
general photoacoustic theory is given by two Russian researchers, Gusev
and Karabutov [40].
The mechanisms that contribute to photoacoustic wave generation have been
extensively studied. Great and insightful work was done by Diebold et al..
PA wave equations have been solved for the photoacoustic point source
[41, 42, 43] and the cylindrical geometry [44, 45, 46]. An early and still useful
solution to the spherical PA source is presented by Sigrist and Kneubühl in
1978 and can be found in [47]. The cylindrical geometry case was also solved
by Lai and Young [48], Heritier [49] and Kuo [50] et al.. Photoacoustic wave
propagation in 3D space has been studied by Cox and Beard [35, 51]. A
R toolbox that is based on this work can be found at [52].
k-space Matlab
In photoacoustic imaging, special attention has been paid to the stress
confinement condition, which describes the relation of efficient signal
generation based upon the laser pulse duration, speed of sound and the
photoacoustic source dimensions. This is of particular interest in the
determination and estimation of the object size and the true absorption
coefficients. The latter has been studied by Choi et al. [53]. Other related
work can be found in [54, 55, 37]. Furthermore, an excellent paper on
theoretical explanation of photoacoustic signal generation is written by
Hoelen and De Mul [56]. Here an explanation of the spherical photoacoustic
wave is given and applied to several source geometries. The model is also
validated by their experimental work. The theory presented in [56] forms the
basis for most of the derivations and further calculations of photoacoustic
signals within this thesis.
The number of studies undertaken to describe photoacoustic wave behavior
indicates that it is not a trivial matter. This especially applies to geometries
other than the spherical. This chapter will focus on cylindrical geometry.
This is of particular interest when studying PA signals originating from
small blood vessels. However, it is best to start with the geometry for a
19
Signal generation: changing laser pulse duration
small spherical PA source, from which, all other geometries can be solved.
In the case of gases and non-viscous fluids, PA signal generation can be
understood as a rotationally symmetric local pressure field, P . This pressure
originates from a local temperature rise accompanied with a volume change;
expressed by the relation:
∆V
= β∆T,
V
(2.2)
where β is the thermal volume expansion coefficient, T temperature and
V volume. When relating ∆V
density of the medium ρ times the
V to thep
longitudinal sound velocity v, via v = P0 /ρ0 , (see [57]), we obtain:
P0 = βρv 2 ∆T
(2.3)
This relation is strictly valid only when the laser pulse duration is sufficiently
short. Now, the temperature rise ∆T can be understood as the conversion
of absorbed light energy Ea , to heat via the specific heat capacity Cp of the
medium, giving the following relation:
P0 =
βc2
Ea = ΓEa .
Cp
(2.4)
Γ is referred to as the Grüneisen coefficient and Ea is the absorbed energy,
assumed to be µa times the laser fluence L. A more rigorous expression of
the same ∆T is given by Sigrits et al. [47].
2
− 2r
Ea
r0 +4κt
∆T (r, t) =
e
,
Cp π 3/2 (r02 + 4κt)3/2
(2.5)
where r0 is the source diameter and κ (m2 /s) represents the thermal
diffusivity. This equation describes the Gaussian spatial and temporal
temperature distribution following light absorption. When there is no
diffusion of heat (known as the thermal confinement condition) and the
heating period is much shorter than the acoustic travel time through the
source (known as the as the stress confinement condition) the PA wave
equation can be solved. When only thermo-elastic expansion upon light
absorption is considered, Sigrits et al. [47] derived the following equation
for photoacoustic pressure:
√
h
i2
2ev r − rv (t− vr )
P (r, t) = −Pmax
t−
e 0
;
r0
v
βEa v 2
Pmax =
,
(2.6)
√
(2π)3/2 ecp r02 r
Theory
20
where cp = Cp /ρ. Equation 2.6 shows that the PA wave consists of an
amplitude element and a normalized function that causes the wave to change
sign at retarded time, t − r/v = 0. The shape of a typical PA wave is given
in figure 2.3:
Figure 2.3: Arbitrary PA wave generated by a spherical source
distribution upon a δ pulse heating function with respect to
the retarded time (vt − r)/r0 . The indication of τ denotes the
peak-to-peak travel time.
This approach is valid for the 1D case, but may cause some unsatisfactory
results when applied to the 3D case. Hoelen et al. approach this
problem from the spherical source perspective, which at t = 0 contains
a homogeneous pressure distribution unequal to the surrounding pressure.
The authors argue that as a consequence, there must be an outgoing
decreasing pressure distribution originating at the center of the source and
extending to the point of observation. Associated with the outgoing pressure
distribution will be an increase of pressure due to the local decrease in
pressure. Both pressure fields, along which the PA wave will evolve, have a
1/r dependence with respect to the change in pressure. These considerations
of two pressure fields are valid through the conservation of energy. The
beauty of this interpretation is that there is no need for an artificial inverting
reflection at the origin but rather a smoothly evolving PA wave due to an
infinite negative pressure at the origin. This leads to the following expression
21
Signal generation: changing laser pulse duration
for a PA wave generated by an instantaneous homogeneous heated sphere:
r − vt P (r, t) = A0 U (Rs − vt − r) +
×
2r
U (r − |Rs − vt|)U (Rs + vt − r) U (t).
(2.7)
Where A0 is the initial pressure amplitude, Rs is the source radius, r the
distance, vt stands for the distance traveled and U (t) denotes the Heaviside
unit step function. The two distinct terms in this expression represent the
inward and outward propagating disturbance respectively.
Cylindrical waves The purpose of this chapter will be to examine the
PA wave resulting from a cylindrical source distribution. Following the work
of Hoelen and De Mul, it proves to be advantageous to treat the cylindrical
source as the sum of many small point sources (lim:R → 0). Actually, all PA
source geometries can be approximated by integration of the source volume
‘filled’ with PA spherical sources. In the case of a cylinder, this can be done
by the integration of a line across the diameter of a cylinder. To do so, the
authors showed that it is useful to express P (r, vt) by means of the temporal
derivative of a potential function Φ(r, vt), where
P (r, vt) = lim A0
R→0
r − vt
∂
U (R − |r − vt|) = A0
Φ(r, vt).
2r
∂(vt)
(2.8)
To obtain the acoustic wave generated by an absorbing spherical source,
the sum of all the point source potential functions are taken and integrated.
The potential function after integration of a homogenous cylinder with initial
pressure amplitude A0 is found to be:
Z b s 2
R − (x − r)2
dx.
(2.9)
ΦHC (R, r, vt) = A0 k0 U (vt − (r − R))
(vt)2 − x2
r−R
Here, b denotes the upper limit of the integration interval which is b = vt
when r − R < vt < r + R, and b = r + R when vt > r + R. Note
that this potential function is only valid with the far field approximation
(r R). Since a complete homogeneous temperature distribution is often
not physically achievable, we can use this potential function expression to
calculate the PA wave resulting from an inhomogeneous source distribution.
Hereby we take advantage of the superposition principle by superimposing
multiple potential functions and weighting them with the gradient of the
pressured distribution at t = 0. The potential function Φ(r, vt) then
Theory
22
becomes:
ΦEC (r, vt) =
1
4π
∞
Z
−∇P0 (r0 )ΦHC (r0 , r, vt)dr0 .
(2.10)
0
Here ∇P denotes the gradient of the initial pressure distribution. In this
expression it is of importance to set r to value far larger than the source
volume to overcome any discontinuities in the potential function. The
graph in figure 2.4 shows an arbitrary potential function and PA wave for a
cylindrical source geometry in the case of homogenous pressure distribution.
Figure 2.4: Left: a potential function of a cylindrical wave. Right: a theoretical PA
wave based on the derivative of the potential function shown left.
Amplitude considerations The PA wave equations shown above are
valid under the condition of instantaneous heating, which assumes that the
illumination takes place at an infinite time scale (δ(t)). In reality, this
condition is never met. In the case where heating is not a Dirac delta
function, but a continuous function, the resulting PA wave can be described
by the convolution of the pressure function P (r, t) with a heating function
T (t):
Z ∞
P (r, t) =
Pδt (r, t − τ )T (τ )dτ.
(2.11)
−∞
A typical laser-light-induced heating function would be a Gaussian function
described by:
T (t) =
1
1
2
√
e− 2 (t/τl ) .
( 2πτl )
(2.12)
23
Signal generation: changing laser pulse duration
Here, τl indicates the full width at half maximum (FWHM) of the laser
pulse duration. This convolution method is also discussed by Diebold
et al. [42] and Gusev et al. [40]. From the convolution it can be
appreciated that the maximum pressure depends on the laser pulse duration
τl . This interdependence is related to the aforementioned stress confinement
condition and the associated coefficient.
For spherical source geometry, Lai et al.[48] and Hoelen et al. found that
the laser pulse affects the maximum possible pressure by the relation:
Pmax (coeff) =
τa2
1
,
+ τl2
where
τa =
R
v
(2.13)
The stress coefficient S can thus be expressed as:
S=
τa2
.
τa2 + τl2
(2.14)
A similar relation is found by other authors [55, 37, 7]. For instance,
Dingus et al.[54] showed that the stress confinement coefficient in the case
of inhomogeneous (thus depending on µa ) pressure distribution can be
approached by:
S=
1 − e−τs
,
τs
where
τs =
τl
.
τa
(2.15)
This relation was verified experimentally by Allen and Beard [55]. Figure 2.5
demonstrates the dependence of the laser pulse duration on the stress
confinement coefficient. The values are based on a spherical source with
a diameter of 50 µm and a sound velocity of 1500 m/s.
A final consideration of the amplitude of PA waves relates to frequency. PA
waves often show a much broader frequency spectrum than those observed in
conventional ultrasound. Therefore, the frequency dependent attenuation as
well as the ultrasound frequency characteristics play a dominant part in the
correct interpretation of detected PA waves. In soft tissue, the absorption
of high frequencies is often due to thermoviscous absorption and shows a
linear relationship to the frequency [58]. However, in the case of water,
absorption has a frequency squared dependence that goes as α = γf 2 , where
γ = 25 × 10−15 s2 m−1 [56, 57]. According to Hoelen et al., its amplitude
spectrum can be expressed as:
r
βEa −(2(πτe )2 +γr)f 2
γr
|PA (r, f )| =
fe
, and τe (r) = τl2 + τa2 + 2 .(2.16)
2cp r
2π
Theory
24
Figure 2.5: Stress confinement for a 50 µm spherical source
numerically calculated with two different methods.
In water, the absorption coefficient ranges from 0.3-0.9 dB/cm/MHz, see also
table 1.1 and [59, 60, 15]. Despite the limited depth penetration of light in
tissue, and thus the low travel distance of the PA wave, the attenuation of
high frequencies component that constitute the two PA-wave-peaks might
still be of influence of the maximum pressure detected.
A second aspect related to the frequency characteristic of PA waves is the
frequency band limitation of conventional ultrasound transducers. There is
a known tradeoff between sensitivity and band width of these transducers.
This band-limitation does of course restrict the resolution in resolving
separate PA sources but it also alters the maximum amplitude measured.
As mentioned before, the PA wave signature depends on several parameters
such as source geometry and pressure distribution. When imaging an object
with unknown PA sources that differ in their geometry, two different PA
signals and two different PA maxima may be expected, which will depend
strongly on the matching of the transducer frequency band and the frequency
characteristics of the PA waves. In order to mimic the behavior of a band
limited ultrasound transducer, a simple band-pass filer can be applied to the
theoretical signals calculated.
To summarize, band limitation of ultrasound transducers may cause
inaccurate estimation of the true size and optical absorption coefficient by
indicating a false PA maximum. Since we know that the true PA maximum
25
Signal generation: changing laser pulse duration
is subject to the duration of the heating function we might be able to utilize
this dependence in order to extract the size or absorption properties of the
PA source. A previous study by Choi et al. showed that a better estimation
of the absorption coefficient can be obtained by altering the laser pulse
duration [53]. Therefore, this study will focus on the relationship between
the size of the source and the maximum PA signal. by monitoring the change
in PA amplitude with increasing laser pulse duration. This study will be
performed with small cylindrical sources and the results verified with the
theory presented above.
2.3
2.3.1
Materials and Methods
Pulse delivery system
For the purpose of this study, it is necessary to vary the laser pulse duration.
Lasers that possess this feature are primarily pulsed laser diodes with
tunable pulse durations. An advantage of laser diodes is that they are able
to function at faster repetition rates than the Q-switch lasers normally used
for photoacoustic applications. However, a serious drawback of laser diodes
is their low output power that does not come close to the higher power, but
slower repetition rate Q-switch lasers [55, 61].
A typical laser pulse from a Q-switch laser has a Gaussian beam profile
and for a neodymium-doped yttrium aluminium garnet (Nd:YAG) laser,
a duration of typically 4-9 nanoseconds (FWHM)[37]. Unfortunately, the
pulse duration of Q-switch lasers is not tunable. In order to increase the
laser pulse duration and still take advantage of the high-energy pulses of
a Q-switch laser, it was necessary to develop a new optical delay system.
The basic idea for this system is that multiple pulses can be combined to
make a single long pulse. However, since the repletion rate of Q-switch
lasers is low, typically 5 -50 Hz, only one pulse can be used. To make this
possible, a single laser pulse is split into several pulses, each with the same
pulse duration but lower in amplitude. These split pulses are individually
delayed, where after they are merged to form one long pulse.
The optical delay is realized by using different lengths of optical fibers.
Since the duration of a laser pulse traveling through an optical clear fiber is
determined by the length of the fiber lf , the index of refraction of the fiber
Materials and Methods
26
material nf and the speed of light c in a vacuum (3.0 × 108 m/s) is given
by the relation:
tdelay =
lf nf
c
(2.17)
In figure 2.6 a schematic overview of the optical delay system is shown.
Figure 2.6: Schematic overview of the pulse delivery system
It is possible to utilize multiple fibers of different lengths for every pulse
composition. A few of these possible combinations for two different lasers
are shown in figures 2.7 and 2.8. The first is a Q-switch Alexandrite laser
(Candela corp.) operating at 755 nm with a pulse-duration of 50 ns. The
second laser, which also will be used throughout this study, is a tunable
optical parametric oscillator (OPO), operating at any wavelength in the
spectrum [680 − 950 and 1200 − 2400 nm] which is pumped by a Nd:YAG
Q-switch laser (Vibrant B, Opotek, Inc.) with a pulse duration of 7 ns.
The optical fibers were 600 µm low OH, step index fibers with an NA of
0.39 (FT600EMT Thorlabs). The laser light was focused, collimated, and
equally distributed over the 5 fibers. At the distal end, the fibers were joined
together and tapered to another optical fiber with a diameter of 1.5 mm.
The resulting pulses were measured with a fast, silicon biased photodiode
with a rise time of 1 ns (DET10A Thorlabs). The photodiode was either
coupled to a digital oscilloscope with a maximum sampling rate of 2.0 GS/s
(Tektronings TDS 2024) or a computer based digitization card (Gage-card)
with a maximum sampling rate of 0.2 GS/s.
2.3.2
Phantom
To perform experiments, a phantom was made consisting of seven
different plastic filaments mounted on a metal support. The filaments
were pieces cut from ordinary fishing line ranging in diameter from
360, 250, 200, 120, 100, 80, and 60 µm. The three largest filaments were made
27
Signal generation: changing laser pulse duration
Figure 2.7: Alexandrite laser pulse with 5 different optical delay settings
Figure 2.8: Nd:YAG-OPO laser pulse with two different optical delay settings
of polyamide (produced by Berkley Corp.) the four smaller ones were also
made of polyamide (nylon) but these were explicitly sold as monofilament
R
lines (StroftGTM).
This might entail that the speed of sound in the four
smaller filaments might be slightly higher compared to the three bigger ones.
The speed of sound in nylon is in the range of 2500-2650 m/s.
The initial optical absorption of these filaments was too low to produce
any sensible PA response. The filaments were therefore submerged for 48
hours in a diluted ink solution. By close examination, the filaments showed
complete absorption of the ink solution and therefore produced higher PA
responses. In figure 2.9 a photograph of the phantom is shown.
Materials and Methods
28
Figure 2.9: Filament phantom consisting of 7 different
diameter filaments which were colored by an ink treatment
2.3.3
Experimental setup
The complete experimental setup was as follows. A filament phantom was
scanned along a line perpendicular to the orientation of the filaments. The
filament phantom was moved with respect to the stationary light delivery
system and the ultrasound transducer (both above the phantom). To control
the step-size of the mechanical movement, a computer-controlled motorized
linear slide (Zaber) with sub-micrometer precision was used. On every scan
position, several acoustic lines (A-lines) were recorded and stored for further
analysis and image reconstruction. The Nd:YAG/OPO-laser combination
was used for light delivery and was set to a wavelength of 680 nm. The
single element ultrasound transducers used in these experiments were a
semi-focused 40 MHz (2.5 French, Atlantis SR Plus, Boston Scientific, Inc)
and a flat, unfocussed 1 MHz transducer (Panametrics). The received signals
were amplified using a pulser-receiver (5073PR Panametrics) which was
set to a gain of 39 dB. The output of the photodiode was coupled to the
output of the pulser-receiver using a passive electronic splitter and fed into
a digitization card (Gage) which was operated at a sampling frequency of
200 MHz and a 12 bits voltage digitization. A schematic overview of the
experimental setup is given in figure 2.10:
29
Signal generation: changing laser pulse duration
Figure 2.10: Schematic overview of the experimental setup
2.4
2.4.1
Results
Model verification
To see whether the the theoretical model presented earlier agrees with the
experimental data, the following analysis was performed: a 360 µm nylon
filament was imaged with a 40 MHz and 1 MHz ultrasound transducer and
both signals were compared with the analytic solutions. The analytical
solutions were obtained using the equation (2.9) convoluted with a 7 ns
Gaussian laser pulse where after the derivative was taken. The transducer
response was simulated by a 4th order Butterworth band pass filter. The
cutoff frequencies were 30 / 50 MHz and 0.85 / 1.29 MHz respectively. These
values correspond to the frequency response specified by the manufacturer.
The experimental data was obtained with the experimental setup as
described in section 2.3. In both cases 8 averages were taken to improve
the SNR. The comparison is shown in figure 2.11.
2.4.2
Filament data comparison
This second result is a comparison between the model, described in the
‘Theory’ section and experimental data obtained by using two different laser
pulse durations and taking a cross section image of the filament phantom.
The settings were as follows: the transducer signal was 39 dB amplified
Results
30
Figure 2.11: Comparison between model and real data based on a
photoacoustic signal resulting from a 360 µm nylon filament. Top: 1 MHz
transducer. Bottom: 40 MHz transducer.
and digitized with a sampling frequency of 200 MHz . The total horizontal
scanning length was 18.2 mm, scanned with an increment of 25 µm. Eight
signals were averaged at every scanning position. The acquired signals were
filtered using a 10th order Butterworth band-pass filter (0.5 − 3 MHz) after
which, an absolute Hilbert transform was taken.
The total PA amplitude of each filament was calculated using a maximum
of 10 scan-positions of each filament. The maxima were summed and
divided by the number of scan-positions. This procedure results in a
weighted filament amplitude, Ã. This was done for both the short-pulse
and long-pulse images. Subsequently, every long-pulse weighted filament
amplitude was divided by the short-pulse weighted filament amplitude,
31
Signal generation: changing laser pulse duration
resulting in a filament efficiency ratio:
W (n)eff =
Ãlong (n)
Ãshort (n)
n = 1 · · · 7 (f ilamentnumber).
(2.18)
The filament efficiencies resulting from the 1 MHz transducer were calculated
and compared to the expected efficiencies calculated using the model
described in the ‘Theory’ section. The theoretical PA pulses where
calculated using equation 2.10 and 2.11, together with the laser pulses as
recorded in the experimental data. The resulting cylinder potential was
filtered with a Butterworth filter as described in the previous result. The
efficiencies where calculated using equation 2.18. For best comparison, all
filament efficiencies were normalized to the maximum filament efficiency.
Figure 2.12 shows the comparison between the experimental filament data
and the model.
Figure 2.12: Calculated weighted filament amplitude for each filament on the
basis of, blue: experimental data and red: the model by Hoelen et al.
Results
2.4.3
32
Image correction
This last result comprises the main objective of this chapter, which is
to determine whether the size of the PA source and its relationship to
photoacoustic amplitude relation can be elucidated when two different sets
of images are used. The two images of the filament phantom for this result
were made using the unfocussed 1 MHz transducer. The first image was
obtained using one 7 ns laser pulse. The second image was obtained using the
optical delay system resulting in three half-overlapping 7 ns laser pulses. The
laser-pulse correction image was obtained by dividing the second long-pulse
image by the first short-pulse image. The resulting images are shown in
figure 2.13. These images were formed using the same scanning procedure
as for the previous result. For display purposes, the images were convoluted
with a circular Gaussian kernel (low-pass filtering) where after, the square
root of the images was normalized to the maximum amplitude. The bottom
image results from the division of the middle image by the top image, before
normalization. Again, for display purposes, a hard thresholding criterion
was applied to remove the extreme values obtained after division.
33
Signal generation: changing laser pulse duration
Figure 2.13: Top: photoacoustic image of seven small filaments obtained with a
1 MHz transducer and 7 ns laser pulse. Middle: photoacoustic image of the seven
small filaments obtained with a 1 MHz transducer and 30 ns laser pulse. Bottom:
laser-pulse weighted correction image by dividing middle with top image. Field of
view is 18.2 by 7.3 mm.
Discussion
2.5
34
Discussion
The purpose of the experiments in this chapter was to see whether changing
laser pulse duration would give more information about the nature of the
photoacoustic source. Through the experimental data discussed in the
introduction, it became apparent that the maximum PA amplitude is not
only dependent on the optical absorption, but also on the size of the PA
source. When the size of the PA source cannot be deducted from the PA
signature due to band limitation of the transducer, an incorrect estimation of
the optical absorption is likely possible. In order to study this phenomenon,
a theoretical model was adopted and evaluated. The PA model presented
by Hoelen et al. was of particular interest for this chapter [56].
The theoretical model proved to be sufficient in dealing with the main
processes and source geometries present in photoacoustics. The model also
provided a relation between the laser pulse duration and the maximum PA
amplitude. This relation was compared to another model by Dingus et al.
(see figure 2.5). A discrepancy between the two models can be observed.
This might be related to the generalized assumptions, such as homogeneous
pressure distribution, that lay at the basis of these two models. However,
both models showed a clear decrease of PA amplitude by increasing laser
pulse duration; especially in the case of small source geometries.
In order to obtain a longer laser pulse duration, a new optical delay system
was developed. This system, which is based on overlapping laser pulses by
increasing the travel time of each pulse, proved to be very effective. Not
only did it allow for high laser energies, but due to its simplicity, it also
allowed for high compatibility with other lasers. A major drawback of the
system is the coupling efficiency of the light and the inability to choose a
desired laser pulse duration. A few of the combinations possible are shown
in the ‘Material and Method’ section, which show excellent agreement with
the theoretically expected pulses.
The phantom that was used for the experiments in this chapter consisted of
seven black nylon filaments with variable diameters. An important drawback
of this phantom is that the true optical absorption of these filaments is
unknown. Also, the order of filaments (ranging from large to small) was not
ideally chosen; since any trend during the acquisition process could easily
lead to misinterpretation of the results.
Nevertheless, the signals calculated with the model show good agreement
35
Signal generation: changing laser pulse duration
with the experimental data, as can be seen in figure 2.11. The two
signals that are obtained from the same filament also illustrate the influence
of the transducer characteristics in the determination of the PA source
geometry. The 4th order Butterworth band pass filter was sufficient to
mimic the transducer behavior for these experiments. However, in cases
where a qualitative comparison is needed, one should opt for a more realistic
transducer model. However, such an analysis goes beyond the scope of this
paper.
The photoacoustic theory also predicts that as the duration of laser pulse
increases, the signal generation efficiency decreases. This effect was observed
in the experiments that have been reported on here. Figure 2.12 shows that
the PA amplitude ratio, which is calculated by dividing the PA amplitude
obtained with a long laser pulse with the amplitude upon a short laser pulse,
decreases with the size of each filament. Both the theory and experimental
data follow this declining trend. However, an exact agreement between
the theory and experiments is not present. There are several factors that
may explain this discrepancy. First, the transducer model that is used is
very simplistic and probably not effective enough to mimic the true physical
behavior of the ultrasound transducer. Second, the optical properties of
the filaments are unknown, which means that the correct absorption model
cannot be applied. In addition, the laser pulses were monitored during
the experiment by a photodiode that was sampled at 200 MHz. Since the
laser pulse duration is ± 7 ns, the Nyquist criterion was not met, which
may have resulted in incorrect energy input estimation. Another issue is
that the geometry of the experimental setup does not completely match the
geometry assumed in the model. In the latter, a homogenous illumination is
assumed, whereas in the experiment the illumination comes only from one
direction. Finally, the optical delay system provided a pulse consisting of
three half-overlapping pulses. These are modeled as being one pulse, but
this might be not true to the real physical reality.
The results of the final set of experiments demonstrate that by combining
two images obtained with different laser pulse durations, a new image
containing information about the size-amplitude-dependency can be
constructed. The bottom image in figure 2.13 illustrates that the differences
in signal amplitude between the different filaments are related to the size of
the PA source rather than the optical absorption. Were this not the case,
an equivalent energy ratio for all of the filaments would have been observed.
From figure 2.13, it can also be inferred that the filaments were illuminated
from above. The difference in amplitude ratio is most noticeable in the
vertical strip across the filament. This strip coincides with the maximum
intensity position obtained right above each filament. It is also obvious that
Conclusion
36
the images show strong contrast, but lack any resolution. This is mainly due
to the use of the unfocussed, low frequency ultrasound transducer without
applying any delay and sum processing. Theoretically, the information
obtained by violation of the stress confinement condition could also be used
to improve upon the resolution set by the transducer characteristics.
2.6
Conclusion
In this chapter, it was shown that the size of the photoacoustic source
is an important parameter for the measured photoacoustic amplitude.
To estimate this influence, a theoretical model describing photoacoustic
generation was adopted. For experimental verification, a new optical
delay system was developed that uses multiple laser pulses to form one
longer laser pulse. With the use of this system and a low frequency
ultrasound transducer, a filament phantom consisting of seven thin filaments
was imaged. Utilizing the violation of the stress-confinement condition,
information about the size-amplitude-dependency of the photoacoustic
sources could be deducted. By combining two images obtained with two
different laser pulse durations, a new correction image was produced.
This image showed that the differences in photoacoustic amplitude were
mainly due to the size of the photoacoustic source and not related to the
optical absorption. These results indicate that the phenomenon of stress
confinement may be utilized to obtain additional information about the
photoacoustic source, even beyond the band limitation of the ultrasound
transducer.
3
Metallic contrast agents:
improving stability
3.1
Introduction
As said in the introduction of this thesis: photoacoustic imaging is of
great use to biomedical applications when a 3D optical absorption map of
a biological structure is required. However, the natural contrast present
in these biological structures might not always be sufficient to produce
significant PA signals. This shortage of initial contrast might be increased by
introducing artificial contrast agents. Once these agents are bounded to the
site of interest, the local contrast is increased and hence the PA amplitude
is increased simultaneously. Metal nanoparticles (MNP’s) are proven to be
good contrast agents in the case of PA imaging [62, 63, 64].
Metal nanoparticles, typically 30-100 nm in diameter, provide contrast based
on the elastic scattering of light through a surface plasmon resonance by
the metal particle. The order of resonance, hence the absorption strength,
absorption wavelength and the spectral bandwidth, depends on the size,
geometry, and composition of the particle as well as the local surrounding
environment. This particular set of conditions that defines the resonance
of these particles have been one of the main triggers for numerous research
into the usage of MNP’s.
The advantage of using MNP’s in a biomedical imaging setting is manifold.
First of all, it has been shown that the absorption efficiency from
spherical gold or silver particles is orders of magnitude higher than that
of organic dyes [65]. Despite their inorganic nature many MNP’s, especially
gold nanoparticles, are proven to be biocompatible and can be easily
bioconjugated [66, 67, 68]. This allows the particles to be labeled with
an antigen to bind with the location of interest. Another advantage of
39
Metallic contrast agents: improving stability
using metallic NP’s is that they can be used for photothermal therapy
[34, 69, 70]. Photothermal therapy relies on the conversion of the absorbed
electromagnetic energy by the nanoparticle to heat, in order to destroy
malignant tissue. Still the main advantage of MNP’s is their capability
to be tuned to any desirable wavelength. This is especially of interest to
the near infra-red (NIR) spectral range where tissue absorbs minimally, see
[71, 72] and figure 1.1. Gold nanorods are of particular interest in this range,
since their large aspect ratio allows for a high absorption cross section while
the resonance is perfectly tunable in the NIR. Also, these rods are easy to
synthesize and are small enough to be still biocompatible.
Due to the large absorption cross section, these gold nanorods also make
ideal candidates for PA imaging. However, since the generation of PA waves
is preceded with the absorption of a high-energy nanosecond laser pulse,
the nanorod needs to sustain this energy in order to maintain the same
optical properties. This desired stability upon energy absorptions appears
not always achievable. The heat that is generated in the particle can be
substantial and will often lead to morphological changes such as “spherical
regeneration”. Nanoparticle melting has been shown for nanospheres and
nanorods to occur at significantly lower temperatures than bulk melting of
the metal, in part because surface reorganization processes may dominate
[71, 73, 74, 75].
3.1.1
Problem statement and chapter overview
Thus, for the purpose of photoacoustic imaging, it is highly desirable to
have plasmonic nano-absorbers that can resist high laser energies, and at the
same time keep the desired properties such as the ability of bioconjugation
and specific optical absorption. Normal conjugated gold nanorods lack
this stability. A possible solution would be to coat the nanorods in order
to strengthen the initial geometry. Previous studies showed that when
nanorods are embedded in a solid environment, such as carbon or PMMA,
a significant increase of photothermal stability can be achieved [76, 77].
Silica coating of nanorods has also shown to be possible by a relatively easy
chemical synthesis [78, 79, 80, 81]. Another advantage is that silica can be
used for bioconjugation [82, 83]. In this chapter, we will therefore study the
feasibility of increasing thermal stability by coating the gold nanorods with
silica.
Materials and Methods
3.2
Materials and Methods
3.2.1
Silica coated gold nanorods
40
The silica-coated gold nanorods were produced from CTAB-stabilized gold
nanorods by exchanging CTAB with the biocompatible mPEG-thiol, where
the PEG polymer can be used as a silane coupling agent to coat the particle
with silica [83]. The coating procedure was done via a modified Ströber
method, which allowed the NP to grow a silica shell with reasonable control
of overall shell thickness [84, 85]. For more details on the synthesis of these
silica-coated gold nanorods, see [86]. The characterization of the produced
silica-coated gold nanorods was done with the use of ultraviolet to visible
(UV-Vis) extinction spectroscopy (BioTek Synergy HT) and transmission
electron microscopy (TEM). Below, two TEM images of two different sets
of silica-coated gold nanorods are shown. These images were obtained using
a Hitachi S-5500 FESEM TEM machine equipped with a field emission
electron source operating at 30kV.
Figure 3.1: TEM images from two different sets of silica-coated gold nanorods
adopted from [86].
3.2.2
Experimental setup
The experimental setup for photoacoustic data acquisition consisted of a
tunable laser, an ultrasound acquisition system, and a thin glass tube
which was subsequently filled with different types of MNP’s in solution.
The laser source used was a tunable optical parametric oscillator (OPO),
41
Metallic contrast agents: improving stability
operating at 800 nm, which was pumped by a Nd:YAG Q-switch laser
(Vibrant B, Opotek, Inc.). The laser beam was collimated for optimal and
equal illumination and consisted of a burst of multiple pulses each with
7 ns pulse duration and with a repetition rate of 10 Hz. The ultrasound
system consisted of a 7.5 MHz single element focused ultrasound transducer
(Panametrics) with a focal distance of 50.4 mm. The transducer was coupled
to a pulser-receiver (5073PR Panametrics) with a build-in amplifier which
was set to a gain of 39 dB. The signal was digitized using a computer-based
digitization card (Gage) which was set to a sampling frequency of 200 MHz
and a 12 bits voltage digitization. The nanorods solutions were introduced
with the use of a 1 mm in diameter glass tube which was fixed in a water
tank. Both sides of the tube were connected to an in- and outlet to change
nanorods solution samples during the experiment. The laser light was
introduced through an optically transparent window allowing a PA-signal
free uniform irradiation of the glass tube. Every measurement, a sample
portion of 50 µL of either, PEG-coated gold nanorods or 20 nm silica-coated
gold nanorod solution (with an optical density of 0.5) was injected into
the glass tube. The amplitude of the recorded photoacoustic signals were
compensated by the fluence fluctuation factor (calculated from recorded
power meter readings per pulse), and then normalized to the maximum
photoacoustic signal recorded. The schematic overview of this experimental
setup described is shown in figure 3.2. Furthermore, the UV-Vis extinction
graphs, acquired before and after laser light exposure, as shown in the
section 3.3, were obtained by illuminating a 96-well microliter plate with
the same laser and laser settings as used in the PA characterization.
Figure 3.2: (a) A block diagram of the ultrasound and photoacoustic imaging system
used to evaluate the thermal stability of silica-coated and PEG-coated nanorods. (b)
Close-up schematic illustration of the sample irradiated by a pulsed laser beam while
photoacoustic transients were measured using the 7.5 MHz ultrasound transducer (adopted
from [86]).
Results
3.3
3.3.1
42
Results
Fluence impact comparison
The first result is a laser-light-impact comparison verified by taking the
UV-Vis extinction spectrum of MNP’s solutions after exposure to different
laser fluences. In addition several TEM images were made to check whether
morphological changes were induced. The comparison was done between
CTAB-coated, PEG-coated, 6 nm silica-coated and 20 nm silica-coated gold
nanorods. The laser fluences used in the UV-Vis experiments were: 4, 8,
12, 16 and 20 mJ/cm2 . In the case of the TEM images a fluence of 20
mJ/cm2 was used. Each illumination burst consisted of 300 pulses with a
repetition rate of 10 Hz. Figure 3.3 shows the UV-Vis extinction graphs for
the different particles at different fluence intensities. Figure 3.4 shows the
TEM images of three different particles.
Figure 3.3: UV-Vis extinction spectra of gold nanorods coated with: (a) CTAB,
(b) PEG, (c) 6 nm Silica and (d) 20 nm Silica, irradiation with fluences ranging from
0 − 20 mJ/cm2 (Figure adopted from [86]).
43
Metallic contrast agents: improving stability
Figure 3.4: TEM images show the morphology evolutions of various gold nanorods
before and after 300 pulses of 20mJ/cm2 laser irradiation. (a,b) PEG coated gold
nanorods, (c,d) 6 nm silica-coated gold nanorods, (e,f) 20 nm silica-coated gold
nanorods (adopted from [86]).
3.3.2
Stability comparison
The second result is to check whether the stability of the induced PA signal is
increased by coating the nanorods with a silica shell. This result is obtained
with the PA acquisition setup as described in Section 3.2 and illustrated in
figure 3.2. Two types of particles, PEG-coated gold nanorods and 20 nm
silica-coated gold nanorod solution, were compared. The intensity of the PA
signal after every laser pulse was monitored and normalized to the maximum
amplitude received. The normalized amplitudes were plotted versus pulse
number ranging from 0 to 300. Two fluence intensities of 4 mJ/cm2 and
18 mJ/cm2 were applied. The two graphs are shown in figure 3.5. Both
experiments were repeated three times and the error bars shown in the
figure indicate the standard deviation with respect to the average (dot).
Discussion
44
Figure 3.5: Photoacoustic signal intensity of PEG coated (red scatters) and
silica-coated gold nanorods (blue scatters) versus number of pulses with fluence (a) 4
mJ/cm2 and (b) 18 mJ/cm2 (adopted from [86]).
3.4
Discussion
The first and general observation that can be made with respect to the
UV-Vis spectrum graphs shown in figure 3.3 is that all graphs show two
extinction bands at 530 nm and at 780 nm. These bands correspond to
the radial polarization (small blue peak) and the cylinder axial polarization
(higher red peak). The relative broadness of the absorption bands depend
very much on the distribution of the aspect ratios of the particles [71, 87].
Also by close examination there is a slight (± 20 nm) red-shift noticeable
from un-coated to coated nanorod, which agrees with the simulations done
by varying the index of refraction surrounding the particle. For more details
on these simulation see [86].
The longitudinal extinction peak is a good indication for possible changes of
the nanorods due to laser light exposure, because the peak position strongly
depends on the aspect ratio of the particle [72]. When the morphology of
the particle changes due to e.g. heating, the longitudinal band should also
change. This phenomenon is clearly visible in figure 3.3. Fluences below
4 mJ/cm2 did not show any mayor spectral changes. However above this
so called ‘threshold’ the longitudinal peak especially decreases in amplitude
and broadens in shape. Fluences above 8 mJ/cm2 show a 10% decrease in
amplitude for the case of CTAB-coated gold nanorods, while no changes
were observed for the other particles. Further increase of the laser fluence
led to significant decrease in amplitude and a blue-shift of the longitudinal
peak, and a strong increase of absorption in the 600-650 nm range. These
changes are consisted with the rounding or ‘spherical regeneration’ of the
45
Metallic contrast agents: improving stability
nanorods. Both trends are observed for the CTAB- and PEG-coated rods
while the silica-coated particles showed a more stable extinction profile with
respect the other particles. Despite the loss in amplitude of the 6 nm silica
shell coated gold nanorods, there was no observable ‘shoulder’ development
in the 600-650 nm range. This might be an indication of a different
regeneration process occurring with normal gold nanorods. Nevertheless
the 20 nm silica-coated nanorods show a distinctively increased robustness
with respect to the increase of laser fluence. There is only a small spectral
change observable above a fluence of 16 mJ/cm2 . This clearly indicates that
the silica coating protects the gold nanorod to change optical properties
upon high energetic pulsed laser light exposure.
To see the morphological impact, TEM images were made of PEG-, 6 nm
silica- and 20 nm silica-coated nanorods after receiving 300 pulses of
20 mJ/cm2 . These particles correspond to the black and orange curves
in figure 3.3 (a), (b) and (c) respectively. Quite obvious, the PEG-coated
nanorods show a great variety of shapes other than the initial rod-shape
geometry. Among them there are spherical, ellipsoidal and ellipsoidal with
an equatorial thickening, see figure 3.3 (b). The latter was also observed
by Chang et al. [71]. The 6 nm silica-coated particles show less of this
reshaping phenomenon. Instead the pulsed laser irradiation rather caused a
decrease in length thereby changing the overall aspect ratio from 3.9 ± 0.4
to 3.0 ± 0.3 (n=100). This might explain the earlier observed blue-shift of
the longitudinal peak in the spectrum shown in figure 3.3 (c). The thick
silica coating of around 20 nm seems to completely stabilize the initial
rod shape, for there was almost no deformation observed after laser light
irradiation. Another observation is the apparent aggregation of the 6 nm
silica coated particles. This was not found in the case of PEG-coated or
20 nm silica-coated nanorods. It is unclear why this happened but might be
a result of the centrifugation process.
The scope of this chapter is to see whether silica-coated gold nanorods
produce a more stable photoacoustic signal than normal gold nanorods. The
results discussed above already point to success, however the last result is the
most indicative that the silica-coated particle does provide a more stable PA
signal after laser light exposure. PEG-coated nanorods were compared with
20 nm silica-coated nanorods by exposing both solutions to 300 pulses at a
fluence of 4 and 18 mJ/cm2 . From figure 3.3 it is obvious to see why these
particular fluences are chosen. The two particle solutions showed similar
behavior for the case of 4 mJ/cm2 . However in the case of high fluence, the
PA amplitude resulting from the PEG-coated particles dropped by 40% in
the first 100 pulses, whereas the PA signal from the silica-coated nanorods
stayed relatively constant, for all 300 pulses. This strongly supports the
Conclusion
46
idea that silica-coated gold nanorods are promising candidates to be used
as contrast agents for photoacoustic imaging.
Furthermore, we also observed that the amplitude of the PA signal resulting
from the silica-coated particles was several factors higher than that of other
particles. This was not shown in the results presented here. This subject
goes beyond the scope of this chapter but will be dealt with in another paper
soon to be published (see Section Published Papers).
What causes the silica-coated gold nanorod to be more stable than the
other gold nanorods? The answer to that question is not an easy one. As
we’ve seen, the covalent binding of the mPEG-thiol group already leads
to an increased stability. This might indicate that it stabilizes the surface
gold atoms or changes the interfacial heat resistance. This influence of the
chemical nature of the environment close to the nanorod surface has been
treated earlier by Mohamed et al. [88]. The stability shown by PEG-coated
nanorods also agrees with the increased thermal stability of PVA stabilized
nanorods [74]. An aspect that might speak to the good performance of the
silica-coated particles is that thermal conductivity might be increased which
will result in an improved cooling of the particle. This process might also
advocate for the higher PA signal observed for the silica-coated rods. The
influence of heat transfer to the surrounding medium with respect to the
stability of nanorods have been studied and proven to be a serious factor
[74, 89]. Indeed silica has a significantly higher thermal diffusivity compared
to that of water.
3.5
Conclusion
In this chapter it was proven that gold nanorods could be coated with a
silica shell with controllable thickness. The silica coated nanorods were able
to sustain higher laser fluences and consequently kept their initial optical
properties much better than the other gold nanorods. This was proven
by UV-Vis spectroscopy as well as TEM images taken before and after
laser light exposure. Finally, the photoacoustic signal resulting from the
silica-coated particles showed to be stable in amplitude whereas the signal
induced by other gold nanorods decreased significantly after several pulses.
As a result silica-coated nanorods show to be a promising candidate for
molecular photoacoustic imaging.
4
Signal processing: denoising
scanlines
4.1
Introduction
A common problem for photoacoustic imaging, if not for all imaging
techniques, is the low SNR of a single measurement. In the case of
photoacoustic imaging, a single measurement consists of acoustic transients
that are recorded after the input of a short laser pulse. The underlying
phenomenon for this acoustic signal generation is a thermo-elastic expansion
following a fast (nanosecond) light energy deposition in an ‘optically active’
structure. Since these photoacoustic waves are localized in time and have
unique amplitudes, a 2 or 3D optical absorption map of the object of interest
can be reconstructed [14].
Despite the reasonable efficiency from optical absorption to thermo-elastic
expansion, there are other adverse processes that impede easy signal
generation and detection [40]. These processes include light attenuation,
spherical wave propagation, acoustic diffraction and transduction loss from
the ultrasonic wave to the measured signal [7]. In order to improve detection
efficiency, researchers are developing new signal processing tools.
The general objective of all signal processing techniques that deal with
photoacoustic data is to increase the SNR of the acoustic recordings and
subsequent produced images. Besides simple thresholding and frequency
filtering, research has been done to see how wavelet analysis can be applied
to the field of photoacoustics [90, 91, 92, 93]. So far, wavelet analysis has
been a partially successful endeavor. Negative effects include the robustness
or stability, change of signal amplitude and possible loss of resolution. In
terms of photoacoustic tomography, SNR and contrast have been increased
by various forms of filtered backprojection [94, 95], deconvolution [96],
49
Signal processing: denoising scanlines
statistical analysis, [97] and variants of the delay-and-sum method [36, 98].
Nonetheless, all of the techniques mentioned above still use the standard
averaging method to increase the SNR of the scanline before applying the
proposed algorithm.
4.1.1
Averaging
The basic idea behind averaging multiple measurements into a single
scanline is that noise will average-out to its zero-mean and signal, which
is measurement invariant, will remain as a non-zero signature. To formalize
this thought let us assume that we have a digital measurement yi that is
composed of a PA signal and measurement noise. So we have:
yi (k) = x(k) + vi (k)
k = 1, · · · , N.
(4.1)
Here yi (k) is k th sample of measurement i, where i = 1, · · · , M , x(k) denotes
the PA signal and vi (k) the noise. The latter is assumed to be zero-mean
i.i.d. (independent and identically distributed) with variance σv2 for all k =
1, · · · , N and all measurements i = 1, · · · , M .
Furthermore, PA acquisition often suffers from signal displacement or jitter
between multiple measurements. This can be due to acquisition related jitter
(e.g. unstable triggering) or movement of the PA source (e.g. due to local
heating). For simplicity reasons let us assume that these jitter processes
occur in the discrete time domain. The measurement yi then becomes
yi (k) = x(k − di ) + vi (k)
k = 1, · · · , N,
(4.2)
where di accounts for all possible jitter processes (in this case di is a positive
or negative integer) which can vary between measurements yi . In order
to increase the SNR we should separate signal x(k) from the noise vi (k).
This separation first involved estimating x(k) given the measurements yi (k),
i = 1, · · · , M , k = 1, · · · , N . In averaging, x(k) is estimated (e.g. using least
squares) from
x̂(k) =
M
1 X
x(k − di ) + vi (k)
M
k = 1, · · · , N.
(4.3)
i=1
Expression 4.3 can be appreciated as the ‘averaging method’. It simply
shows that the measured noise vi goes to its mean value (typically zero or
a dc offset) and the true signal(s) x(k) will remain in original amplitude
(especially when di is small). This method is called averaging and can
Introduction
50
be considered as the main and most used SNR-improving technique in
photoacoustic signal processing
4.1.2
Problem statement and chapter overview
Despite its relative popularity, there are some intrinsic disadvantages
associated with the process of averaging. The first and foremost shortcoming
is the fact that averaging is a stationary sample-to-sample comparison. For
illustration purposes let us consider two averaging cases. In the first case
there is noise present and in the second case there is jitter present.
Figure 4.1: Top left: PA signal with noise sample, middle left: PA signal with noise
sample, bottom left: average signal obtained by using the two left signals above. Top
right: PA signal with jitter of two sample points to the left, middle right: PA signal
with jitter of two sample points to the right, bottom right: average signal by using
the two right signals above.
From expression 4.3 and figure 4.1 it can be inferred that x̂(k) can be an
inaccurate estimate in the presence of dominant vi (k)0 s . Even though white
noise will have a random Gaussian distribution, local sample points will
not necessarily be immediately averaged to its zero-mean. Related to this
51
Signal processing: denoising scanlines
disadvantage is the difficulty of dealing with high electromagnetic (EM)
interference. EM interference signals can be considered as deterministic but
time-variant with respect to the stationary trigger signal. Therefore, these
interference signals will cause a drastic change to the local-zero-mean of
all the sample points associated with time duration of the EM interference
signal. In this case more averages are needed in order to reach the same
noise level as other unaffected time instances.
The second disadvantage associated with averaging is that acquisition jitter
and small signal displacement, caused by sample movement or local heating,
causes an immediate degradation of the estimated signal x̂(k) (see figure
4.1). Now y1 (k) is no longer summed with y2 (k) at the same k, but at
two different k 0 s. This discrepancy leads to signal degradation in terms of
amplitude and time spreading, causing a loss of resolution. Compensation
for signal displacement when using the averaging method is a non-trivial
procedure and is therefore, despite its consequences, often neglected.
This time element is also inherent to the process of averaging where multiple
measurements are needed to obtain one scanline. Time can become a very
dominant factor when photoacoustic imaging is used to image structures
where high laser fluencies are needed. These fluences are currently being
produced by Q-switch lasers. These lasers operate with pulse repetition
rates in the range of 5 to 50 Hz. Therefore, scanning a small part of a female
breast with 100 averages will take an average scan time of 30 minutes [28].
Most of the disadvantages mentioned above boil down to the fact that normal
averaging can be considered as a ‘measurement costly approach’. Whenever
the noise level is high, EM-interference is present or jitter occurs, and one
needs to collect a considerable amount of A-lines before a desirable and
trustworthy SNR can be obtained. The method presented in this chapter
only requires a few A-lines to reach the same SNR obtained using normal
averaging.
In the following section the proposed method will be explained and a
short example of pseudo-code will follow. To prove the superiority of this
method, the new method is compared with normal averaging in terms of
SNR improvement of both synthetic data and in-vivo data.
Method
4.2
52
Method
As discussed above, averaging is sample-to-sample summation without
making a distinction between ‘signal samples’ and ‘noise samples’, ‘moved
samples’, and ‘unmoved samples’. The technique presented here does make
that distinction by utilizing the phenomena that each photoacoustic response
1) consists of more than one sample point only, 2) is unique in its sample
order and 3) occurs at all A-lines around the same time. By observing all
A-lines locally, a sensible distinction between signal and noise can be made.
4.2.1
Window matrix
The technique has two distinct components, the first of which is a
sliding window matrix. This window-matrix Wk consists of M individual
windows. Every window contains a few adjacent samples k taken from
one measurement. In the window-matrix Wk , each row coincides with
one measurement window and every column denotes a sample position
k. Every matrix is evaluated on the basis of signal content from which a
signal-content-value is deducted belonging to that sample position k. From
here the window is moved one sample position k and a new window-matrix
is obtained, etc.
The window matrix Wk in terms of yi can be expressed as:


y1,1 y1,2 · · · y1,w
 y2,1 y2,2 · · · y2,w 


Wk =  .
..
.. 
..
 ..
.
.
. 
yM,1 yM,2 · · · yM,w
(4.4)
where yi,k is the k th sample point of the ith measurement, i = 1, · · · , M ,
k = 1, · · · , w, where w denotes the window size.
Now let us assume that on a certain time instance matrix Wk contains
a complete PA signal. Also, let us assume that every PA signal x(k) is
independent of measurement i. This assumption can be valid if the physical
situation is equal for each measurement (e.g. same laser energy). Let us also
assume that x is independent of i and that there is no jitter of any kind.
53
Signal processing: denoising scanlines
We then obtain:

x1 + v1,1
 x1 + v2,1

Wk = 
..

.
x2 + v1,2
x2 + v2,2
..
.
···
···
..
.

xw + v1,w
xw + v2,w 


..

.
(4.5)
x1 + vM,1 x2 + vM,2 · · · xw + vM,w
Unlike noise vi , which is assumed to be i.i.d., signal has a defined signature.
This means that x1 , which is the first sample of x, can only be followed
by x2 and x2 only by x3 and so forth. Together with the assumption that
every PA signal is equal for all measurements, we may deduct that x1 can
be followed by any x2 independent of measurement i.
4.2.2
Signal evaluation
The technique further demands that every window matrix Wk is evaluated
on the basis of signal content. In order to do so, a new periodic signal is
constructed that is based on the resampling of matrix Wk . Since the sample
order of x is defined by the PA source (and transducer impulse response)
and independent of i, we are free to randomly reshuffle the elements of Wk
along the dimension of i but not along the dimension of k. This random
‘picking of elements’ with replacing is quite similar to what is done in
‘Bootstrapping’ (for more information about bootstrapping see Efron and
Tibshirani [99] and Zoubir [100]). By repeating this procedure several times,
a new one-dimensional periodic signal is obtained that contains repeated
arbitrary copies of the PA signal or the impulse response of the transducer.
This periodic signal can be expressed as;
S = [yi,1 , yi,2 , · · · , yi,w , yi,1 , yi,2 , · · · ] ,
i = random
(4.6)
The length of the periodic signal, S, can be chosen arbitrarily or may be
evaluated on the basis of processing speed and overall SNR gain. When
stating S in terms of x and v we get;
S = [xi,1 + vi,1 , xi,2 + vi,2 , · · · , xi,w , xi,2 +, · · · ] i = random
(4.7)
After the construction of the periodic signal S, a fast Fourier transform is
performed on S to see if any periodicity is present. Since the window size
w is predefined and the sampling frequency is known, the expected periodic
frequency should be close to the sampling frequency divided by the window
size.
Method
54
The final step is to check the magnitude of the periodic frequency. This
amplitude is a direct measure of the signal present in the matrix Wk .
When there is no signal present in the matrix Wk there will be no defined
periodicity in S. When Wk contains a full signal, a periodic frequency will
be measured due to the signal values that are the same for all i0 s. The
magnitude of the periodic frequency is stored to an intermediate scanline.
Figure 4.2 provides a schematic representation of the averaging method and
the signal-content-windowing (SCW) method just explained.
Figure 4.2: Schematic overview of the averaging method (Top) and the SCW method
(Bottom)
55
4.2.3
Signal processing: denoising scanlines
The algorithm in pseudo-code
The algorithm explained above can be summarized as follows:
1. Define a window size w. Length w is ideally based on the
impulse response of the acoustic transducer. Begin-point =
1, end-point = window size w.
2. Construct a window-matrix Wk that contains all window
samples from k = begin-point to k = end-point. Every row
coincides with one measurement i, Wk = [M × w].
3. Construct a periodic window signal S by taking random
measurement sample values in sample-wise order, S =
[W(i,1) W(i,2) , · · · , W(i,w) , Wi,1 , · · · ], until the desired length
of S is reached. Where i is a random integer from 1 to M.
4. Apply
PN
FFT
transform
on
S,
FS (x)
=
− 2π
N xn ,
x
=
1,
·
·
·
,
N
(where
N
is
the
S(n)e
n=1
length of S and n is the sample position).
5. The magnitude of the periodic frequency, FS (periodic), is
assigned to the local SCW position; SCW (k + 12 w) =
|FS (periodic)|.
6. Update Begin-point and End-point by the addition of 1 (or
w/2) and repeat step 2 to 5.
4.2.4
Modifications
The code outlined above represents the SCW-algorithm in its most compact
form. Numerous modifications are possible to improve or to customize the
code for specific needs. Three possible modifications are discussed below.
Thresholding Since this algorithm includes a local signal evaluation one
may introduce a thresholding criterion to decide whether |FS (periodic)| is
significant or not. A hard thresholding mechanism would replace step 5
with;
5. If |FS (periodic)| is significant SCW (k +
|(FS (periodic)| otherwise SCW (k + 21 w) = 0.
1
2 w)
=
Method
56
Several options exist for choosing the right test of significance. An easy
implementation would be to see if |FS (periodic)| is several folds higher
than the mean value of |FS |. If the distribution of |FS | is known, a more
sophisticated method could be beneficial; e.g. perform a chi-square test to
determine the right thresholding parameters.
Masking Related to the thresholding option is the possible usage of the
SCW-signal. Since the SCW-signal contains only absolute values that are
related to the amount and strength of the signal measured, we can use the
SCW-signal directly for imaging purposes. The signal strongly resembles
the absolute of the Hilbert transform (often used in photoacoustics to
construct images). Nevertheless, when the signature of the underlying
signal is of importance (e.g. as in Doppler ultrasound), one could also use
the SCW-signal to mask the average signal. The best way would be to
use the above described thresholding and thereby replacing the significant
|FS (periodic)|0 s by 1.
Lag compensation Another advantageous feature of using a window
based algorithm is the ability to adjust every row entry by any possible lag
(in our example denoted by d). This lag can be very beneficial when there
is signal movement or acquisition jitter present. One implementation would
be to move every row entry back and forth until the optimal periodicity is
found. A faster procedure would be to perform cross correlation among the
separate windows and obtain the lag values to adjust the entries.
57
4.3
4.3.1
Signal processing: denoising scanlines
Results
Denoising of synthetic photoacoustic signals
To demonstrate the signal-revealing capability of the SCW-algorithm
compared to that of normal averaging, both methods were tested on an
artificial dataset. This dataset consisted of eight measurements all corrupted
with a Gaussian noise distribution with an SNR of -12 dB (measured
from the lowest amplitude signal). Each measurement contained five
photoacoustic responses, which are all of different length and different
amplitude. All signals have a center frequency around 20 MHz and were
sampled with a frequency of 200 MHz. The input parameters for the
SCW-algorithm consisted of a window size of ten and a periodic signal S
of 400 sample points long (40 copies of arbitrary windows). The FFT was
performed using 512 points and no thresholding was applied.
Figure 4.3: Averaging and SCW-method compared on the basis of reconstructed
scanline.
4.3.2
SNR reconstruction comparison
To measure how much the SNR improves by using the SCW method, a
SNR reconstruction comparison simulation was performed. The simulation
consisted of 12 A-lines that were corrupted with a certain noise level. At
every noise level these A-lines were used to construct a scanline based on
Results
58
the averaging method and the SCW method. The SNR of the scanlines was
estimated using the following two equations:
var(H(Ac))
var(H(Ac) − H(Av))
(4.8)
var(H(Ac))
,
var(H(Ac) − SCW ))
(4.9)
SNRAverage = 10 log10
SNRSCW = 10 log10
where H(Ac) denotes the absolute of the Hilbert transform of the noise-free
scanline, H(Av) denotes the absolute Hilbert transform of the average
scanline, and SCW is the reconstructed scanline using the SCW algorithm.
The parameters for the simulation were 12 A-lines where corrupted with a
noise level of -25 dB up to 0 dB with an increment of 0.5 dB. Each A-line
was 300 sample points long and contained one signal with a center frequency
of 20 MHz sampled with a frequency of 200 MHz. The signal itself was
30 sample points long and was truncated with a Hamming window. The
input parameters for the SCW algorithm were: a window size of 10, the
periodic signal S contained 30 windows. For the sake of fair comparison,
no thresholding was applied. Furthermore no pre or post filtering was
performed.
Figure 4.4: The SNR of the reconstructed scanline
compared to the SNR of the used A-lines.
59
4.3.3
Signal processing: denoising scanlines
Measurement comparison
For the following result the two methods were compared on the basis of
SNR in relation to the number of measurements taken. The simulation
parameters were almost identical to those used for the previous result.
However the number of measurements was varied. The noise level of each
A-line was set to -8 dB. The calculated SNR of the reconstructed scanline
was measured using equation (4.8) and (4.9). The horizontal line plotted in
the same figure is used to highlight the difference in measurements needed
to obtain the same comfortable SNR level.
Figure 4.5: Comparison of the two methods on the basis
of measurements needed to achieve a SNR level.
4.3.4
In-vivo mouse tumor
The SCW algorithm was also applied on a real photoacoustic in vivo
dataset. Figure 4.5 shows the top-middle a tumor of a mouse. The mouse
was injected via the tail vein with EGFR targeted gold nanoparticles.
Over time the particles accumulated at the tumor site. To monitor this
process photoacoustic images were acquired. For more information about
this subject and the imaging setup used, see Mallidi et al. [64]. The
two photoacoustic images seen at the bottom of figure 4.5 were obtained
using a tunable optical parametric oscillator (OPO) laser, operating at
Results
60
800 nm that was pumped by a Nd:YAG Q-switch laser (Vibrant B,
Opotek, Inc.) and interfaced with a focused (focal depth = 25.4 mm, f #4)
25 MHz single element ultrasound transducer (Panametrics) coupled to a
50 dB amplifier (Panametrics). Multiple planes of 129 scanlines each were
obtained. Furthermore, each scanline consisted of 21 averages from which
only four were used for this result. Each A-line was digitized by an eight bit
acquisition card (Gage-card) with a sampling frequency of 200 MHz. The
input parameters of the SCW-algorithm consisted of a window size of 12
sample points where 20 window combinations were used in the periodic
signal S, and a thresholding criterion for the magnitude of the periodic
frequency was set to be at least six times the average FFT magnitude. Also,
no other frequency filtering was performed and no lag was considered.
Figure 4.6: Both methods applied on the same photoacoustic dataset of a mouse
tumor. Top middle is a photograph of the tumor. Top right is an ultrasound image
of the tumor. Bottom left is photoacoustic image using averaging and the bottom
right is same data now using SCW method.
61
4.4
Signal processing: denoising scanlines
Discussion
The objective of this chapter is to present a new SNR-improving technique
that is superior to the commonly used averaging method. Figure 4.3 shows
that the SCW-algorithm is able to reveal signals underneath an arbitrary
noise level in the circumstances where normal averaging fails. Specifically, we
can see that the dominant noise peaks shown around t = 0.1 µs, t = 0.8 µs
and t = 3.8 µs are nicely suppressed and the third and fourth signals are
recovered. Attention should be paid to the recovered amplitude however.
These amplitudes do not completely match the initial signal amplitudes.
However, with decreased noise levels, the weighting of each window becomes
more accurate along with the amplitude recovery.
Another issue that shows in figure 4.3, is the apparent decrease of resolution.
This decreased resolution, however, is a direct result of decomposing signals
into windows of fixed sizes and evaluating them locally. The same problem
occurs in wavelet analysis. Therefore, one could opt for a method in which
the window size is varied after each scanline iteration (similar to wavelet
analysis), where after an optimum or average window-response might be
use to produce a better scanline. However, the real question is what is
the ultimate comparison? The averaging method in this respect might look
ideal, however one has to take into consideration that when jittering occurs,
average signals will also degrade and lose their initial amplitude. In this
respect, the SCW-algorithm again seems to be superior since it is able to
adjust for jitter.
Figure 4.4 shows a comparison between the two methods on the basis of SNR
of the reconstructed scanline. In this simulation one photoacoustic response
was buried underneath a noise level that was subsequently decreased.
Two interesting observations can be made regarding this figure. The
SCW-algorithm outperforms averaging by a 5 to 10 dB difference. This
by itself is an impressive improvement. However, more interesting is that
even at very high noise levels of 20 dB the SCW algorithm, unlike averaging,
is able to make a fair distinction between signal and noise. Lastly, the SNR
was estimated on the basis of resemblance with the Hilbert transform of the
clean signal. Therefore no thresholding was applied in the SCW routine.
Nevertheless one can positively argue that when the SNR was calculated on
the basis of a ‘signal window’ and a ‘noise window’, the SCW line plotted
in figure 4.4 would rise much faster than it did here.
Figure 4.5 was obtained by varying the number of measurements taken
Discussion
62
in the scanline reconstruction. A horizontal line was plotted highlighting
the observation of the difference in required measurements to obtain a
comfortable SNR level of 12 dB. When relating the number of measurements
to the scanning time the difference between both methods can be quite
drastic. It therefore suggests that real time photoacoustic imaging might
benefit from this algorithm. Note of course that the computation time
when using the SCW algorithm is longer than in the case of averaging.
Furthermore, the exact window size does not dominate the graphs produced
in figure 4.4 and 4.5 to a great extent. Although there is a clear optimum,
the method does not appear to be very sensitive to slight changes of the
window size w. This also accounts for the length of the periodic signal
S. Nevertheless, for extreme values the results will be unreliable and not
satisfactory.
The last result shown in the right bottom image of figure 4.6 is probably
the most valuable. It proves that the SCW method does not only work on
artificial data but also improves real in-vivo data. The image obtained from
a tumor of a mouse shows clear contrast improvement when comparing the
SCW image with the averaging image. The photoacoustic active site of the
tumor really pups up in the SCW image where in the average image one
needs a far closer look to perceive the contours of the tumor. In terms of
resolution these images equal each other quite well.
A difficulty that was noticed when producing these images was that the
SCW method benefits from a high resolution amplitude digitization. The
measurements used for this image were recorded and digitized using an
8 bit digitization card that was set to have no saturation. This digitization
entailed that the first ‘bang’ (transducer response to laser light) often
seen in photoacoustic data took up all the eight bits leaving only a few
bits to digitize the small photoacoustic signals. In order to construct a
periodic signal S that differs from a noisy signal, it was more difficult and
less discriminative when the difference of noise and signal amplitudes were
not translated into amplitude deviations. This point should receive extra
attention when applying the SCW-algorithm to real photoacoustic data.
To finalize this discussion, the SCW algorithm works well, is very stable
and easy to implement. There are areas of improvement and probably
automation, e.g. determination of the optimal window size or length of the
periodic signal. However, the idea to decompose multiple measurements into
windows that are evaluated on the basis signal content, is to our knowledge
new for the field of photoacoustics and probably also ultrasound signal
processing. We also think that the latter might also benefit from this new
63
Signal processing: denoising scanlines
presented method. Additionally, the evaluation of signal, now done by a
FFT, which appeared to be fast and effective might also be changed to
for example any cross-correlation, other convolution method or statistical
analysis.
4.5
Conclusion
In this chapter a new SNR-weighting-windowing technique, called signal
content windowing (SCW), was presented. This technique makes use of
the similarity between multiple A-lines by decomposing them in adjacent
window matrixes that are evaluated on the basis of signal content, hereby
improving the SNR of the reconstructed scanline. It was shown that the
SCW method is superior to conventional averaging and has an average SNR
gain of 5 - 10 dB. Furthermore it was proven that this technique needs far
less measurements in comparison with averaging to obtain a scanline with
the same SNR. As a consequence, images with better contrast could be
produced using the same number of measurements.
5
Discussion and conclusions
5.1
Introduction
The advancements in photoacoustic imaging (PAI) have drawn the attention
of many researchers in the medical optics and ultrasound fields. Logically
so, as PAI, utilizes the benefits of both techniques and combines them in a
new, standalone imaging modality. PAI utilizes the uniquely defined optical
properties of different tissue types in its signal generation. Additionally,
PAI exploits the transient ultrasonic behavior of the signal for its image
formation. The combination leads to an imaging technique that is able
to map the optical absorption of tissue constituents in a three-dimensional
space. However, the contrast given by the current usage of PAI is often
very low and the resulting images can be of poor quality. Consequently,
PAI may require substantial development before it is widely accepted as a
useful medical imaging technique. To address these issues, this thesis has
explored three different approaches for contrast enhancement in PAI: one, by
altering the duration of the laser pulse to overcome bandwidth limitations of
conventional ultrasound transducers, secondly by increasing the stability of
nano contrast agents by the addition of complementary layer, and thirdly,
by introducing a new signal processing technique to increase the SNR of
photoacoustic scanlines.
5.2
General discussion
In Chapter two, the issue of measuring PA signals resulting from small
PA sources was examined. The first observation was that the dimensions
of the PA active object influence the proper estimation of the optical
absorption when measuring the signal with a band-limited ultrasound
65
Discussion and conclusions
transducer. By altering the laser pulse duration, it was shown that more
information could be obtained about the constituents of the PA signal
amplitude. A longer pulse reduced the PA amplitude generated by small
sources relative to the PA amplitude produced by larger sources. This
effect was used to determine if the difference in PA amplitude resulting
from seven small wires was due to the difference in optical absorption or
due to the difference in their respective sizes. While the results indicated
that the objects could be distinguished by their optical absorption alone,
the question remains whether the implementation of the pulse delay system
or any pulse stretching would be beneficial for clinical PAI. However, this
study addressed an important problem for PAI: the use of a band limited
ultrasound transducer makes the true optical absorption of small sources
difficult to measure experimentally. One possible solution would be to
use ultrasound transducers with the broadest bandwidth possible. Using
a transducer of unlimited bandwidth may make it theoretically possible
to determine the size of the photoacoustic absorber and relate it to the
measured signal amplitude. Nevertheless, consideration must be given to
the loss of overall sensitivity that results from the bandwidth gain, which
may make detection of small sources problematic.
In Chapter three, the stabilization of metallic PAI contrast agents was
explored. It was shown that under high laser fluence, the morphology of
gold nanorods was preserved when they were coated with a silica shell.
Furthermore, a stable PA signal was observed after several laser pulses.
This study was performed using high concentrations of the contrast agent
in a tissue-like phantom, and must be compared with in vivo measurements
before its practical use can be considered. For example, it is unlikely that the
high laser fluences used in the phantom study would be applicable in clinical
PAI. Therefore, the next step in this study would be to monitor the PA
stability of the silica-coated particles administered in an animal model, such
as mouse, with the appropriate laser fluence. Finally, Chapter four focused
on enhancing contrast by de-noising individual PA scanlines. The superiority
of the presented method for enhancement of SNR was demonstrated with
respect to normal averaging. The method utilizes local similarity of multiple
measurements to discriminate between signal and noise. The resulting
algorithm is easy to implement and provides the ability to produce images
with higher contrast. The algorithm proved to work very well with synthetic
signals, but in the case of real data, the algorithm seems to be sensitive to
the vertical level (voltage) of digitization. Nevertheless, the advantage of
applying this de-noising method is evident. The work presented in Chapter
four also indicates that there is still room for improvement in terms of
increasing the SNR using different signal processing techniques. The idea of
looking at local similarity between A-lines exemplifies how the behavior of
Future prospects
66
signal and noise in subsequent measurements can be better utilized.
5.3
Future prospects
Intrinsic to the nature of research is that it is never ending, and the same
can be said for PA research. There will always be opportunities to refine
techniques or to make new discoveries. However, consideration must always
be given to the usefulness of the advance. In this light, Chapter two showed
that the laser pulse duration is an important parameter with regard to small
PA active sources. A useful implementation of this observation would be
to consider the laser pulse duration and the pulse rise-and fall-time when
choosing a laser for PAI. These parameters are often neglected once the laser
pulse duration falls within a certain range, typically 5 − 10 ns.
It is difficult to predict the future regarding photoacoustic contrast agents.
In recent years, the academic interest in MNP’s has increased considerably.
The main cause for this is the remarkable physical behavior of these
particles at the nanoscale and the promise of tunable contrast for therapeutic
applications. It remains uncertain whether MNP’s will be able to live up to
these high expectations, since in terms of PAI it seems like trading two selling
points. On the one hand, you provide tunable contrast at any desirable
place, on the other hand, you lose the noninvasive aspect of PAI which will
make clinical acceptance more difficult. Nevertheless, the advantages are
clear and the future will reveal whether the research endeavors into their
use has been a successful or not.
In the last chapter, the issue of denoising PA scanlines was treated, a topic
that deserves more attention in the PA research field. Noise of every kind,
is a very dominant factor for PAI, if not for all imaging techniques. The
quality of the images produced is directly related to the presence of noise.
The attempt of denoising individual scanlines presented in this chapter is
just an example of what can be done. I think that in this respect a lot can
be learned from other research, such as in optics or radar. This not only
accounts for signal processing but also for the acquisition hardware. More
than just the ordinary attempt is necessary, for it are the weak signals what
make an ordinary image to a beautiful image.
To make a generalized observation regarding the future of PAI, it is fair to
say that the success of the technique will require significant improvement
67
Discussion and conclusions
of acquired images. While the advantages of PAI are numerous and
theoretically valid, high quality images at deep tissue levels has yet to be
achieved. In this respect, research into the ultrasonic behavior and detection
of PA signals can be a fruitful undertaking. In terms of optics, it is unlikely
that significant improvements will be possible. The scattering and diffusion
of light in tissue is complex and difficult to control. Nevertheless, the recent
interest and progression in research concerning controlled light propagation
in scattering media, by alerting wave fronts, might at one point become
beneficial and worthwhile adapting (especially so, since in PAI there is an
intrinsic feedback mechanism from the focusing of light to the measured
acoustic amplitude). It is, however, uncertain if PAI will make it as a
standalone imaging technique within the clinic. There are still many hurdles
to overcome. However, the attempts mentioned in section 1.1 of this thesis
do indicate that such a development is possible. The coming years will be
critical to show whether PAI can make it to the clinic or not.
5.4
Conclusions
In this thesis, three separate approaches to enhance contrast in
photoacoustic imaging were presented. By changing the laser pulse duration,
it was shown that crucial information regarding the photoacoustic source
could be obtained. With use of this information, a more insightful image
could be produced. In the second approach, it was shown that the thermal
stability of gold nanorods could be enhanced by coating them with a
silica shell. These new contrast agents show a more stable photoacoustic
response in comparison with uncoated particles. In the last approach, a new
method was presented to improve the signal-to-noise ratio of photoacoustic
scanlines. When compared with conventional averaging, the new method
showed superiority with respect to decreasing the noise level.
Published papers
Peer-reviewed papers
• Enhanced thermal stability of silica-coated gold nanorods for
photoacoustic imaging and imageguided therapy, Yun-Sheng Chen,
Wolfgang Frey, Seungsoo Kim, Kimberly Homan, Pieter Kruizinga,
Konstantin Sokolov, and Stanislav Emelianov, Optics Express 2010.
• Silica-coated Gold Nanorods as Photoacoustic Signal Nano-amplifiers,
Yun-Sheng Chen, Wolfgang Frey, Seungsoo Kim, Pieter Kruizinga,
Kimberly Homan, Konstantin Sokolov, and Stanislav Emelianov, Nano
Letters 2010 (in review).
Conference proceedings
• Evaluation of Arsenazo III as a Contrast Agent for Photoacoustic
Detection of Micromolar Calcium Transients, Erika J. Cooley,
Pieter Kruizinga, Darren W. Branchc, and Stanislav Emelianov,
Proceedings of SPIE 2010
• On stability of molecular therapeutic agents for noninvasive
photoacoustic and ultrasound image-guided photothermal therapy,
Yun-Sheng Chen, Pieter Kruizinga, Pratixa Joshi, Seungsoo Kim,
Kimberly Homan, Konstantin Sokolov, Wolfgang Frey, and Stanislav
Emelianov, Proceedings of SPIE 2010
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