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A STUDY OF SPECTRAL DOMAIN OPTICAL COHERENCE
TOMOGRAPHY AND PHOTOACOUSTIC MICROSCOPY
FOR BIOMETRIC AND BIOMEDICAL APPLICATIONS
by
Mengyang Liu
A thesis submitted to the Faculty of the University of Delaware in partial
fulfillment of the requirements for the degree of Master of Science in Electrical and
Computer Engineering
Spring 2011
Copyright 2011 Mengyang Liu
All Rights Reserved
A STUDY OF SPECTRAL DOMAIN OPTICAL COHERENCE
TOMOGRAPHY AND PHOTOACOUSTIC MICROSCOPY
WITH BIOMETRIC AND BIOMEDICAL APPLICATIONS
by
Mengyang Liu
Approved:
__________________________________________________________
Takashi Buma, Ph.D.
Professor in charge of thesis on behalf of the Advisory Committee
Approved:
__________________________________________________________
Kenneth E. Barner, Ph.D.
Chair of the Department of Electrical and Computer Engineering
Approved:
__________________________________________________________
Michael J. Chajes, Ph.D.
Dean of the College of Engineering
Approved:
__________________________________________________________
Charles G. Riordan, Ph.D.
Vice Provost for Graduate and Professional Education
ACKNOWLEDGMENTS
Here I want to thank for Dr. Buma‟s three years‟ training for me. Without
his patient and detailed guidance and supervision I would not be able to do the work
shown in this thesis. His working attitudes set up a model for me to follow as an
engineer. His training makes me get closer, if any, to the requirements of being a
competent researcher.
Also I want to thank my parents, Liangchao‟s, Jian Gao‟s and Zhengyu
Cao‟s families for backing me up in my years as a graduate student here. It is their help
that makes my life easier and drives me back to the focus when my life is disturbed.
My colleagues Ya Shu and Xinqing Guo shared a lot of happy moments
with me in the small office. Foods in their places are always so good. I am so going to
miss them after I leave the U.S.
Special thanks are given to the President Yifang Wei and the two VicePresidents Ding Li and Xinquan Zhang of our co-ed fraternity along with my friends
Jian Bai, Jiayin Wang, Jamall Willams, Duo Shen, Dr. Amardeep Dhanju, Dr.
Xiaoxuan Meng and Erica Dang who are with me through all these years.
iii
TABLE OF CONTENTS
LIST OF TABLES ......................................................................................................... vi
LIST OF FIGURES ...................................................................................................... vii
ABSTRACT .................................................................................................................. xi
1. INTRODUCTION ...................................................................................................... 1
1.1
Theory of Optical Coherence Tomography ............................................... 2
1.1.1
1.1.2
1.1.3
1.2
Spectral Domain Optical Coherence Tomography ........................ 3
Detection and Reconstruction Derived from Michelson
Interferometer ................................................................................ 4
Resolution of SD-OCT System ..................................................... 6
Theory of Photoacoustic Microscopy ........................................................ 7
1.2.1
1.2.2
Optical Resolution Photoacoustic Microscopy ........................... 11
Spectroscopic Photoacoustic Microscopy ................................... 12
2. ECCRINE GLAND MAPPING USING SD-OCT ................................................... 13
2.1
OCT in Biometrics Using Human Fingertips .......................................... 13
2.1.1
2.1.2
2.2
Human Sample Selection and Experiments ................................ 14
Dummy Fingertip Preparation ..................................................... 14
SD-OCT System ...................................................................................... 16
2.2.1
2.2.2
2.2.3
2.2.4
Setup and Parameters .................................................................. 16
System Synchronization and Data Acquisition ........................... 17
Spectrometer Calibration ............................................................. 19
Post Processing ............................................................................ 23
2.2.4.1
2.2.4.2
B-Mode Image Reconstruction ..................................... 23
Fingertip Touchless Scan and Triple Layer
Information Retrieval ................................................... 25
iv
2.3
2.4
Experiment Results .................................................................................. 26
Conclusion and Future Work................................................................... 31
3. PHOTOACOUSTIC MICROSCOPY ...................................................................... 33
3.1
PAM and SD-OCT in Osteoarthritis Studies........................................... 33
3.1.1
3.1.2
3.1.3
3.1.4
3.1.5
3.2
Spectroscopic PAM Using a Supercontinuum Source ............................ 43
3.2.1
3.2.2
3.2.3
3.3
Optical Imaging for OA ............................................................... 35
India Ink Staining ........................................................................ 37
Collagenase Treatment for the Sample........................................ 38
Results and Discussion ................................................................ 38
Conclusions ................................................................................. 42
From OR-PAM to Spectroscopic PAM....................................... 43
Materials and Methods ................................................................ 44
Results and Discussion ................................................................ 47
Wavelength Agile Spectroscopic PAM ................................................... 49
3.3.1
3.3.2
3.3.3
3.3.4
Tunable Wavelength Filter .......................................................... 49
Data Acquisition and System Synchronization ........................... 50
Results and Discussion ................................................................ 51
Conclusions ................................................................................. 53
4. FUTURE WORK ..................................................................................................... 54
4.1
4.2
Spectrophotometry for Mixed Dye Solutions .......................................... 54
Stokes Source OR-PAM with Fast Raster Scanning ............................... 58
BIBLIOGRPAHY ......................................................................................................... 61
APPENDIX: HUMAN RESEARCH APPROVAL FORM AND HUMAN
SUBJECT CONSENT FORMS ....................................................................... 66
v
LIST OF TABLES
Table 2.1
Repeatability of Eccrine Glands in Different Subjects Tested in
Different Days ......................................................................................... 30
vi
LIST OF FIGURES
Figure 1.1
Typical configuration of an SD-OCT system ........................................... 3
Figure 1.2
Schematic of a Michelson Interferometer ................................................ 5
Figure 1.3
Schematic of a Confocal Dark Field PAM System .................................. 8
Figure 1.4
Schematic of an OR-PAM system. a, condencer lens; b, pinhole;
c, microscope objective; d, ultrasonic transducer; e, correcting
lens; f, isosceles prisms; g, acoustic lens; h, silicon oil [5] .................... 12
Figure 2.1
Photograph of a; live finger; b, plastic mold made from that live
finger; c, the transparent dummy finger; d, the scattering dummy
finger. ..................................................................................................... 15
Figure 2.2
Schematic of the SD-OCT System ......................................................... 16
Figure 2.3
The function generator is used for the Servo Driver and the CCD
camera. The triangular waveform is used to control the GM; the
Sync channel of the function generator is used as the camera‟s
frame trigger. .......................................................................................... 18
Figure 2.4
Schematic of the Spectrometer ............................................................... 19
Figure 2.5
Interference fringe recorded by the uncalibrated spectrometer .............. 22
Figure 2.6
The power spectra of the signals. a, before calibration; b, after
calibration ............................................................................................... 23
Figure 2.7
B-Mode image of a fingertip .................................................................. 24
Figure 2.8
B-Mode image showing the surface layer (surface fingerprint) and
papillary layer (internal fingerprint). The torturous ducts in
between these two layers are eccrine glands. Integration between
the surface and papillary layers gives the eccrine gland map,
which is the same as pore distribution – a Level 3 fingerprint
identification feature............................................................................... 26
vii
Figure 2.9
Upper left: B-Mode image (x-z plane) of a live fingertip showing
the stratum corneum, eccrine glands and the papillary layer; lower
left: B-Mode image of the artificial fingertip shows no internal
banding structure and no eccrine gland ducts. The two B-Mode
images are displayed over 0.6 × 3.9 mm field of view and 30 dB
dynamic range in gray scale. Upper right: 3D display of the
digitally straightened live finger; lower right: volumetric display
of the fake finger whose surface resembles with that of the real
fingertip. The two volumetric images are shown over a 4.6 × 4.0 ×
0.5 mm region......................................................................................... 27
Figure 2.10 (a)-(c) the top, eccrine gland and papillary layers of the thumb;
(d)-(f) the three layers extracted at the same positions or ranges as
those in (a)-(c) for the dummy of the thumb; (g)-(i) the top,
eccrine gland and papillary layers of the index finger; (j)-(l) the
three layers extracted at the same positions or ranges as those in
(g)-(i) for the dummy of the index finger; (m)-(o) the top, eccrine
gland and papillary layers of the middle finger; (p)-(r) the three
layers extracted at the same positions or ranges as those in (m)-(o)
for the dummy of the middle finger. ...................................................... 29
Figure 2.11 a, topography of the surface fingerprint; b, topography of the
internal fingerprint; c, triple-layer display of the volumetric
fingertip image; d, reconstructed 3D surface without digital
straightening shows the feature of touchless scan. ................................. 32
Figure 3.1
Schematic of the OR-PAM system for cartilage imaging ...................... 36
Figure 3.2
a, photo showing the treated cartilage submerged in PBS solution;
b, SD-OCT image of the untreated cartilage showing an intact
region; c, PAM image of the same region as in B; d, SD-OCT
image for the untreated cartilage showing a small cleft in the
surface; e, PAM image reveals the depth and shape of the fissure
in d; f, SD-OCT image showing the 13-hour treated CIA cartilage,
note that the smooth gradual change in depth as in b & c is now
replaced by a ~10 µm bright surface layer and dark region
beneath; g, the PAM image for the same region as shown in f. ............. 40
Figure 3.3
3D volumetric display using SD-OCT showing the isosurfaces
with 71, 77 and 83 dB intensity. Isocaps are added and isonormals
calculated. ............................................................................................... 41
viii
Figure 3.4
3D volumetric display using PAM showing the isosurfaces with 23, -20 and -17 dB intensity. Isocaps are added and isonormals
calculated. ............................................................................................... 42
Figure 3.5
a, Q-switched microchip Nd:YAG laser emits 1064 nm pulses
coupled into and progagate through 7 m PCF (λzdw = 1040 nm) to
generate a supercontinuum. The PCX collimates the microchip
laser output. An asperic lens (AL) focuses the 1064 nm pulses into
the PCF. A microscope objective (MO) collimates the
supercontinuum output; b, The spectrum of the supercontinuum
measured by an optical spectrum analyzer; c, A photo showing the
dispersed supercontinuum on a piece of white paper. ............................ 45
Figure 3.6
Prism-based monochromator, where a concave mirror collimates
the dispersed light. .................................................................................. 46
Figure 3.7
Schematic of the OR-PAM system featuring a 25 MHz transducer....... 47
Figure 3.8
Multi-wavelength images of black, blue, green and red ink spots
(left to right). All images displayed over a 1.8 × 5.4 mm range in
40 dB scale. The spectrally processed image clearly identifies the
four ink regions. ..................................................................................... 48
Figure 3.9
The automatic tunable wavelength filter. One end of the lever is
fastened on top of the wave driver. The other end of the lever is
connected to the mask, with a tapered slit going in the diagonal
direction. A supercontinuum spectrum is drawn over the mask to
demonstrate the mechanism of how bandwidth selection is
performed. .............................................................................................. 50
Figure 3.10 Ink spot images for four ink spots (red, green, blue black from left
to right). Sequential bandwidths see 20 nm increments in their
central wavelength. Images are displayed over 8.4 × 1 mm region
in 10 dB range gray scale. ...................................................................... 52
Figure 3.11 The fiber phantom taken at 570 nm and 690 nm with the
spectrally processed image in RGB display. .......................................... 53
Figure 4.1
The Spectrophotometry System with a Separate Display of the
Cuvette ................................................................................................... 56
ix
Figure 4.2
Extinction coefficients calculated from the spectrophotometry
data for mixtures made of red and blue food dyes with different
ratios. ...................................................................................................... 57
Figure 4.3
USAF resolution target imaged by Stokes source OR-PAM
system. All elements in Group 4, 5, 6 and 7 are in the scanning
range. From the vertical bars we can see that the interdigitating
error has not been fully removed. ........................................................... 59
x
ABSTRACT
Optical coherence tomography (OCT) has been widely used in biomedical
imaging ever since its introduction. However, the application of OCT for biometrics is
relatively new. This thesis demonstrates the feasibility of applying a spectral domain
optical coherence tomography (SD-OCT) system to fingertip biometric information
retrieval. The results show that SD-OCT is capable of retrieving Level 3 fingertip
information. Combined with traditional Level 1 and 2 fingerprint information, which
can also be easily acquired by SD-OCT, the proposed application shows potential for
ultra-secure scenarios.
Photoacoustic microscopy (PAM) is another imaging modality gaining
considerable interest in the past decade. Different than OCT, it employs ultrasound
detection for optically induced thermal expansion in tissue. Bovine articulate cartilage
samples are imaged with a single wavelength PAM system using India ink as a
contrast agent. The same samples were also imaged with SD-OCT. Image analysis
shows that PAM produces excellent contrast between intact and fibrillated regions in
cartilage while SD-OCT visualizes topological changes with fine spatial resolution. A
spectroscopic PAM system is also developed using a tunable source based on
supercontinuum generation in a photonic crystal fiber. Imaging experiments of tissue
phantoms demonstrate the ability to differentiate various chromophores with unique
absorption characteristics. Another spectroscopic PAM system is currently being
developed, where the tunable source is based on stimulated Raman scattering. This
xi
new system is being studied and some preliminary results are given to demonstrate its
advantages over the supercontinuum approach.
xii
Chapter 1
INTRODUCTION
Optical imaging, with its strengths in fast acquisition, nonionizing
radiation within human tissue and other functional properties, has been heavily studied
in the past few decades and continuously applied clinically. Among them, optical
coherence tomography (OCT) and photoacoustic microscopy (PAM) are two relatively
novel imaging modalities with more and more applications in both clinics and
research.
OCT is analogous to conventional B Mode ultrasound imaging in
principle and in the sense of its non-invasive, high resolution, cross-sectional imaging
features. The difference lies in the use of a broadband light source and the detection of
the backscattered light instead of ultrasound pulses which dramatically boosts the
spatial resolution of OCT to be in the range of 1 ~ 10 µm that is about 100 times better
than the conventional ultrasound imaging [1, 2]. The evolvement of this modality sees
a great leap from time domain optical coherence tomography (TD-OCT) to spectral
domain optical coherence (SD-OCT) in the sense that the time needed for imaging is
greatly reduced [3]. Other sub-modalities of OCT such as full field OCT (FF-OCT),
polarization sensitive OCT (PS-OCT), Doppler OCT, Spectroscopic OCT etc. are
developed henceforth for different applications and bearing with them unique
advantages.
PAM is based on the photoacoustic effect, namely: an object is irradiated
by a short-pulsed laser beam; the deposition of the laser pulse energy into the object is
1
partially converted into heat; the heat will cause thermoelastic expansion of the object;
this thermoelastic expansion will induce the propagation of a ultrasound wave, which
is referred to as the photoacoustic wave [1]. This photoacoustic wave can be easily
detected using commercially available ultrasound transducers. A raster scan of the
photoacoustic wave generated in the object will generate a 3D image with
unprecedented contrast and good spatial resolution for the microstructures, hence the
name photoacoustic microscopy. In order to improve the resolution of PAM beyond
the limitation set by the focal spot size of the transducer which is determined by the
frequency response of the transducer without having to trade it off with the increased
tissue absorption when higher ultrasound frequency is involved [4], optical resolution
PAM (OR-PAM) is proposed and developed, whose transverse spatial resolution is
determined by the focal spot size of the illuminating laser beam [5, 6]. Dual
wavelengths [7, 8] and multi-wavelengths [9] laser beam as the excitation sources for
PAM have demonstrated the feasibility of spectroscopic PAM.
This thesis deals with a SD-OCT system which is applied in both
biometric research and biomedical imaging along with several PAM systems used for
phantom and cartilage imaging.
1.1 Theory of Optical Coherence Tomography
The basics of OCT employ a Michelson Interferometer. A broadband light
source illuminate the interferometer, then the light is split in two: one serves as a
reference beam while the other one serves as the sample illumination beam. The
reflected beams from both arms are combined at the beam splitter and sent to a
photodiode for detection. The interference fringes can be observed when the optical
pathlengths in the sample arm and the reference arm match. In a TD-OCT system,
2
depth information is acquired by scanning the mirror in the reference arm and the axial
resolution determined by the coherence length of the light source being used. Due to
the limitation for mechanically scanning the mirror and the depth samples needed for a
typical A-line of OCT, TD-OCT is excessively slow in many practical applications
[10, 11]. Therefore, SD-OCT is developed with faster acquisition rate, which is
introduced below.
1.1.1
Figure 1.1
Spectral Domain Optical Coherence Tomography
Typical configuration of an SD-OCT system
The spectrometer in the sample arm detects the spectral fringes in the
interferometer output, which is inversely proportional to the sample depth. An A-line
in the sample can be simply reconstructed by taking a Fourier transform of a calibrated
and back ground subtracted spectrum fringe.
The acquisition speed, instead of being limited by the mechanical scanning
of the reference mirror in TD-OCT, is limited by the frame rate of the spectrometer in
3
SD-OCT. With current commercially available charge-coupled device (CCD) cameras,
the line scan rate can easily reach tens of kHz [11].
In Fig. 1.1, the broadband laser is used as the light source for the
Michelson interferometer. A 50:50 fiber coupler is generally used to split the light into
the reference arm and the sample arm. Before the static silver mirror in the reference
arm from which the light will be reflected, a neutral density (ND) filter may be used to
adjust the light intensity detected by the CCD camera in case it cannot fall into a
proper range without the ND filter. A galvanometer controlled scanning mirror is
applied in the sample arm for one dimensional scanning necessary to form a B-Scan
image. The spectrometer of the SD-OCT system is composed of a diffraction grating,
an achromatic doublet and a CCD camera. The CCD camera will capture the
interference fringes, converting them to digital form and sending them to a PC with a
data acquisition (DAQ) board for data acquisition. The spectrometer calibration from
linear wavelength space to linear k space and the image reconstruction are performed
by specific software programs after data acquisition.
1.1.2 Detection and Reconstruction Derived from Michelson
Interferometer
Denote
as the electric field at the laser output,
pathlength from the laser output to the beam splitter,
the reference arm mirror to the beam splitter,
sample arm mirror to the beam splitter and
as the optical
as the optical pathlength from
as the optical pathlength from the
as the optical pathlength from the beam
splitter to the detector, which is a photodiode detector as shown in Fig. 1.2. The
electric field at the detector can be expressed as:
4
1.1
Figure 1.2
Schematic of a Michelson Interferometer
The optical intensity incident upon the detector can be expressed by:
1.2
where c is the speed of light and n is the refractive index. Substitute Eq. 1.1 into Eq.
1.2 and simplify the expression we can get:
1.3
where
is the intensity of the optical source and the multiplier in the
right part of Eq. 1.3 is the desired interference term with ∆z being the pathlength
difference between the reference arm and the sample arm.
5
In an SD-OCT system where a broadband laser source is used, we can
express
as a function of k, so that:
1.4
Since in reality, we do not have a perfect 50:50 beam splitter, also there is no 100%
reflection, the actual optical intensity reaching the detector is:
1.5
where
and
are the reference arm and sample arm reflectivity, respectively,
assuming the two reflectivities do not depend on k and omitting the detector
responsivity. Typically we have
. Use this inequality and take the inverse
Fourier transform of Eq. 1.5 we can get:
1.6
Generally the camera will detect the fringe in λ space instead of in k space. A
conversion and calibration is introduced in detail in Chapter 2. The reconstruction of
an A-line is performed using Eq. 1.6 after background subtraction. Note that the last
two terms in Eq. 1.6 indicating one real image and a mirror image. Special care is
taken when the actual A-line is displayed. Compiling consecutive A-lines in the same
cross section comprises a B-Scan image [12].
1.1.3
Resolution of SD-OCT System
Assuming the light source as a Gaussian beam with
as the full width
half maximum (FWHM) of the Gaussian spectrum, the depth information
6
of the
sample, which is the inverse Fourier transform of the backscattered interference signal
in k space, has the following relation:
1.7
The FWHM of
can be easily calculated, which is:
1.8
Taking into consideration that
and the backscattered light travels back
and forth in the sample, we have the expression for the axial resolution as the
following:
1.9
Instead of depending on the coherence length of the light source which is
the case for the axial resolution, the transverse resolution of an SD-OCT system, using
beam focusing theory, is a function of the focused probe beam waist
beam waist radius of the probe beam before focusing
, the minimum
, the focal length of the
sample arm lens F and the distance between the beam waist of the unfocused probe
beam and the focal plane of the sample plane z, which is generalized in the following
expression:
1.10
1.2 Theory of Photoacoustic Microscopy
PAM, being a hybrid biomedical imaging modality based on the
photoacoustic effect, combines the advantages of optical absorption contrast with
7
ultrasonic spatial resolution for deep imaging in optical diffusive or quasi-diffusive
regime. It transcends the current depth limit of high resolution optical imaging
modalities while maintaining the depth-to-resolution ratio to be greater than 100 [6].
At the same time, PAM is safe for human subjects and hence suitable for both clinical
and basic research purposes.
A reflection-mode confocal PAM system such as the one given in Fig. 1.3
consists of dark-field pulsed-laser illumination and high numerical aperture (NA)
ultrasound detection [1].
Figure 1.3
Schematic of a Confocal Dark Field PAM System
The interference caused by strong photoacoustic signals from superficial
structures is minimized by dark-field illumination and a high lateral resolution is
8
provided by using a high NA acoustic lens. High axial resolution of about 15 µm can
be achieved using a broadband ultrasonic detection system [13].
After the laser beam is collimated, it is incident upon the axicon lens. The
donut shaped laser beam is formed after the axicon lens before it is focused by the
focusing lens which is coaxially aligned with the ultrasound transducer. The focused
beam hit the sample and following the laser pulse energy deposition, heat conversion,
thermoelastic expansion and ultrasonic pulse generation, the ultrasound pulse is
detected by the spherical ultrasound transducer. The signal is transmitted to an
amplifier, a filter and recorded by DAQ board connected to a computer. The signal
recorded as a function of time can be easily converted into an A-line based on the
sound velocity in soft tissue which is 1.54 mm/µs. 3D images can be reconstructed by
raster scanning of the probe in the horizontal plane of the system [1].
In order to interpret the mechanism of the photoacoustic effect
quantitatively, the concept of the thermal relaxation time and the stress relaxation time
are given below respectively:
1.11
1.12
where
is the thermal diffusivity (
heated region and
);
is the characteristic dimension of the
is the speed of sound. Suppose the laser pulse width is much
shorter than both the thermal relaxation time and the stress relaxation time, the
excitation is said to be in both thermal confinement and stress confinement. In this
case, the local pressure rise immediately after the laser pulse can be expressed as:
9
1.13
where κ is the isothermal compressibility (~5×10-10 Pa-1 for water or soft tissue); β is
the thermal coefficient of volume expansion (K-1); T is the temperature change (K); Cv
is the heat capacity at constant volume (J/(kg K)); ρ is the mass density (kg/m3); Ae is
the specific optical absorption (J/m3) and ηth is the heat conversion percentage. If we
define the dimensionless Grueneisen parameter as:
1.14
and use the empirical formula of Γ in water or diluted aqueous solutions:
1.15
where T0 is in the unit of Celsius degree, Eq. 1.13 can be rewritten as:
1.16
where µa is the optical absorption coefficient and F is the optical fluence (J/cm2). By
solving the general photoacoustic equation:
1.17
with r and t denoting the position and time, respectively, we can get to the expression
of the response of a small sphere to a finite-duration excitation pulse as:
1.18
where S depicts the excitation pulse. From Eq. 1.18 we can see that the photoacoustic
pressure is proportional to the time derivative of the excitation pulse [1].
10
1.2.1
Optical Resolution Photoacoustic Microscopy
Traditional PAM is good enough for many applications with a lateral
resolution about several 10s of micrometer, but in the situation when finer resolution is
required, such as imaging of capillaries whose diameter is 4~9 µm [14], better
resolution is needed. An intuitive choice is to use ultrasound transducers with very
high response frequencies, corresponding to smaller focal spot size and better later
resolution. But the absorption coefficient α of a material is generally dependent on
frequency f with a good model for this dependency given below:
1.19
where a and b are two constants with b slightly greater than 1 in biological tissues
[15]. If the lateral resolution is dependent on frequency, we can tell from Eq. 1.19 that
the absorption will render the imaging depth of high resolution PAM very shallow.
Simple calculation reveals that for a 5 µm lateral resolution, an ultrasound transducer
with higher than 300 MHz central frequency is needed, and this high frequency makes
the penetration depth only about 100 µm [5].
OR-PAM is developed to overcome the ultrasonically provided lateral
resolution issue. In an OR-PAM system given in Fig. 1.4 [5], optical focusing can
achieve lateral resolutions of several micrometers. Ultrasonic focusing is achieved by
an acoustic lens. Isosceles prisms are used to align the laser beam illumination and
ultrasonic detection orthogonally since in this schematic the photoacoustic wave is
reflected 90°before it is recorded by the ultrasound transducer.
11
Figure 1.4
Schematic of an OR-PAM system. a, condencer lens; b, pinhole; c,
microscope objective; d, ultrasonic transducer; e, correcting lens; f,
isosceles prisms; g, acoustic lens; h, silicon oil [5]
Raster scanning over the sample and proper data processing can give 3D
volumetric images or maximum amplitude projection (MAP) images [5].
1.2.2
Spectroscopic Photoacoustic Microscopy
With the contrast of PAM being produced by the endogenous absorption
of absorbers within the tissue, different kinds of absorbers or the same kind of
absorbers in different physiological statuses tend to give different photoacoustic
responses for a certain frequency of light illumination. Thus in Eq. 1.16 the optical
absorption coefficient
is a function of wavelength. Therefore, when dual
wavelengths or multi-wavelengths are applied for the same sample during a PAM
experiment, we can reconstruct from the localized photoacoustic (PA) signal the
different components or the ratio of them within the sample [8, 9, 16].
12
Chapter 2
ECCRINE GLAND MAPPING USING SD-OCT
OCT has been widely applied in biomedical studies ever since its
introduction in the early 1990s. Its application in biometrics study is relatively new.
This chapter mainly demonstrates the possibility of applying SD-OCT in the
biometrics along with detailed description of the system and the related software
programs.
2.1 OCT in Biometrics Using Human Fingertips
Fingerprint identification is the most widely used biometric identification
method [17, 18]. But this widely applied method showed its unreliability to
counterfeited fingers using materials available in grocery and craft stores [19]. The
reason for the unreliability is mainly due to the 2D mechanism of currently
commercially available automatic fingerprint identification systems (AFIS) [20].
Considering the fine structure of the integumentary system of finger pads and the
mechanism of OCT, it can be applied for authenticating purposes using 3D
information of the fingertip. In recent years, efforts have been made using TD-OCT to
discriminate artificial fingerprint from genuine fingerprint in the 1310 nm imaging
window [18, 21, 22]. The use of the dermoepidermal junction undulation as the new
fingerprint pattern retrievable by OCT is proven to provide securer approach to
biometric identification [17]. But a potential problem of solely relying on this papillary
13
layer is that as one ages, the dermal papillae tend to flatten and the papillary layer
undulation may appear less pronounced [17, 23].
All the aforementioned efforts are not using the Level 3 fingerprint
information, which is also easily retrievable by using OCT. Thus, in this thesis an SDOCT system is used to accurately map the distribution of eccrine glands within the
fingertip [24]. Combining this Level 3 eccrine gland duct distribution information with
the surface and inner fingerprint, a triple-layer biometric identification method is
realized.
2.1.1
Human Sample Selection and Experiments
I myself and two other fellow research assistants are enrolled in this study.
Every enrollee signed the consent form for this project and an IRB review approved
this study under expedited review category. Experiment data are safely saved and
enrollees are referred to as Subject # 1, 2 and 3 to protect privacy. To achieve
statistically convincing results and to show repeatability, experiments were performed
in different days for the same finger of the same subject. Also multiple fingers of the
same subject were compared to test the applicability of this method on different
fingers of the same subject.
2.1.2
Dummy Fingertip Preparation
Spoofing proof of this proposed method can only be tested when fake
fingers are used. Plastic (Friendly Plastic, AMACO/Brent Ceramic & Craft Materials)
is used for making the mold of dummy fingertips. When making the mold, the plastic
is firstly heated till it is transparent and soft. The live finger is pushed firmly into the
14
plastic for several minutes when the temperature of the plastic dropped to about 60°C.
The live finger is then carefully dispatched from the plastic to form the mold.
Polydimethylsiloxane (PDMS) (Sylgard 184, Dow Corning corp.) is used
for making the transparent fake finger while a mixture of PDMS with titanium oxide
(Sigma Aldrich) with a 100:1 mass ratio is used for making scattering fake fingers.
When the mold is cooled down to room temperature, PDMS or PDMS/ TiO2 mixture
is poured into the mold. Dummy fingers are peeled off carefully from the mold about
one day later after they are solidified. Fig. 2.1 shows the photographs of a live finger,
the plastic mold, the transparent dummy and the scattering dummy, respectively.
Figure 2.1
Photograph of a; live finger; b, plastic mold made from that live
finger; c, the transparent dummy finger; d, the scattering dummy
finger.
The live fingers and their corresponding dummy fingers are imaged for the
same registered region to compare the difference. For more accurate results, only the
scattering dummy fingers are used in the actual comparison.
15
2.2 SD-OCT System
2.2.1
Setup and Parameters
The home-built fiber-optic SD-OCT system is sketched in Fig. 2.2. The
Ti:Sapphire oscillator (KM Labs) emits 60 nm bandwidth pulses with center
wavelength at 830 nm. Measured at the sample arm cube entrance, the average power
is 14 mW. A 12 bit 2048 pixel line scan camera (CL2014-SM2, Atmel) is used for the
home-made spectrometer with 29 kHz line scan rate and 30 µs integration time.
Figure 2.2
Schematic of the SD-OCT System
The x-axis scanning range determined by the galvanometer scanning
mirror (GM) is 6.6 mm in this experiment and the depth range was calculated to be 3.4
mm although the effective depth range is within 2 mm. Each B-Mode (x-z plane)
frame consists of 2048 A-lines. The y-axis scanning is achieved by using a stepper
16
motor (T-LA13A, Zaber Technologies). This stepper motor translates the sample arm
cube in 20 µm increments over a 4.04 mm range, corresponding to 202 frames for each
individual data set. The axial resolution of the system is experimentally tested by the 6 dB points to be 7.0 µm and the lateral resolution about 10 µm.
2.2.2
System Synchronization and Data Acquisition
The acquisition is through National Instrument Image Acquisition (NIIMAQ) Board (PCI-1426), which serves as the interface between the PC and the
camera. The camera is connected with PCI-1426 by the Camera Link cable, which has
two MDR 26-pin connectors that can provide reliable high-frequency transfer rates.
The CCD camera is set in free run mode in each cross sectional frame acquisition. The
GM (6210H, Cambridge Technology) is controlled by a Servo Driver. The Servo
Driver is connected to a function generator (33220A, Agilent) to provide the GM with
a certain frequency and amplitude (in degrees). The angle of the GM is directly
proportional to the voltage applied on the Servo Driver. The sign of the angle, with
respect to the 0 volt position, agrees with the sign of the voltage applied on the Servo
Driver.
The successfulness of the acquisition depends on the proper
synchronization of the components of the SD-OCT system. Namely between the CCD
camera frame trigger, the y-axis stepper motor and the GM Servo Driver. The
following sequence is used for this system:
1. Initialization of the NI-IMAQ board and the stepper motor;
2. Pose the GM to one end of the range;
3. Same trigger sent for GM Servo Driver and the CCD camera frame
grabber;
17
4. One B-Mode image scanned and data stored in PC memory;
5. GM retreats to the starting position; stepper motor moves one
increment; data streamed to PC hard drive;
6. Go to step 3 or stop acquisition.
Since the angle of the GM is proportional to the voltage applied on the
Servo Driver, the function generator sending a triangular waveform is set to work in
burst mode with 5 Hz frequency, 7 volts peak-to-peak amplitude and -90°starting
phase, which is sketched in Fig. 2.3:
Figure 2.3
The function generator is used for the Servo Driver and the CCD
camera. The triangular waveform is used to control the GM; the
Sync channel of the function generator is used as the camera’s
frame trigger.
A LabVIEW project is made for this SD-OCT system. One of the VIs in
this project performs the function of acquisition and synchronization. This VI enables
the system to work in three modes:
1. Don‟t Save Data: used to test whether every component of this
system is working properly. No data is saved.
2. Save Data without Background Subtraction: the program will save
data to the specific file name defined by the user. No
18
background subtraction is performed. Data is streamed
continuously into one binary file using data streaming
technique.
3. Save Data with Background Subtraction: user need to block the
sample arm first for this VI to store a background frame. After
the background frame is captured, the actual data streamed into
the hard drive will have undergone background subtraction
already.
All synchronization issues are taken care by this VI. Users only need to adjust the
control values and select a proper mode for the experiments.
2.2.3
Spectrometer Calibration
The schematic of the home-made spectrometer is given in Fig. 2.4:
Figure 2.4
Schematic of the Spectrometer
19
For the SD-OCT system, back-reflected light from both the sample arm
and the reference arm will be incident upon a spectrometer after they interfere in the
optical fiber Michelson interferometer. Based on the mechanism of the spectrometer,
we know that the output beam from the interferometer is separated by wavelength
. So the distribution of λ on the CCD is
according to the grating equation
proportional to sinθ instead of a linear distribution. From the theory of SD-OCT we
know that for a certain depth in the sample, the signal to be detected is proportional to
, which means that we are expecting a signal linearly scaled in the k space. In
order to properly process the data and perform the inverse Fourier transform for the
acquired signal, it is necessary for us to use an algorithm to convert the signal from
wavelength scale to linear k space. A wavelength independent method is used for this
conversion [25].
A gold mirror is placed in the sample arm at the focal plane of the
objective focusing lens. The reference arm beam pathlength is adjusted so that the
zero-pathlength-difference plane coincides with the objective focusing lens's focal
plane. Then shorten (lengthen) the reference arm beam pathlength to manually set the
pathlength difference, hence the interferometry pattern on the CCD camera. The GM is
temporarily disabled to make sure that the laser beam is exactly incident upon the
same point on the gold mirror. The data that we recorded in fact has an ideal sinusoidal
pattern taken the frequency as the horizontal axis, but because this signal is recorded in
the λ space, the data will look like a nonlinear quasi-sinusoidal pattern.
In order to convert the signal, we can first detect the phase of the signal.
To do the phase detection, we first perform fast Fourier transform (FFT) to the original
data. In order to get the complex form of the data, we only keep the positive frequency
20
part. But by doing this we will lose half of the samples when the spectrum is
transformed back by inverse Fourier transform, thus zero-padding is used in frequency
domain to compensate for the loss of samples. When the frequency signal is
transformed back, we get the complex form of the original signal. The phase
information is included in the angle of the complex signal. Now, we use the array of
phase values as the x-axis; use the index of the pixel as the y-axis; we can perform a
polynomial curve fitting and get the equation of n(Φ), where n is the pixel index
number and Φ is phase. Divide Φ into equal distances according to the total index
number, then we can find the indices (now expressed as decimals) that correspond to
equal phase increment. That is to say: using the angle information, firstly we get
, where i is the index number and
get the inverse function of
(
. Then via polynomial curve fitting we
, which is
. Let
have a unit increment
), we will be able to get a new set of ''index'' (we can call it
to distinguish it from the integer index
in order
), which is actually decimals and correspond
to equal phase increment.
Save the new index set
get directly is
. In the future, after the data acquisition, what we
. Via interpolation, we can easily get
. Save the array which
is in effect a signal expressed in linear k space.
This method is free of spectrometer wavelength calibration. It can be
applied to whatever wavelength bandwidth being used. The manually set pathlength
difference of the Michelson interferometer will not affect the final result of this
method, since it only contributes to the first order term of
.
Both LabVIEW and MATLAB programs were made to perform this
spectrometer calibration. Following the calibration results are given by LabVIEW
21
processing and the raw data is used when the pathlength difference is set to be 400 μm.
CCD array size is 2048.
Fig. 2.5 shows the raw data after averaging:
Figure 2.5
Interference fringe recorded by the uncalibrated spectrometer
Fig. 2.6 compares the power spectra of the raw data before and after
spectrometer calibration:
22
Figure 2.6
The power spectra of the signals. a, before calibration; b, after
calibration
From Fig. 2.6 it is clearly shown that after this wavelength independent
spectrometer calibration the spectrum is much more accurate.
2.2.4
Post Processing
2.2.4.1
B-Mode Image Reconstruction
Both LabVIEW VIs and MATLAB programs are prepared for the post
processing of the SD-OCT data to reconstruct B-Mode images. The program generally
deals with background subtraction first, then the predetermined “new” index value for
the spectrometer calibration is applied to the data through interpolation to get the λ- to
k-space conversion done. FFT is used to generate the B-Mode image and the image
may undergo 2D interpolation, A-line compression to enhance image quality. A
Ronchi Pattern (Edmund Optics) is imaged in order to experimentally determine the
lateral scanning range. The axial range and axial increment between consecutive
indices in the reconstructed B-Mode images are acquired by placing a mirror in the
23
sample arm and manually adjust the height of this mirror to a set of predetermined
values. The reconstruction programs compensate for the increments in both
dimensions in the B-Mode image automatically when interpolations with different
orders are used. Fig. 2.7 shows a test image using my left hand index fingertip.
Contrast improvement is accomplished both by properly selecting the dynamic range
of the gray scale display and by A-line compression – in this figure it is by grouping
every four neighboring A-lines and averaging them, which also suppresses the random
noise in the reconstructed image.
Figure 2.7
B-Mode image of a fingertip
24
2.2.4.2
Fingertip Touchless Scan and Triple Layer Information
Retrieval
A special holder is designed to secure the subjects‟ fingers during
scanning. The movement of the finger scanned in free space is reduced by using this
holder. The frame rate is set to 1 Hz which is experimentally determined to be a
suitable value considering the line scan rate of the CCD camera and the subject
movement during scan. As is shown in Fig. 2.7, the fingertip is scanned in air;
therefore the natural curvature is preserved. Though this touchless scan has potential
for even finer details which can be used for biometric purposes, in order to effectively
retrieve the surface fingerprint, eccrine gland duct map and the internal fingerprint,
every B-Mode image is digitally straightened to make the ridge-ravine undulating on a
horizontal line. This digital straightening is performed in the following manner:
Find and save the vertical indices of the maximum values in the BMode images. This corresponds to the stratum corneum position
of the epidermis of the fingertip.
Perform 3rd order polynomial fitting for the indices, basically serving
as a low pass filter. Save the vertical indices of the fitted curve.
Using the indices acquired in step 2 in the original B-Mode image.
Select a certain depth range above the “new” indices and
another certain depth range below the “new” indices. The
selected region, after realignment, is a digitally straightened BMode image with fingerprint fringe undulation in a fixed
horizontal value.
After the overall curvature of the stratum corneum is shifted in every BMode image, these shifted cross sectional images are combined into one 3D matrix,
containing the volumetric information of the fingertip. Integration over the top 32.5
µm of the volumetric matrix gives the surface fingerprint. Integration over the 166.7
µm range from 100 µm under the stratum corneum surface gives the eccrine gland
25
layer. Finally, integration over the 149.2 µm range from 290 µm under the stratum
corneum surface gives the internal fingerprint pattern. Because the laser pulse intensity
incident on the sample in the same cross section may not be the same due to the
focusing lens used in the sample arm cube, a background x-y plane is generated to
normalize the eccrine gland layer display.
2.3 Experiment Results
Fig. 2.8 gives a B-Mode fingertip cross section image showing the
tortuous eccrine gland ducts distributed in the epidermis. The bright surface layer is
the stratum corneum and the internal ridge-furrow undulation is the dermoepidermal
junction, which is taken as the internal fingerprint pattern.
Figure 2.8
B-Mode image showing the surface layer (surface fingerprint) and
papillary layer (internal fingerprint). The torturous ducts in
between these two layers are eccrine glands. Integration between the
surface and papillary layers gives the eccrine gland map, which is
the same as pore distribution – a Level 3 fingerprint identification
feature.
To compare the differences between a dummy finger and its live
counterpart, both B-Mode images and their 3D rendering are given in Fig. 2.9. Note
that the 3D volumetric display is after the digital straightening.
26
Figure 2.9
Upper left: B-Mode image (x-z plane) of a live fingertip showing the
stratum corneum, eccrine glands and the papillary layer; lower left:
B-Mode image of the artificial fingertip shows no internal banding
structure and no eccrine gland ducts. The two B-Mode images are
displayed over 0.6 × 3.9 mm field of view and 30 dB dynamic range
in gray scale. Upper right: 3D display of the digitally straightened
live finger; lower right: volumetric display of the fake finger whose
surface resembles with that of the real fingertip. The two volumetric
images are shown over a 4.6 × 4.0 × 0.5 mm region.
Clearly indicated by Fig. 2.9, the lack of internal structure in the dummy finger makes
SD-OCT based fingertip biometric system spoofing proof, although the top surfaces of
the two volumes appear similar, which contributes to the susceptibility to artificial
fingers of current AFIS. Another observation by comparing the two cross sectional
images is that we can see the natural curvature of the live finger and the dummy finger
is different. This attributes to the touchless feature of the SD-OCT system. And the
preservation of the fine natural curvature can be utilized as another extra level of
information in touchless AFIS.
27
Eccrine gland duct distribution maps along with the surface and internal
fingerprints for three different fingertips of one subject are given in Fig. 2.10. The
dummy fingertips and their corresponding images are also shown for comparison.
Three fingers – thumb, index finger and middle finger – of a male left hand are taken
as the samples along with their scattering dummies. Figs. (a)-(c), (g)-(i), (m)-(o) are
live thumb, forefinger and middle finger images, respectively; Figs. (d)-(f), (j)-(l), (p)(r) are the layers extracted at corresponding depths in the dummies for the thumb, the
forefinger and the middle finger. Surface and papillary layer images are displayed in a
30 dB dynamic range gray scale and the eccrine gland distribution is shown in 1.7 dB
reverse gray scale. Although the actual scanning range is larger than what is displayed
in Fig. 2.10, a 4 × 4 mm area is used for all the images in Fig. 2.10 in order to be in
agreement with palmar sweat index (PSI) [26, 27] and for better registration between
real and fake fingerprints.
From the left column we can find that the dummy fingers bear a close
resemblance to the real fingers in surface fingerprint pattern. The comparison given by
the middle column and the right column demonstrates the feasibility and robustness of
this approach to tell dummy fingertips from the real ones. Another observation is that
in this specific subject, different fingertips all give an eccrine gland duct density of
higher than 640/cm2. This density gives sufficient information within a fairly small
region for identification purposes.
28
Figure 2.10 (a)-(c) the top, eccrine gland and papillary layers of the thumb; (d)(f) the three layers extracted at the same positions or ranges as those
in (a)-(c) for the dummy of the thumb; (g)-(i) the top, eccrine gland
and papillary layers of the index finger; (j)-(l) the three layers
extracted at the same positions or ranges as those in (g)-(i) for the
dummy of the index finger; (m)-(o) the top, eccrine gland and
papillary layers of the middle finger; (p)-(r) the three layers
extracted at the same positions or ranges as those in (m)-(o) for the
dummy of the middle finger.
29
It is known that eccrine glands may not be active at all time. Also due to
the mechanism of SD-OCT and the composition of the skin tissue surrounding the
eccrine gland ducts, an inactive eccrine gland may not be discernable in a
reconstructed image [28]. This issue poses potential question to this biometric
mapping approach. Therefore the repeatability of eccrine gland maps in different
subjects scanned in different days is tested and the results are given in Tab. 2.1:
Table 2.1
Subject
Scan
1st
Round
2nd
Round
3rd
Round
Repeatability of Eccrine Glands in Different Subjects Tested in
Different Days
Subject 1
Miss New Match
≥157; template
11
3
90.85%
11
2
91.72%
Subject 2
Miss New Match
10
2
89.47%
Subject 3
Miss New Match
2
≥114; template
5
1
93.48%
1
96.34%
≥99; template
5
1
93.94%
Three rounds of scan are performed on each subject. One of the scans is
selected for each subject as the template with which the other two rounds of scan are
compared to calculate the match, which is given as:
2.1
The number of eccrine glands used in the template is given in the table for each
individual subject. The Miss column gives the number of eccrine glands shown in the
template but not appear in the other scan. The New column gives the number of
eccrine glands shown in the scan but not appear in the template. From Table 2.1, we
30
can easily conclude that the repeatability of eccrine glands in different subjects is fairly
high. This implies that the eccrine gland mapping is a reliable approach for biometric
application.
2.4 Conclusion and Future Work
Human fingertip eccrine glands can be mapped for biometric identification
purpose using SD-OCT. The map, along with the surface and internal fingerprints,
which are also retrievable through SD-OCT, shows high repeatability for the same
subject and universality for different subjects. The identification based on eccrine
gland maps, though similar with poroscopy, should have its own software algorithm,
which is one of the future works.
Another direction to go which is made possible by SD-OCT based
biometric system is the topography and touchless scan. Shown in Fig. 2.11 there are
two topography images showing the surface fingertip ridge-furrow structure as well as
the internal one. This topography information is useful to enhance the counterspoofing ability. Also, the overall contour of the fingertip, resolved in the lower right
image, gives potential for touchless fingerprint identification application.
31
Figure 2.11 a, topography of the surface fingerprint; b, topography of the
internal fingerprint; c, triple-layer display of the volumetric
fingertip image; d, reconstructed 3D surface without digital
straightening shows the feature of touchless scan.
32
Chapter 3
PHOTOACOUSTIC MICROSCOPY
In this chapter, two PAM systems are introduced. The first PAM system is
applied to study collagenase-induced-osteoarthritis (CIA) in bovine articulate cartilage
samples. Using the SD-OCT system introduced in Chapter 2, images for the same
bovine samples are reconstructed and compared. The chemical treatment of the bovine
cartilage sample enables a longitudinal study using both PAM and SD-OCT and some
useful information about different stages of CIA is extracted from comparing these
two modalities‟ images.
Another OR-PAM system is used for spectroscopic PAM study. Using a
supercontinuum source and a home-mode bandwidth selection mask, fast selection of
different bandwidths makes this spectroscopic OR-PAM system suitable for
differentiating different kind of absorbers within a sample. Both ink spot and dyed
cotton swab phantoms are used to demonstrate this feature and the micrometer level
lateral resolution.
3.1 PAM and SD-OCT in Osteoarthritis Studies
Osteoarthritis (OA) is a disease characterized by deterioration and
localized erosion of articular cartilage, accompanied by bone remodeling and changes
of the periarticular tissues, and characterized by a variable rate of progression [29].
There is no complete cure for OA currently, hence the degenerateness of this disease
that is symptomatically affecting about 14% of the adult population of the U.S. alone
33
[30]. Thus, non-invasive imaging modalities for OA detection are needed for the
monitoring of this disease and are especially useful for early stage prevention of the
degeneration of OA.
Both SD-OCT and PAM have been applied in imaging cartilage
structures. SD-OCT, with its high resolution, gives information of articular cartilage
structural changes including cartilage thinning, fissures and fibrillation [30-32]; PAM
has been used to the diagnosis of OA by detecting the ultrasound pulses‟ relaxation
time changes [33, 34]. Although SD-OCT showed promising images for the changes
in OA samples, the lack of contrast in the gray scale intensity images makes this
modality hard to be used as easy and decisive criteria for diagnostic purposes. The
PAM method, though accurate in the aspect for relying on the relaxation time of the
ultrasound pulses within the tissue, neglected the most helpful feature of photoacoustic
effect – the absorbing agent as a contrast enhancer [35]. Therefore, a combination of
SD-OCT and PAM imaging techniques for the detection of CIA has the potential to
give high-resolution structural images with high-contrast.
The CIA will cause cartilage fibrillation, erosion of cartilage and
eventually the erosion of subchondral bone [36]. For in vitro treatment using
collagenase, the destruction of cartilage superficial zone and the reduction of safraninO stainability were observed, indicating breakdown of the cartilage layers [37]. By
histology, a digestion of the superficial zone of the murine femoral condyles‟ cartilage
after 10 hours of in vitro treatment by collagenase was observed. Though intraarticular injection of collagenase is better in producing OA like changes, in vitro
treatment‟s faster speed of digestion [37] makes it preferred in this study.
34
After the collagenase treatment of cartilage, India ink staining is a very
useful and efficient way to tell the degradation of the cartilage. The particles of India
ink cannot penetrate into the intact cartilage, whose pore size is around 6 nm, but can
be entrapped by surface irregularities and fibrillation of the degraded cartilage [38].
Previous studies show that the degree of India ink staining is proportional to the
fibrillation, erosion of the cartilage and the subchondral bone [39]. Therefore India ink
staining is used to indicate in a macro scale the stages of OA development [39-43]. At
the same time, India ink, consisting of both small (0.1 µm diameter) and large (1.0 µm
diameter) particles [44], can serve as a good absorber for the PAM mechanism and a
scattering enhance agent due to the significant scattering component in its optical
attenuation coefficient for SD-OCT scanning.
In this section, experiments are performed firstly on an intact region of the
bovine osteochondral core cartilage by both SD-OCT and PAM. A fissure, presumably
due to the spontaneous OA in the bovine knee joint, which is noticeable by unaided
eye, is also scanned by these two imaging modalities after India ink staining. After
these scanning, the osteochondral core undergoes 13 hours of in vitro collagenase
treatment for CIA. The treated sample is again imaged by the two techniques after
India ink staining for an overt fibrillation and subchondral bone exposed region. Pairs
of images for both SD-OCT and PAM at the same regions are given to show the
structural changes and the distribution of India ink.
3.1.1
Optical Imaging for OA
Fig. 3.1 gives the schematic of the PAM system designed for this
particular imaging application:
35
Figure 3.1
Schematic of the OR-PAM system for cartilage imaging
A Q-switched Nd:YAG microchip laser (NP10820-100, Teem Photonics)
produces 0.9 ns duration pulses at 1064 nm with 8 µJ of energy at a 6.6 kHz repetition
rate. The frequency of the laser pulse is doubled by a frequency-doubling crystal and
then delivered in optical fiber. The final stage 532 nm laser pulse illuminating the
sample is measured to be 1 µJ. A double-prism is attached to a 50 MHz flat surface
ultrasound transducer (Olympus) to reflect the excitation light to the sample but let
through the PA wave. On the laser incident side of the double-prism a plano-convex
glass lens is used to compensate for aberration at the light-glass interface while a
plano-concave acoustic lens on the PA wave side is used for correcting the aberration
for the ultrasound wave. A holder for the osteochondral plug is designed to secure the
position of the sample during the hours‟ long scan. The holder is glued to the bottom
of a high profile petri dish, which contains 1X phosphate buffered solution (PBS) to
preserve the cartilage sample.
36
The petri dish is secured in its position while the probing head performs a
raster scan using two orthogonally placed stepper motors (T series, Zaber Technology)
over the cartilage surface. PA signals are amplified by a 60 dB amplifier (ultrasound
pulser/receiver, 507LPR, Olympus) before digitized by an oscilloscope (WaveJet 324,
LeCroy) and recorded by PC. All the system synchronization and data saving functions
are realized by LabVIEW programs. The increment of the two stepper motors is set to
10 µm considering the spatial and axial resolution of this system, which are ~30 µm
for each. The stepper motor of the SD-OCT system is also set to 10 µm for this
specific application in agreement with the settings for the PAM system.
In order to improve image SNR in the PAM image, a Butterworth 7th
order low pass filter having a cutoff frequency of 70MHz was applied in MATLAB
code and a linear interpolation to the factor of 3 was used in both dimensions of the
PAM B mode image. The sampling frequency for this PAM scan is 1GHz.
3.1.2
India Ink Staining
Commercially available India ink (Higgins Water Proof Ink, Higgins) is
purchased for the staining of the cartilage. The detailed procedure is:
1. Move the osteochondral core from 1X PBS solution;
2. Drip the surface of the core with India ink and wait for about half a
minute;
3. Rinse with tap water for about 1 minute;
4. Use a Q-tip to brush out the remaining ink on the cartilage surface;
5. Repeat 2-4 for three times;
6. Put the osteochondral core back into sample holder and fill the
holder with PBS solution.
37
3.1.3
Collagenase Treatment for the Sample
Solution of 150 units/ml bacterial collagenase (Collagenase Type 2,
Worthington Biotech) in 50mmol/l Tris-HCl containing 5mmol/l CaCl2 is prepared for
the treatment [45] of the osteochondral core. The sample, fastened in the HDPE block
is submerged into a beaker of this solution and the beaker is placed on top of a hot
plate (Cimarec, Barnstead/Thermdyne) for heating. A magnetic stirrer is placed within
the beaker to agitate the solution for better digestion. The temperature of the solution
was measured to be 38°C and the pH was maintained within 7.33-7.4 in our home
build rocker. The treatment lasts 13 hours.
3.1.4
Results and Discussion
Before any results are shown, a well-accepted India ink cartilage staining
categorization is given below [40]:
Sites at which no distinct markings were seen;
Sites showing dark ink markings against a pale grey background, such
marking having either approximately parallel alignments or
other, more complex, configurations on macroscopic
examination;
Sites showing confluent or semi-confluent blackening;
Sites of full thickness loss of the original cartilage;
Experiment results are analyzed per the list above.
Fig. 3.2 gives the B-Mode images for both the untreated and post
treatment cartilage. In Fig. 3.2 (a) a photo is taken for the osteochondral plug (after
collagenase treatment) in the high profile petri dish. A ruler is used to show the
dimension. As we can see, the osteochondral plug is held in a holder and India ink is
applied to stain the cartilage. Fig. 3.2 (b) and (c) give the SD-OCT image and the
38
PAM image for the intact bovine cartilage, respectively. The two slices are carefully
chosen to represent the same registered area in the osteochondral plug. For the intact
cartilage, India ink is not able to stain the surface, thus the reconstructed PAM BMode image does not show anything but background noise. In contrast, Fig. 3.2 (b)
shows the smooth inner structure of the cartilage. Close to the position where (b) and
(c) are taken, there is a cleft in the cartilage which is presumably caused by
spontaneous OA. This cleft is taken to be imaged before collagenase treatment and the
results are given in Fig. 3.2 (d) and (3) for SD-OCT and PAM, respectively. The cleft
measures about 10 µm in dimension, which is too small for current major arthroscopic
method to detect. Fig. 3.2 (f) and (g) are the B-Mode images for the treated cartilage
(photo given in (a)) using SD-OCT and PAM, respectively. Overt fibrillation is shown
in the treated cartilage, this is confirmed by the wide spread entrapment of India ink
implied by the PAM image. In (f) we can also see that the inner display of the cartilage
in SD-OCT also changes before and after the collagenase treatment.
All B-Mode SD-OCT images are shown in 40 dB dynamic range while
PAM B-Mode images are given in 19 dB gray scale.
39
Figure 3.2
a, photo showing the treated cartilage submerged in PBS solution; b,
SD-OCT image of the untreated cartilage showing an intact region;
c, PAM image of the same region as in B; d, SD-OCT image for the
untreated cartilage showing a small cleft in the surface; e, PAM
image reveals the depth and shape of the fissure in d; f, SD-OCT
image showing the 13-hour treated CIA cartilage, note that the
smooth gradual change in depth as in b & c is now replaced by a
~10 µm bright surface layer and dark region beneath; g, the PAM
image for the same region as shown in f.
3D display is a built-in feature of both SD-OCT and PAM. By stacking
together slices and carefully choosing the viewing perspective, volumetric figures are
given in Fig. 3.3 using SD-OCT and in Fig. 3.4 using PAM for the post treatment
40
bovine cartilage showing CIA. Fig. 3.3 and 3.4 cover a region of 4 mm× 0.578 mm × 1
mm and 5 mm × 0.59 mm × 1.54 mm (width × length × depth), respectively.
Figure 3.3
3D volumetric display using SD-OCT showing the isosurfaces with
71, 77 and 83 dB intensity. Isocaps are added and isonormals
calculated.
41
Figure 3.4
3D volumetric display using PAM showing the isosurfaces with -23,
-20 and -17 dB intensity. Isocaps are added and isonormals
calculated.
From the two volumetric images, manifested surface structure and indepth banding in SD-OCT are observed, which is different than the India ink staining
pattern indicated by Fig. 3.4. Thus, combining these two images allows a detailed
structural display of the cartilage as well as a depth and lateral distribution information
of India ink.
3.1.5
Conclusions
SD-OCT shows better than 15μm resolution laterally and axially, enabling
the imaging of delicate structural change detection in the bovine femoral condyle
articulate cartilage before and after collagenase treatment. PAM shows great contrast
42
between intact and fibrillated region in cartilage. India ink, as an indicator of overall
cartilage OA stage, successfully served as the optical absorber, hence contrast
enhancer, for the PAM images. The combination of SD-OCT and PAM shows
promising future in OA progression detection and monitoring.
3.2 Spectroscopic PAM Using a Supercontinuum Source
In the previous section, the wavelength of the illumination source is fixed
at 532 nm, thus precludes its use in spectroscopic PAM. In this section, a tunable
optical source based on the same microchip laser is developed, generating a
supercontinuum spectrum spanning 500 – 1300 nm. The PA detection is achieved
using a 25 MHz spherically focused detection transducer. By selecting different
wavelength bands from the supercontinuum spectrum and run a raster scan, the PA
data produces images that clearly distinguish the different absorbing regions of ink
phantoms after a simple discriminant analysis.
3.2.1
From OR-PAM to Spectroscopic PAM
From Eq. 1.16 we can tell that low laser pulse energies (e.g. less than 100
nJ) are still sufficient for OR-PAM because the extremely small optical focus produces
high enough optical fluence. Therefore, OR-PAM can be used with high repetition rate
pulsed lasers to significantly increase image acquisition speed.
The Q-switched microchip laser source described in the previous section,
however, emits single wavelength pluses, making it not suitable for spectroscopic
PAM [46-48]. To deal with this, we propagate the laser pulses through several meters
of photonic crystal fiber (PCF) to generate an ultra-broadband spectrum [49, 50]. After
the tunable band pass filter, the low pulse energy is still sufficient for OR-PAM. Seven
43
different wavelength bands are used to successfully differentiate ink phantoms with
overlapping absorption spectra.
3.2.2
Materials and Methods
Fig. 3.5 depicts the system and a photo for the resulting supercontinuum
spectrum. In Fig. 3.5 (a) we can see the laser pulses at 1064 nm is coupled into the 7 m
long PCF (Crystal Fibre, Inc.) through an aspherical lens (NA = 0.4). The air-silica
honeycomb-like microstructure of the PCF provides favorable dispersion properties to
boost the nonlinear optical transmission. The input average power is 51 mW and the
output power is 2.8 mW for the supercontinuum. After collimated by a 20X
microscope objective lens, the beam diameter is measured to be 3 mm. Fig. 3.5 (b)
gives the spectrum measured by an optical spectrum analyzer with a useful scanning
range from 600 to 1700 nm. The residual power in the pump wavelength is represented
by the large spike at 1064 nm. Fig. 3.5 (c) shows the dispersed light on a piece of
paper, which confirms that the supercontinuum contains wavelengths near 500 nm,
which is beyond the useful scanning range of the optical spectrum analyzer used to
give Fig. 3.5 (b).
44
Figure 3.5
a, Q-switched microchip Nd:YAG laser emits 1064 nm pulses
coupled into and progagate through 7 m PCF (λzdw = 1040 nm) to
generate a supercontinuum. The PCX collimates the microchip laser
output. An asperic lens (AL) focuses the 1064 nm pulses into the
PCF. A microscope objective (MO) collimates the supercontinuum
output; b, The spectrum of the supercontinuum measured by an
optical spectrum analyzer; c, A photo showing the dispersed
supercontinuum on a piece of white paper.
Fig. 3.6 shows the mechanism of wavelength selection. The dispersed
light from an equilateral BK-7 prism is collimated by a 75 mm diameter spherical
mirror with a focal length of 500 mm. Spectral filtering is performed in the Fourier
plane using a manually adjusted slit placed in front of the mirror [51]. The flat mirror
has a slight vertical tilt in order to separate the return beam from the incident beam.
45
Seven wavelength bands with 40 nm bandwidth from 575 to 875 nm in 50 nm
increment are used for this experiment with 7, 15, 24, 31, 31, 31 and 33 nJ as their
pulse energies.
Figure 3.6
Prism-based monochromator, where a concave mirror collimates the
dispersed light.
The schematic of this system is modified from the one given in previous
section. As shown in Fig. 3.7, the PA beam splitter transmits the laser beam but
reflects the PA signal. The excitation laser pulse is focused with 4X infinity corrected
microscope objective (NA = 0.1). The beam splitter is composed by a plane glass
wafer surrounded on both sides by water. The upper water section is in contact with a
glass PCX lens for aberration reduction. The lower water section is in contact with a
25 MHz spherically focused f/2 transducer (Olympus V324). The bottom of the
chamber is sealed by 25 micron thick Mylar membrane. The detection and the raster
scanning mechanism are the same with the one described in the previous section.
46
Figure 3.7
Schematic of the OR-PAM system featuring a 25 MHz transducer
3.2.3
Results and Discussion
En face imaging of a USAF resolution target (Edmund Optics) was
performed on Group 3 elements and the simulated profile assuming a Gaussian-shaped
point spread function (PSF) with a FWHM of 18 µm is indicated as the lateral
resolution. Four ink spots with black, blue, green and red color are imaged afterwards.
MAP images of these spots for all the seven wavelengths are given in Fig. 3.8 over 40
dB dynamic range covering an area of 1.8 × 5.4 mm. The different colored spots
clearly exhibit different wavelength behavior. Using a simplified discriminant analysis
[9], the classified groups are displayed as red-green-blue (GRB) image. The
classification has some error, such as in the blue and green ink regions and for the
black ink spot which appeared purple in the reconstructed image. Higher accuracy is
achievable with more sophisticated multispectral image analysis and a larger number
of wavelength bands.
47
Figure 3.8
Multi-wavelength images of black, blue, green and red ink spots
(left to right). All images displayed over a 1.8 × 5.4 mm range in 40
dB scale. The spectrally processed image clearly identifies the four
ink regions.
In order to demonstrate the system‟s optical resolution, a more realistic
phantom made of stained cotton-swab fibers are imaged under 0.4 mm scattering layer
consisting of an aqueous suspension of 1 µm diameter polystyrene microspheres at a
0.09% concentration whose extinction coefficient measured by spectrophotometry is
1.9 mm-1 at 675 nm [9].
Though showing great potential of real world application using this
spectroscopic OR-PAM system, two issues are present: 1. the bandwidth selection is
manually controlled. Each time this experiment is done the cover slit needs to be
manually adjusted to select the desired wavelength bandwidth. This makes the
experiment very time consuming; 2. the bandwidth is 40 nm, although useful for many
48
spectroscopic applications, is still big for more accurate detection and monitoring.
Efforts are made to solve these issues and some results are given in the following
sections of this thesis.
3.3 Wavelength Agile Spectroscopic PAM
In this section, the system described in section 3.2 has a new automatic
wavelength bandwidth selection mechanism enabling fast sweeping through more than
10 bandwidths with 20 nm span. The acquisition is also changed from digital
oscilloscope to a DAQ board for fast data transfer and easier system synchronization.
These system modifications are introduced in detail in this section and experiment
results on ink spots and cotton swabs are given to demonstrate the function of this
wavelength agile spectroscopic PAM system.
3.3.1
Tunable Wavelength Filter
In Fig. 3.5 (c) we observe that in the supercontinuum spectrum, each
wavelength occupies a different lateral position along the focal plane of the mirror.
This allows spectral filtering to be achieved by placing an appropriately sized slit. The
width of the slit and its projection on the spectrum determines the wavelength range of
the filtered light. Fig. 3.9 shows the automatic externally triggered tunable wavelength
filter. A voice-coil actuator called the wave driver (SF-9324, Pasco Scientific) drives
the leverage system to provide rapid wavelength sweep from 570 to 930 nm within
500 ms. Since the dispersed wavelengths are not evenly distributed along the focal
plane of the concave mirror as shown in Fig. 3.6, the slit is tapered to produce an
approximately constant spectral bandwidth during the wavelength sweep. The
bandwidth for the filtered light is measured to be 20 nm with about 10 nJ pulse energy.
49
Due to the low pulse energy, signal averaging is done during acquisition for better
SNR. Immediately behind the slit is a flat mirror to reflect the spectrally filtered light
back to the concave mirror as is described in the previous section. This wave driver is
controlled by a function generator & amplifier (Frederiksen) which is externally
triggered. The amplitude and frequency of the triangular output from the function
generator & amplifier is predetermined based on experiment results.
Figure 3.9
The automatic tunable wavelength filter. One end of the lever is
fastened on top of the wave driver. The other end of the lever is
connected to the mask, with a tapered slit going in the diagonal
direction. A supercontinuum spectrum is drawn over the mask to
demonstrate the mechanism of how bandwidth selection is
performed.
3.3.2
Data Acquisition and System Synchronization
The data acquisition is achieved by a PCI DAQ board (NI 5114, National
Instruments) in this experiment. The board provides dual channel 250 MS/s sampling
rate with 8 bit analog to digital conversion (ADC) and 8 MB on board memory.
50
Trigger is provided externally from the photodiode detecting the 1064 nm laser output
but a software acquisition delay is in effect to compensate for the time elapse before
the mask moves to such a position that the first desired bandwidth is selected. This
delay time is experimentally determined by triggering the acquisition in software and
recording a fairly long continuous trace.
The whole system works in the following manner:
2D stepper motors position the sample to the proper location;
Wave driver moves downward, hence the upward movement of the
mask;
After waiting for a certain delay time, the acquisition starts on the DAQ
board;
Number of average for each bandwidth is predetermined
experimentally. The averaged data corresponding to all the
bandwidths are saved in the on board memory during one
upward movement of the mask;
One position‟s data is streamed to the hard drive of the PC. The mask
moves back to the initial position and the 2D motor moves the
sample to the next position for scan.
3.3.3
Results and Discussion
MATLAB is used to produce the MAP images for all the bandwidths and
the same discriminant analysis is applied to give RGB coloring to the MAP images.
Fig. 3.10 shows the 4 ink spot MAP images for all the bandwidths. All images span an
8.4 × 1 mm region and are given in 10 dB dynamic range. From left to right they are
red, green, blue and black in color. Compare this with Fig. 3.8 we can see that this
system provides similar spectroscopic PAM function as that described in section 3.2
51
but significant speed improvement is realized in this system, potentially enabling
functional imaging.
Figure 3.10 Ink spot images for four ink spots (red, green, blue black from left
to right). Sequential bandwidths see 20 nm increments in their
central wavelength. Images are displayed over 8.4 × 1 mm region in
10 dB range gray scale.
Cotton swab images are given in Fig. 3.11 along with its spectrally
processed RGB display.
52
Figure 3.11 The fiber phantom taken at 570 nm and 690 nm with the spectrally
processed image in RGB display.
All the three images in Fig. 3.11 are shown in a 0.9 ×0.9 mm range over
20 dB scale. The disappearance of the two parallel diagonal fibers visible in the 570
nm image suggests these threads are stained by red ink. This is confirmed in the RGB
image. The remaining threads are identified as stained with either green or blue ink.
Only the 570 nm and 690 nm images are used for the RGB image, so there is slight
error (blue-green). This should be corrected with a more sophisticated spectral
processing algorithm.
3.3.4
Conclusions
In this section, a spectroscopic PAM system with rapidly tunable optical
source based on a PCF supercontinuum source is demonstrated. The tunable bandpass
filter provides rapid access to any desired wavelength band.
53
Chapter 4
FUTURE WORK
In sections 3.2 and 3.3, OR-PAM system has been applied on ink spot and
cotton swab phantoms and shows promising images successfully differentiating either
ink spots or stained cotton swabs with different colors. With this in mind, we need
better phantoms for microvasculature PAM imaging and higher pulse energies in each
bandwidth for better SNR. Previous studies have shown the potential of using PAM to
discover the hemoglobin oxygen saturation (SO2) in the subcutaneous
microvasculature of rats in vivo. Their phantom tests using two inks matched the
actual values pretty well and the SO2 acquired by PAM agrees with the
spectrophotometric measurements with only a 4% systematic difference [8]. Inspired
by this research, our future work involves developing a system with high energy multiwavelength pulses for an OR-PAM system to test the ratio of a certain dye component
in mixed dye solutions enclosed in thin tubes mimicking human vasculature. Some
preliminary results are given in the following sections.
4.1 Spectrophotometry for Mixed Dye Solutions
From section 1.2 we know that the absorption coefficient of a sample is
directly proportional to the PA signal generated after laser illumination. In the
preliminary test, red and blue food dyes (McCormick) are used and mixed with
different mass ratios. Eq. 4.1 gives the relationship between PA signal strength and the
dye absorption coefficient:
54
4.1
where
1
is the extinction coefficient of the solution at a certain wavelength (cm-1M-
); [red] and [blue] are the concentrations of the dye solutions; PA(λ) is the PA signal
detected at a certain wavelength. Denote the extinction coefficient matrix as M, the
concentration matrix as C and the photoacoustic signal matrix as PA, using leastsquare-method (LSM), we can then calculate the concentration of the two dyes based
on the PA matrix and the predetermined M matrix, which is given in Eq. 4.2:
4.2
Therefore we need accurate measurement of the extinction coefficients to get correct
concentration information. A home-made spectrophotometry system is used to test
solutions with various red/blue dye ratios. Fig. 4.1 shows the schematic of the
spectrophotometry system with multiple views of the home-made cuvette. A halogen
bulb is used as the white light source. The white light is focused on the cuvette, and
then coupled into another optical fiber directly connected to a spectrometer (SP2-USB,
Thorlabs). The data is stored in PC and processed using MATLAB. Cost consideration
makes us fabricate cuvettes on our own using glass slides and cover slips (Fisher
Scientific). The side view and top view of the home-made cuvette in Fig. 4.1 shows
the overall structure. The effective thickness of the cuvette is 0.19 mm in the
experiments. Solutions are injected into the gap between the cover slip and the glass
slide carefully before spectrophotometry experiment. After a certain solution is
measured by this system, 0.9% saline solution is used to wash thoroughly the cuvette
55
followed by pure water wash. The cuvette is kept stable in the system to guarantee the
same thickness being used when different solutions are tested.
Figure 4.1
The Spectrophotometry System with a Separate Display of the
Cuvette
The spectrum data are averaged by 100 before recording and the
integration time for the spectrometer is 40 ms. An all dark spectrum by blocking the
cuvette with opaque cover is recorded along with a reference spectrum by using pure
water in the cuvette.
The recorded data along with the all dark spectrum and the reference
spectrum are used to calculate the transmittance first using the following equation
4.2
where A is the recorded spectrum data. The transmittance is related to the extinction
coefficient showing in Eq. 4.3:
56
4.3
where d is the thickness of the cuvette. From Eq. 4.3 we can easily get the expression
for the extinction coefficient given below:
4.4
Using the equations above, the extinction coefficient for different mixtures is given in
Fig. 4.2 as a function of wavelength:
Extinction coefficient V.S. wavelength for different dye mixtures
RED
BLUE
RED:BLUE=1:2
RED:BLUE=2:1
RED:BLUE=1:3
RED:BLUE=3:1
200
180
160
cm-1
140
120
100
80
60
40
20
0
550
Figure 4.2
600
650
700
750
800
/nm
850
900
950
1000
Extinction coefficients calculated from the spectrophotometry data
for mixtures made of red and blue food dyes with different ratios.
The processing includes a moving average filter for the raw data to smooth
the spectra. The extinction coefficients are saved in matrix format as a function of
57
wavelength for future use in the concentration calculation by spectroscopic PA
experiment.
4.2 Stokes Source OR-PAM with Fast Raster Scanning
Systems described in Chapter 3 suffers from two main problems: the low
energy from the laser source available for each bandwidth and the slow speed of
scanning which takes hours for finish a fairly small region (< 1 mm2). In order to
overcome these two concerns, a Stokes source for the OR-PAM system is developed
using stimulated Raman scattering (SRS) in single mode optical fiber [52]. After
filters matched with each Stokes line, three wavelengths are picked to image the
sample, which are 532 nm (seed light frequency), 546 nm and 560 nm with pulse
energies being approximately 60 nJ, 80 nJ and 110 nJ, respectively.
When a certain wavelength is selected, fast raster scan is performed using
two stepper motors. The x-axis stepper motor moves at a constant speed with
maximum acceleration over the full range in x-direction. A laser pulse counter circuit
is made to select every 1 in 8 pulses from a photodiode detector and this selected pulse
is used to trigger the data acquisition performed by the NI-DAQ board. When each line
in the x-direction is finished, the y-axis motor moves a certain distance. Then the xaxis motor moves at the same speed in the backward direction as it was moving in the
forward direction. Data is also collected during this backward direction movement. All
the scanned records are temporarily stored in the DAQ device on board memory and
saved in the hard drive after the whole raster scan is finished.
The new Q-switched microchip laser source (Teem Phonics) emits 1064
nm pulses at 7.45 kHz repetition rate with a pulse width of 0.6 ns. All the other
hardware instruments are the same with those in section 3.2 and 3.3. The digital
58
counter circuit and the LabVIEW programs are carefully designed to take care of all
the synchronization and data saving issues. A MATLAB code is programed to process
the data and compensate for the interdigitating error in the reconstructed MAP image
caused by the dual direction scan of the x-axis stepper motor. An image reconstructed
for the USAF resolution target is shown in Fig. 4.3 with Group 3, 4, 5, 6 elements
displayed. Though in this image only Group 5 elements can be well resolved,
corresponding to a maximum lateral resolution of 17.5 µm, better alignment of the
system and further process to compensate for the interdigitating error should get us
better resolution limited by the optical focal spot size, which is calculated to be around
8 µm.
Figure 4.3
USAF resolution target imaged by Stokes source OR-PAM system.
All elements in Group 4, 5, 6 and 7 are in the scanning range. From
the vertical bars we can see that the interdigitating error has not
been fully removed.
Future work is to apply this system on tubing phantoms filled with
different colored dye solutions showing various component mixing ratios. After PA
signals are acquired for these phantoms, using the LSM described in section 4.1, we
59
can test if the calculated concentration matches the real value. This can further
determine if this system can be used for fast functional spectroscopic imaging.
60
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65
APPENDIX
HUMAN RESEARCH APPROVAL FORM AND HUMAN SUBJECT
CONSENT FORMS
66
1
2
3
4
5
6
7
8