Download Three-dimensional ultrasound imaging

INSTITUTE OF PHYSICS PUBLISHING
PHYSICS IN MEDICINE AND BIOLOGY
Phys. Med. Biol. 46 (2001) R67–R99
www.iop.org/Journals/pb
PII: S0031-9155(01)12089-0
TOPICAL REVIEW
Three-dimensional ultrasound imaging
Aaron Fenster1 , Dónal B Downey and H Neale Cardinal
The John P Robarts Research Institute, London, Canada
and the Department of Diagnostic Radiology and Nuclear Medicine,
The University of Western Ontario, London, Canada
E-mail: [email protected]
Received 6 November 2000
Abstract
Ultrasound is an inexpensive and widely used imaging modality for the
diagnosis and staging of a number of diseases. In the past two decades, it has
benefited from major advances in technology and has become an indispensable
imaging modality, due to its flexibility and non-invasive character. In the
last decade, research investigators and commercial companies have further
advanced ultrasound imaging with the development of 3D ultrasound. This
new imaging approach is rapidly achieving widespread use with numerous
applications.
The major reason for the increase in the use of 3D ultrasound is related to the
limitations of 2D viewing of 3D anatomy, using conventional ultrasound. This
occurs because: (a) Conventional ultrasound images are 2D, yet the anatomy
is 3D, hence the diagnostician must integrate multiple images in his mind.
This practice is inefficient, and may lead to variability and incorrect diagnoses.
(b) The 2D ultrasound image represents a thin plane at some arbitrary angle in
the body. It is difficult to localize the image plane and reproduce it at a later
time for follow-up studies.
In this review article we describe how 3D ultrasound imaging overcomes
these limitations. Specifically, we describe the developments of a number
of 3D ultrasound imaging systems using mechanical, free-hand and 2D array
scanning techniques. Reconstruction and viewing methods of the 3D images
are described with specific examples. Since 3D ultrasound is used to quantify
the volume of organs and pathology, the sources of errors in the reconstruction
techniques as well as formulae relating design specification to geometric errors
are provided. Finally, methods to measure organ volume from the 3D ultrasound
images and sources of errors are described.
1 Address for correspondence: The J P Robarts Research Institute, Imaging Research Laboratories, 100 Perth Drive,
London, Ontario, Canada N6A 5K8.
0031-9155/01/050067+33$30.00
© 2001 IOP Publishing Ltd
Printed in the UK
R67
R68
A Fenster et al
1. Introduction
For most of the last century, medical imaging has involved visualizing the interior structures of
the human body with two-dimensional (2D) x-ray images. Although 2D x-ray imaging remains
an indispensable imaging modality, it has the disadvantage that almost all three-dimensional
(3D) information about the interior of the human body is lost to the physician. In the early
1970s, the introduction of computed tomography (CT) revolutionized diagnostic radiology
by providing, for the first time, true 3D anatomical information to the physician. At first
this information was simply presented as a series of contiguous 2D image slices of the body.
However, the advent of 3D images from CT, and later magnetic resonance imaging (MRI),
provided a major impetus to the field of 3D visualization, which has lead to the development of
a wide variety of applications in diagnostic medicine (Fishman et al 1991, Robb and Barillot
1989, Vannier et al 1984).
Although the origins of medical ultrasound imaging can be traced back half a century to
the pioneering work of Wild and Reid (1952), progress was slow for the first three decades,
and, outside of research laboratories, medical ultrasound was largely limited to applications in
cardiology, obstetrics, and gynaecology. However, in the past two decades, medical ultrasound
has benefited from major advances in technology and has become an indispensable imaging
modality, due to its flexibility and non-invasive character. Moreover, due to continuous
improvements in image quality, and the availability of blood flow information via the Doppler
effect, ultrasonography is progressively achieving a greater role in radiology and cardiology,
and is finding new roles in image-guided surgery and therapy.
In the last decade, research investigators and commercial companies have further advanced
ultrasound imaging by combining these advances in ultrasound image quality with recent
advances in 3D image visualization, to develop 3D ultrasound imaging. This review article
discusses this new imaging modality, which is rapidly achieving widespread application in
medical imaging.
2. Limitations of ultrasonography addressed by 3D imaging
In a conventional ultrasound examination, the ultrasound transducer is manipulated to obtain
a series of 2D ultrasound images, which are mentally combined by the operator to form a
subjective impression of the 3D anatomy and pathology. The success of the diagnostic or
interventional procedure is thus largely dependent on the skill and experience of the operator
in performing these tasks. This is a suboptimal approach, due to the following limitations:
• Mentally transforming multiple 2D images to form a 3D impression of the anatomy and
pathology is not only time-consuming and inefficient, but is also more importantly, variable
and subjective, which can lead to incorrect decisions in diagnosis, and in the planning and
delivery of therapy.
• Diagnostic (e.g. obstetric) and therapeutic (e.g. staging and planning) decisions often
require accurate estimation of organ or tumour volume. Conventional 2D ultrasound
techniques calculate volume from simple measurements of height, width and length in
two orthogonal views, by assuming an idealized (e.g. ellipsoidal) shape. This practice can
potentially lead to low accuracy, high variability and operator dependency.
• Conventional 2D ultrasound is suboptimal for monitoring therapeutic procedures, or for
performing quantitative prospective or follow-up studies, due to the difficulty in adjusting
the transducer position so that the 2D image plane is at the same anatomical site and in
the same orientation as in a previous examination.
Three-dimensional ultrasound imaging
R69
• Because the location and orientation of conventional 2D ultrasound images are determined
by those of the transducer, some views are impossible to achieve because of restrictions
imposed by the patient’s anatomy or position.
In a 3D ultrasound examination, the 2D ultrasound images are combined by a computer
to form an objective 3D image of the anatomy and pathology. This image can then be viewed,
manipulated and measured in 3D by the physician on the same or another computer. Also,
a 2D cross-sectional image can be generated in any orientation, without restriction, at any
anatomical site, which may easily be registered with a previous or subsequent 3D image.
Thus, 3D ultrasound imaging promises to overcome the limitations of 2D ultrasound imaging
described above.
Moreover, unlike CT and MR imaging, in which 2D images are usually acquired at a slow
rate as a stack of parallel slices, in a fixed orientation, ultrasound provides tomographic images
at a high rate (15–60 s−1 ), and in arbitrary orientations. The high acquisition rate and arbitrary
orientation of the images provide unique problems to overcome and opportunities to exploit
in extending 2D ultrasound imaging to 3D and dynamic 3D (i.e. 4D) visualization.
Over the past two decades, many investigators have attempted to develop 3D ultrasound
imaging techniques (Brinkley et al 1982, Fenster and Downey 1996, Ghosh et al 1982,
Greenleaf et al 1993, King et al 1993, Nelson and Pretorius 1992, Rankin et al 1993).
However, progress has been slow due to the enormous computational requirements which
must be met in order to acquire, reconstruct and view 3D information in near real time on
low-cost systems. Advances in low-cost computer technology and visualization techniques in
the past few years have made 3D ultrasound imaging viable. A number of laboratories have
led the way in demonstrating the feasibility of 3D ultrasound imaging and, more recently,
commercial companies have begun to provide these options on their ultrasound equipment. A
few review articles and two books describing the development of 3D ultrasound imaging have
been published in the past few years (Fenster and Downey 1996, Greenleaf et al 1993, Rankin
et al 1993, Belohlavek et al 1993, Fenster 1998, Ofili and Nanda 1994, Nelson et al 1999,
Nelson and Pretorius 1998, Baba and Jurkovic 1997, De Castro et al 1998). In this review we
summarize the various approaches that have been used in the development of 3D ultrasound
imaging systems, with particular emphasis on the quantitative aspects of geometric accuracy in
viewing the anatomy in 3D and in measuring anatomical volumes with 3D ultrasound imaging.
3. 3D ultrasound scanning techniques
Most 3D ultrasound imaging systems make use of conventional 1D ultrasound transducers to
acquire a series of 2D ultrasound images, and differ only in the method used to determine the
position and orientation of these 2D images within the 3D image volume being examined. The
production of 3D images without distortions requires that three factors be optimized:
• The scanning technique must be either rapid or gated, to avoid image artefacts due to
involuntary, respiratory or cardiac motion.
• The locations and orientations of the acquired 2D images must be accurately known, to
avoid geometric distortions in the 3D image that would lead to measurement errors.
• The scanning apparatus must be simple and convenient to use, so that the scan is
not complicated or awkward to perform and hence easily included in the examination
procedure.
Over the past two decades, four different 3D ultrasound imaging approaches have
been pursued: mechanical scanners, free-hand techniques with position sensing, free-hand
R70
A Fenster et al
Table 1. Summary of 3D scanning methods, acquisition methods of images used to produce the
3D image, and the major disadvantages of the scanning methods.
Scanning method
Mechanical
Linear
Tilt
Rotational
Free-Hand
Acoustic
Articulated arms
Magnetic sensor
Image correlation
No position sensing
2D arrays
Image acquisition method
Disadvantages
Acquired images are parallel to
each other with equal spacing
Acquired images are fan-like with
equal angular spacing
Acquired images are propeller-like
with equal angular spacing
Bulky device
Measure time-of-flight of sound
from spark gaps on transducer to
microphones above patient
Measure angulation between
movable arms
Measure magnetic field generated
by transmitter beside the patient
with receiver on transducer
Measure speckle decorrelation
between adjacent images
Distance or angle between images
is assumed
2D phased array transmits a
diverging pyramidal beam and
returned echoes are displayed in
real time as multiple planes
Resolution degrades with depth
Motion of axis of rotation
results in artefacts
Line of sight required and sound
velocity varies with humidity
Scanning volume limited, flexing
of arms
Ferrous metals distort magnetic
field
Special computer processor
required, compound motion is
difficult to track
Cannot measure distance
System cost and signal/noise
techniques without position sensing and 2D arrays. These approaches are summarized in
table 1 and are discussed below.
3.1. Mechanical scanners
In this approach, the anatomy is scanned by using a motorized mechanical apparatus to translate,
tilt or rotate a conventional transducer as it rapidly acquires a series of 2D ultrasound images
spanning the volume of interest, which are recorded by a computer. Because the scanning
protocol is predefined and precisely controlled, the relative position and orientation of each
2D image can be known accurately.
Using current computer technology, the acquired 2D images may either be stored in their
original digital format in the ultrasound system’s computer memory, or the analogue video
output from the ultrasound machine may be digitized and stored in an external computer
memory. The reconstruction and viewing of the 3D image can then be carried out either in
the ultrasound machine’s computer or in an external computer, using the predefined geometric
parameters that describe the orientation and position of each 2D image within the 3D image
volume. Usually, the angular or spatial interval between successive 2D images is made
adjustable, so that it can be optimized to minimize the scanning time while still adequately
sampling the volume (Smith and Fenster 2000).
Various kinds of scanning apparatus have been developed to translate or rotate the
conventional transducer in the required manner. These vary in size from small integrated 3D
Three-dimensional ultrasound imaging
R71
Figure 1. Schematic diagrams showing the four mechanical scanning approaches. (a) Linear
scanning approach, in which a series of parallel 2D images are collected and used to reconstruct
the 3D image. (b) Tilt scanning approach, in which a series of 2D images are collected as the
transducer is tilted and then reconstructed into a 3D image. (c) Tilt scanning approach used with
a side-firing transrectal (TRUS) transducer to produce 3D images of the prostate. (d) Rotational
scanning approach used with a end-firing endocavity transducer in gynaecological and urological
imaging.
probes that house the scanning mechanism within the transducer housing, to external fixtures
that mechanically hold the housing of a conventional ultrasound probe.
Although the integrated 3D probes are larger and heavier than conventional probes, they
are usually easy for the operator to use. However, they require the purchase of a special
ultrasound machine that can interface with them. On the other hand, while external fixtures
are generally bulkier than integrated probes they can be adapted to hold the transducer of
any conventional ultrasound machine, obviating the need to purchase a special-purpose 3D
ultrasound machine. The image quality and imaging techniques offered by any conventional
ultrasound machine can thus also be achieved in 3D. Three basic types of mechanical scanners
have been developed, as shown schematically in figure 1: linear, tilt and rotational.
3.1.1. Linear 3D scanning. In this approach a motorized drive mechanism is used to linearly
translate the transducer across the patient’s skin, while the 2D images are acquired at regular
spatial intervals so that they are uniformly spaced and parallel (figure 1(a)). The ability to
vary the translation speed and sampling interval is necessary in order to match the sampling
rate to the frame rate of the ultrasound machine and to match the sampling interval to (half)
the elevational resolution of the transducer (Smith and Fenster 2000).
The simple predefined geometry of the acquired 2D image planes allows for a fast 3D
image reconstruction. Thus, using this approach a 3D image can be obtained immediately after
performing the scan, and its resolution can be optimized. Because the 3D image is produced
from the series of conventional 2D images, its resolution will not be isotropic. In the direction
parallel to the acquired 2D image planes, it will equal that of the original 2D images, but in the
perpendicular direction it will be equal to the elevational resolution of the transducer. Thus, the
3D image resolution will generally be the worst in the 3D scanning direction, and for optimal
results a transducer with good elevational resolution should be used.
The linear scanning approach has been successfully implemented in many vascular
imaging applications, using B-mode and colour Doppler images of the carotid arteries (Downey
R72
A Fenster et al
(a)
(b)
Figure 2. 3D ultrasound images of the carotid arteries of a patient with carotid atherosclerotic
disease. The 3D images were obtained using the mechanical linear scanning approach and have
been oriented and ‘sliced’ to reveal the details of the plaque. (a) Transverse ‘slice’ revealing the
complex plaque. (b) Longitudinal ‘slice’ revealing the extent of the plaque.
and Fenster 1995a, Fenster et al 1995, Guo and Fenster 1996, Dicot et al 1991, 1993, Pretorius
and Nelson 1992), tumour vascularization (Bamber et al 1992, Carson et al 1993, Downey
and Fenster 1995b, King et al 1991), test phantoms (Guo and Fenster 1996, Guo et al 1995,
Dabrowski et al 2001) and power Doppler images (Downey and Fenster 1995a, b, Guo and
Fenster 1996). An example of a linearly scanned 3D ultrasound image made with an external
fixture is given in figure 2, which shows a 3D image of carotid arteries with plaques. In
addition to radiological applications, the utility of linear scanning in echocardiology has also
been demonstrated, using pullback of a transesophageal (TE) probe whose imaging plane
is horizontal (i.e. perpendicular to the probe axis) (Pandian et al 1992, Ross et al 1993,
Wollschlager et al 1993).
Three-dimensional ultrasound imaging
R73
3.1.2. Tilt 3D scanning. In this approach, a motorized drive mechanism is used to tilt a
conventional transducer about an axis parallel to the transducer face while the 2D images are
acquired at regular angular intervals, so that they form a fan of images radial to the axis as
shown in figure 1(b). This type of motion is possible with an integrated 3D probe as well as
with an external fixture. In either case, the probe housing remains at a single location on the
patient’s skin. Because of the fan-like geometry of the acquired 2D images, a large region
of interest can be swept out with an angular separation (typically 0.5◦ to 1.0◦ ) that can be
adjustably predefined to yield high quality images in a variety of applications (Fenster et al
1995, Delabays et al 1995, Downey et al 1995a, b, Gilja et al 1994, Sohn et al 1992).
This approach lends itself to compact designs, both for integrated 3D probes and for
external fixtures. Special 3D ultrasound systems with integrated 3D probes, well-suited for
abdominal and obstetric imaging, have been successfully demonstrated by Kretztechnik (Zipf,
Austria) and Aloka (Korea). This compactness allows for easy hand-held positioning and
manipulation. Also, with suitable a total scanning angle, angular interval and ultrasound
frame rate, the scanning time can be made short. For example, with a total scanning angle
of 90◦ , an angular interval of 1◦ and a 30 Hz acquisition rate, the scanning time is only
3 s.
As with linear scanning, the simple predefined geometry of the 2D image planes allows
for a fast 3D image reconstruction, which can therefore be performed immediately after the
scan is complete. Also, the 3D image resolution will again not be isotropic. Typically, the
resolution will degrade away from the axis of rotation, for two reasons. First, because of
the fan-like geometry of the acquired 2D images, the linear distance between acquired image
planes increases with distance from the axis, resulting in decreased spatial sampling and lower
spatial resolution. Second, because of beam spreading in the elevational (or scan) direction,
as well as within the acquired image plane, the resolution will degrade with distance from the
transducer, which is located near the axis (Blake et al 2000).
In a variation of tilt scanning, an external fixture is used to rotate a cylindrical endocavity
probe, such as a transoesophageal (TE) or transrectal ultrasound (TRUS) probe, about its axis
to perform the scan. The 2D images are acquired by a side-firing linear transducer array parallel
to the probe axis, and they again form a fan of images radial to the axis, as shown in figure 1(c),
which span a total scanning angle of about 80◦ to 110◦ (Tong et al 1996, 1998). This approach
has been successfully applied to prostate imaging (Downey and Fenster 1995a, b, Fenster et al
1995, Tong et al 1996, Elliot et al 1996), as illustrated in figure 3, and to 3D ultrasound guided
cryosurgery (Downey et al 1995c, Chin et al 1998, 1999, Onik et al 1996), and it has been
commercialized by Life Imaging Systems Inc. (London, Canada). Also, an application of this
approach to echocardiography has been demonstrated by TomTec Inc. (Munich, Germany)
using a TE probe whose imaging plane is vertical (i.e. parallel to the probe axis) (Belohlavek
et al 1993, Martin and Bashein 1989).
3.1.3. Rotational 3D scanning. In this approach, a motorized mechanism rotates the
transducer array about a fixed axis that perpendicularly bisects the transducer array through
an angle of at least 180◦ , while the 2D images are acquired. Thus, in this scanning geometry,
the acquired images sweep out a conical volume about the axis, in a propeller-like pattern, as
shown in figure 1(d). This approach has also been implemented with both an integrated 3D
probe and an external fixture (e.g. attached to an end-firing TRUS probe).
As with the other scanning approaches, the 3D image resolution will not be isotropic.
Because the acquired 2D images all intersect along the axis, spatial sampling will be highest
near the axis and poorest away from it. Also, both axial and elevational resolution in the
acquired 2D images will decrease with distance from the transducer. The combination of these
R74
A Fenster et al
(a)
(b)
Figure 3. 3D images of two prostates with cancer. Both images were obtained with the tilt scanning
approach using a side-firing transrectal transducer. The image has been ‘sliced’ to reveal a tumour
(arrow pointing to dark lesion) and its relationship to the seminal vesicle and rectal wall. Image (b)
is courtesy of Life Imaging Systems Inc.
effects will cause the 3D image resolution to vary in a complicated manner, being highest near
the transducer and near the axis, and degrading with distance from either.
This technique is particularly sensitive to operator or patient motion. Because the acquired
2D images all intersect along the rotational axis at the centre of the 3D image, any motion of
the axis or the patient during the scan will cause the axial pixels to have different values in
different 2D images. For example, images acquired 180◦ apart will then not be mirror images
of each other. These inconsistencies will result in image artefacts or resolution loss on the
axis, depending on the method of reconstruction. Similarly, artefacts will occur if the axis of
rotation is not accurately known, or if it is not within the 2D image plane of the transducer.
This approach has successfully been used for transrectal imaging of the prostate and for
endovaginal imaging, as illustrated in figure 4. For imaging the prostate, Downey and others
Three-dimensional ultrasound imaging
R75
Figure 4. 3D image of a pregnant uterus with twins. The image has been ‘sliced’ to reveal the
two gestational sacs. This 3D image was obtained using the rotational scanning approach using an
end-firing endovaginal transducer.
acquired 200 images spanning 200◦ in 13 s (Downey and Fenster 1995b, Tong et al 1996, Elliot
et al 1996). Also, rapid imaging of the heart with a multiplane transoesophageal transducer
has been demonstrated by other investigators (Ghosh et al 1982, McCann et al 1987, Roelandt
et al 1994).
3.2. Free-hand scanning with position sensing
The mechanical scanning approaches described above offer short imaging times, high-quality
3D images and fast reconstruction times. However, the bulkiness and weight of the scanning
apparatus sometimes make it inconvenient to use, and large structures are difficult to scan. To
overcome this problem, free-hand scanning techniques that do not require a motorized fixture
have been developed by many investigators. In these approaches, a sensor is attached to the
transducer to measure its position and orientation. Thus, the transducer can be held by the
operator and be manipulated in the usual manner over the anatomy to be imaged. While the
transducer is being manipulated, the acquired 2D images are stored by a computer together
with their positions and orientations. This information is then used to reconstruct the 3D
image. Since the relative locations of the acquired 2D images are not predefined, the operator
must ensure that the spatial sampling is appropriate and that the set of 2D images has no
significant gaps. Several free-hand scanning approaches have been developed, making use of
four different sensing approaches: articulated arms, acoustic sensing, magnetic field sensing
and image-based sensing.
3.2.1. Articulated arm 3D scanning. Position and orientation sensing can be achieved
by mounting the ultrasound transducer on a multiple-jointed mechanical arm system.
R76
A Fenster et al
Figure 5. Schematic diagram showing two free-hand scanning
approaches: (a) acoustic tracking, (b) magnetic field sensing. The
arrangement of the acquired 2D images has no regular geometry.
For accurate 3D reconstruction, the position and orientation of each
plane must be known accurately.
Potentiometers located at the joints of the movable arms provide the information necessary to
calculate the relative position and orientation of the acquired 2D images. This arrangement
allows the operator to manipulate the transducer while the computer records the 2D images and
the relative angles of all the arms. Using this information, the position and orientation of the
transducer is calculated for each acquired 2D image, allowing the 3D image to be reconstructed.
To avoid 3D image distortions and inaccuracies, the arms must not flex. Improved performance
can be achieved by keeping each arm as short as possible and reducing the number of movable
joints. Thus, increased 3D image quality can be achieved by reducing the scanning flexibility
and the maximum size of the scanned volume (Geiser et al 1982).
3.2.2. Acoustic free-hand 3D scanning. In this approach, an array of three sound-emitting
devices, such as spark gaps, are mounted on the transducer, and an array of fixed microphones
are mounted above the patient, as illustrated in figure 5(a). The microphones continuously
receive sound pulses from the emitters while the transducer is being manipulated and the 2D
images are being acquired. The position and orientation of the transducer for each acquired 2D
image can then be determined from knowledge of the speed of sound in air, the measured timesof-flight from each emitter to each microphone and the fixed locations of the microphones.
The microphones must be placed over the patient, to provide unobstructed lines of sight to
the emitters, and must be close enough that the sound pulses can be detected with a good
signal-to-noise ratio (Brinkley et al 1982, 1984, King et al 1990, 1992, Levine et al 1989,
Moritz et al 1983, Rivera et al 1994, Weiss et al 1983).
3.2.3. Free-hand 3D scanning with magnetic field sensors. The most popular free-hand
scanning approach makes use of a magnetic field sensor with six degrees of freedom. In this
approach, a transmitter is used to produce a spatially varying magnetic field and a small receiver
containing three orthogonal coils senses the magnetic field strength, as illustrated in figure 5(b).
By measuring the strength of three components of the local magnetic field, the position and
orientation of the transducer can be calculated each time a 2D image is acquired. Typically, the
transmitter is placed beside the patient and the receiver is mounted on the hand-held transducer.
Magnetic field sensors are small and unobtrusive, allowing the transducer to be tracked
with less constraint than the previously described approaches. However, electromagnetic
Three-dimensional ultrasound imaging
R77
(a)
(b)
Figure 6. 3D B-mode image of the carotid arteries obtained with the free-hand scanning approach
using a magnetic position and orientation measurement (POM) device. (a) The 3D image has been
‘sliced’ to reveal an atherosclerotic plaque at the entrance of the internal carotid artery. The plaque
is heterogeneous, with calcified regions casting a shadow on the common carotid artery. (b) The
same image has been volume rendered to obtain a view of the vessel wall and the surface of the
plaque.
interference from sources such as CRT monitors, ac power cables and electrical signals from
the transducer itself can compromise the tracking accuracy. Geometric distortions in the final
3D image can occur if ferrous or highly conductive metals are located nearby. In addition,
errors in the determination of the location of the moving transducer will occur if the magnetic
field sampling rate is low and the transducer is moved too quickly. This results in an image ‘lag’
artefact, which can be avoided by using a sampling rate of about 100 Hz or higher. By ensuring
that the environment of the scanned volume is free of metals and electrical interference, and
using an adequate sampling rate, high-quality 3D images can be obtained, as illustrated in
figure 6.
R78
A Fenster et al
Magnetic position and orientation measurement (POM) devices of sufficient quality for 3D
ultrasound imaging are currently being produced by two companies: the Fastrack by Polhemus,
and Flock-of-Birds by Ascension Technologies. These devices have been used successfully in
many diagnostic applications, including echocardiography, obstetrics, and vascular imaging
(Fenster et al 1995, Nelson and Elvins 1993, Bonilla-Musoles et al 1995, Detmer et al 1994,
Ganapathy and Kaufman 1992, Hodges et al 1994, Hughes et al 1996, Leotta et al 1997, Gilja
et al 1997, Nelson and Pretorius 1995, Ohbucki et al 1992, Pretorius and Nelson 1994, Raab
et al 1979, Riccabona et al 1995).
3.2.4. 3D tracking by speckle decorrelation. The free-hand scanning techniques described
above require a POM device to record the position and orientation of each acquired image.
An alternative technique, not requiring any device, uses the acquired images themselves to
extract their relative positions. This can be accomplished using the well-known phenomenon
of speckle decorrelation. When a source of coherent energy interacts with scatterers, the
reflected spatial energy pattern will vary, due to interference, and appear as a speckle pattern.
Ultrasound images are characterized by image speckle, which can be used to make velocity
images of moving blood (Friemel et al 1998). In this situation if the red blood cells remain
stationary then two sequential signals scattered by these cells will be correlated and the speckle
pattern will be identical. However, if the blood cells move, the two sequential signals will
become decorrelated, with the degree of decorrelation being proportional to the distance moved.
The same principle can be used to determine the spacing between two adjacent 2D images.
If two images are acquired from the same location, then the speckle pattern will be the same,
so that there will be no decorrelation. However, if one of the images is moved with respect to
the first, then the degree of decorrelation will be proportional to the distance moved, the exact
relationship depending on the beam width in the direction of the motion (Tuthill et al 1998).
Speckle decorrelation techniques for 3D ultrasound imaging are complicated by the fact
that a pair of adjacent 2D images may not necessarily be parallel to each other. Thus, in
order to determine whether the transducer has been tilted or rotated, the acquired images are
subdivided into smaller regions, and similar regions in adjacent images are cross-correlated.
In this manner, a pattern of decorrelation values is generated, which in turn generates a pattern
of distance vectors. These are then analysed to determine the relative position and orientation
of the two 2D images.
3.3. Free-hand scanning without position sensing
An alternative 3D scanning approach, without any position sensing, involves manipulating
the transducer over the patient while acquiring 2D images, and then reconstructing the 3D
image by assuming a predefined scanning geometry. Because no geometrical information is
recorded during the transducer’s motion, the operator must be careful to move the transducer at
a constant linear or angular velocity so that the acquired 2D images are obtained with a regular
spacing. With uniform motion and knowledge of the approximate total distance or total angle
scanned, very good 3D images may be reconstructed, as illustrated by figure 7 (Downey and
Fenster 1995a). However, because this approach offers no guarantee that the 3D images are
geometrically accurate, they should not be used for any measurements.
3.4. 2D Arrays for dynamic 3D ultrasound
The mechanical and free-hand scanning approaches for 3D ultrasound images described above
all require the 2D ultrasound image produced by a conventional transducer, with a 1D array,
Three-dimensional ultrasound imaging
R79
Figure 7. Maximum intensity projection (MIP) of a 3D power Doppler image of a kidney. The
image was obtained with the free-hand scanning approach without any positioning sensing. The
image has been ‘sliced’ to demonstrate that excellent 3D images of vascular structures can be
obtained.
to be mechanically or manually swept across the anatomy. However, by using a transducer
with a 2D array, the transducer could remain stationary, and electronic scanning could be used
to sweep the broad ultrasound beam over the entire volume under examination. Although
a number of 2D array designs have been developed, the most advanced is the one at Duke
University, which was developed for real-time 3D echocardiography and has been used for
clinical imaging (Shattuck et al 1984, Smith et al 1991, 1992, Snyder et al 1986, Turnbull and
Foster 1991, von Ramm and Smith 1990, von Ramm et al 1991).
In this approach, a 2D phased array of transducer elements is used to transmit a broad beam
of ultrasound diverging away from the array, sweeping out a volume shaped like a truncated
pyramid. The returned echoes detected by the 2D array are processed to display multiple
planes from this volume in real time. These planes can be manipulated interactively to allow
the user to explore the whole volume under investigation. Although this approach has been
successfully used in echocardiology, some problems must be overcome before its use can
become widespread in radiology. These problems are related to high cost of the 2D transducer
arrays, which results from the low yield of the manufacturing process, which requires many
electronic leads to be properly connected to the numerous small elements in the array.
4. Reconstruction of 3D ultrasound images
Image reconstruction refers to the process of generating a 3D representation of the anatomy by
first placing the acquired 2D images in their correct relative positions and orientations in the
3D image volume, and then using their pixel values to determine the voxel values in the 3D
image. Two distinct reconstruction methods have been used: feature-based and voxel-based
reconstruction.
R80
A Fenster et al
4.1. Feature-based reconstruction
In this method, desired features or surfaces of anatomical structures are first determined
and then reconstructed into a 3D image. For example, in echocardiographic or obstetric
imaging, the ventricles or foetal structures may be outlined (either manually or automatically
by computer) in the 2D images, and only the resulting boundary surfaces presented to the viewer
in 3D. The surfaces of different structures may be assigned different colours and shaded, and
some structures may be eliminated to enhance the visibility of others. This approach has
been used extensively in 3D echocardiography to identify the surfaces of ventricles (Ofili and
Nanda 1994, Nadkarni et al 2000a, Coppini et al 1995, Wang et al 1994). Then, using ventricle
surfaces from 3D images obtained at different phases of the cardiac cycle, the complex motion
of the heart can be viewed with a computer workstation, as a cine loop. A similar approach has
been also used to reconstruct 3D images of the vascular lumen from intravascular ultrasound
(IVUS) images (Reid et al 1995).
Because this approach reduces the 3D image content to the surfaces of a few anatomical
structures, the contrast of these structures can be optimized. Also, by using multiple (e.g.
cardiac gated) images, their dynamic behaviour can be easily appreciated. In addition, it
also enables the 3D image to be manipulated efficiently using inexpensive computer display
hardware (e.g. to produce ‘fly-through’ views). However, this approach also has major
disadvantages. Because it represents anatomical structures by simple boundary surfaces,
important image information, such as subtle anatomical features and tissue texture, is lost
at the initial stage of the reconstruction process. Moreover, the artificial contrast between
structures may misrepresent subtle features of the anatomy or pathology. Furthermore, the
boundary identification step is tedious and time-consuming if done manually, and subject to
errors if done automatically by a computer segmentation algorithm.
4.2. Voxel-based reconstruction
The more popular approach to 3D ultrasound image reconstruction is based on using a set
of acquired 2D images to build a voxel-based image, i.e. a regular Cartesian grid of volume
elements in three dimensions. Reconstruction is accomplished in two steps: first, the acquired
images are embedded in the image volume, by placing each image pixel at its correct 3D
coordinates (x, y, z), based on the 2D coordinates (x ∗ , y ∗ ) of that pixel in its 2D image, and
the position and orientation of that image with respect to the 3D coordinate axes. Then, for
each 3D image point, the voxel value (colour or grey-scale image intensity) is calculated by
interpolation, as the weighted average of the pixel values of its nearest neighbours among
the embedded 2D image pixels. For mechanically scanned images, the interpolation weights
may be precomputed and placed in a look-up table, allowing the 3D image to be rapidly
reconstructed (Tong et al 1996).
This voxel-based reconstruction approach preserves all the information originally present
in the acquired 2D images. Thus, by taking suitable cross sections of the 3D image volume,
the original 2D images can be recovered. Moreover, new views not found in the original set
of images can be generated.
However, if the scanning process does not sample the object volume adequately, so that
there are gaps between acquired 2D images which are greater than about half the elevational
resolution, then the interpolated voxel values will not depict the true anatomy in the gap. In
this situation, spurious information is introduced, and the resolution of the reconstructed 3D
image is degraded. To avoid this situation, the volume must be well sampled. This generates
large data files which require efficient 3D viewing software. Typical image files can range
Three-dimensional ultrasound imaging
R81
from 16 MB to 96 MB in size, depending on the application. For example, 96 MB images
have been reported in 3D TRUS imaging of the prostate for cryosurgical guidance (Downey
et al 1995c, Chin et al 1996).
Moreover, because all the original image information is preserved, the 3D image can be
processed repeatedly using a variety of rendering techniques. For example, the operator can
scan through the data (review the complete 3D image) and then choose the technique which
displays the features of interest to best advantage. The operator may then apply segmentation
and classification algorithms to segment boundaries, measure volumes or perform various
volume-based rendering operations. Then, if the selected process does not achieve the desired
results, the operator may return to the original 3D image and try a different procedure.
4.2.1. Freehand 3D scanning. Here it is assumed that the transducer housing is rigidly
attached to the ‘sensor’ of a position and orientation measurement (POM) device (such as a
magnetic field sensor). This device determines the position and orientation of the ‘sensor’
relative to a ‘source’ in a fixed remote location (such as a magnetic field generator). Suppose
now that a 2D image pixel has coordinates (x ∗ , y ∗ ) relative to the transducer, 3D coordinates
(x , y , z ) relative to the ‘sensor’, 3D coordinates (x , y , z ) relative to the ‘source’ and 3D
voxel coordinates (x, y, z) in the reconstructed image volume. Suppose further that the x ∗ –y ∗
plane would have to be sequentially rotated through the three angles (α, β, γ ) about three
(given) axes and translated by the vector (X, Y, Z) in order to coincide with the x –y plane;
that the x –y plane would similarly have to be rotated through (α , β , γ ) and translated by
(X , Y , Z ) in order to coincide with the x –y plane; and, finally, that the x –y plane would
have to be rotated through (α , β , γ ) and translated by (X , Y , Z ) in order to coincide
with the x–y plane. Then the embedding transformation from 2D pixel coordinates (x ∗ , y ∗ )
to 3D voxel coordinates (x, y, z) can be written as the product of three homogeneous linear
transformations, so that:
 
 ∗
x
x
∗
y 
 y 
(1)
  = T T T 
z
0
1
1
where, in terms of the 3D rotation matrix Rij = rij (α, β, γ )


R11 R12 R13 X
R22 R23 Y 
R
(2)
T =  21

R31 R32 R33 Z
0
0
0
1
with similar definitions for T in terms of Rij = rij (α , β , γ ) and T in terms of
Rij = rij (α , β , γ ).
Because T is defined by the user and T is given by the output of the POM device, only
T needs to be determined in order to fully specify the embedding transformation. This is done
by calibrating the rotation angles (α, β, γ ) and the translation vector components (X, Y, Z).
Methods for doing this are described by Detmer et al (1994) and Prager et al (1998).
Using commercially available magnetometer-based POM devices, Prager et al (1998)
found a root-mean-square (rms) precision of 1.1–2.2 mm in each 3D coordinate, depending on
the calibration method, and a mean accuracy of better than 0.25 mm for each of the methods
tested. These are similar to results obtained earlier by Detmer et al (1994) and Leotta et al
(1997) for specific calibration methods. Also, the precision and accuracy of various in vitro
and in vivo volume measurements has been reported by Hodges et al (1994) and Hughes et al
(1996).
R82
A Fenster et al
4.2.2. Mechanical 3D scanning. Here, the transducer housing is rigidly mounted on a
mechanical device, often hand-held, which then uses a stepper motor to scan the object by
translating, rotating or tilting the transducer according to a predetermined protocol, often
under microcomputer control. The embedding transformations for mechanical linear and tilt
scanners are described in connection with the next section.
5. Effects of errors in the reconstruction
Except for systems using 2D arrays, 3D ultrasound images are reconstructed from multiple 2D
images, using knowledge of their relative positions and orientations. Thus, any errors in the
position or orientation of the 2D images will cause geometric distortion in the reconstructed 3D
image, resulting in errors in the measured lengths, areas and volumes of anatomical features in
these images. Although other sources of error such as tissue motion during the scan (possibly
caused by the scanning procedure itself) or image segmentation errors can also affect these
measurements, they overlie this fundamental geometric image distortion. In the literature,
analyses of these geometric distortions have been performed for linear and tilt mechanical
scanners.
5.1. Linear 3D scanning
The analysis was performed by Cardinal et al (2000) for the general case of an ultrasound
transducer oriented as shown in figure 8 and scanned parallel to the z-axis. Here, the transducer
array is initially oriented along the x-axis, and aimed in the y direction. After being tilted by
an angle θ about the x-axis, and then swiveled by an angle φ about the y-axis, it is translated
in the z direction, in steps of size d, to acquire a series of parallel 2D images. Thus, for the 2D
image acquired after n steps, the pixel at coordinates (x ∗ , y ∗ ) has 3D Cartesian coordinates
(x, y, z) given by the linear transformation
∗
x
cos φ
sin φ sin θ 0
x
(3)
y∗ .
y =
0
cos θ
0
n
z
− sin φ cos φ sin θ d
Moreover, the embedded 2D image planes are all normal to the unit vector
U = (sin φ cos θ, − sin θ, cos φ cos θ)
(4)
and have a normal spacing
D = d cos φ cos θ > 0
(5)
◦
assuming (without loss of generality) that θ and φ are each less than π/2 (90 ) in magnitude.
When φ = 0, the embedded 2D image planes are all parallel to the x-axis, and hence
the 3D image interpolation reduces to a 2D interpolation in the y–z plane, which is identical
for each value of x. Thus, when φ = 0, the 3D image may be efficiently reconstructed by
computing a single set of 2D interpolation coefficients and applying it to each x plane in
succession.
The error analysis assumes that the 3D image is reconstructed with the nominal parameter
values (d, θ, φ), but that the 3D object was scanned with the actual parameter values (d0 , θ0 , φ0 ).
Because the embedding transformation is linear, straight lines are always imaged as straight
lines. However, the errors d = d − d0 , θ = θ − θ0 and φ = φ − φ0 still cause relative
errors L/L, A/A and V /V in image length, area and volume. If the error vector E is
now defined as:
θ
d
−φ
,
,
(6)
E=
cos φ cos θ cos θ d cos φ cos θ
Three-dimensional ultrasound imaging
R83
Figure 8. Schematic diagram showing the scanning geometry for linear scanning with a
conventional transducer. The transducer array is initially located on the object’s surface, along
the x-axis, and aimed perpendicularly into the object, in the y direction. The transducer is then
tilted by an angle θ about the x-axis and subsequently swiveled by an angle φ about the y-axis,
before being translated across the object’s surface in the z direction (in steps of size d, not shown)
to acquire a series of parallel 2D images.
then, assuming that |d/d| 1, |θ | 1 and |φ| 1, it is found that all three relative
errors are well described by the linear approximations
K
K = L, A, V
(7)
= E · CK
K
where
L × (U × L)
(U · L)L
CL = U −
=
(8a)
L2
L2
which is the component of U parallel to the image line vector L
A × (U × A )
( U · A )A
CA = U −
=
(8b)
2
A
A2
which is the component of U perpendicular to the image area vector A, and
CV = U .
Hence, because |CK | |U | = 1, within these linear approximations
K K = L, A, V .
K |CK ||E | |E |
(8c)
(9)
For volume, the linear approximation is explicitly
V
d
=
− tan θ θ − tan φφ
(10)
V
d
which is insensitive to φ when φ = 0. Also, the exact ratio of image volume V to object
volume V0 is given by the formula
V
d cos φ cos θ
D
=
=
(11)
d0 cos φ0 cos θ0
D0
V0
because the determinant of the embedding linear transformation is D.
R84
A Fenster et al
Figure 9. Schematic diagram illustrating the scanning geometry
for tilt scanning with a side-firing transrectal ultrasound (TRUS)
probe. The acquired 2D images form a radial fan about the probe
axis, with an inner radius R0 , and spanning a total scan angle .
For the case φ0 ≈ φ = 0, θ0 = 32.7◦ , the exact volume error formula was verified
experimentally, using phantom images, within an experimental uncertainty of less than 0.5%.
Also, it was verified numerically that all three linear approximations are perfectly adequate
for parameter errors of any likely magnitude (Cardinal et al 2000).
5.2. Tilt 3D scanning
The analysis was performed by Tong et al (1998) for the case of a side-firing TRUS probe
rotated about its axis, as shown in figure 9. Here, the scanned images comprise a radial fan
of 2D images, each of size (X, Y ), whose inner edges all lie at a distance R0 from the axis,
which forms the z-axis. By convention, the X and Y directions are respectively chosen to be
parallel (lateral) and perpendicular (axial) to the probe axis. Thus, for the 2D image at angle
θ, the pixel at (x ∗ , y ∗ ) has 3D cylindrical coordinates (r, θ, z) = (R0 + y ∗ , θ, x ∗ ) and hence
3D Cartesian coordinates (x, y, z) = (r cos θ, r sin θ, z).
Hence, in this case, the 3D interpolation reduces to a 2D interpolation in the x–y plane,
which is identical for each value of z. The 3D image may therefore be efficiently reconstructed
by computing a single set of 2D interpolation coefficients and applying it to each z plane in
succession.
The error analysis assumes that the angular interval between acquired 2D images is
uniform, and proportional to the total scan angle (Tong et al 1998). However, it applies
equally to the case where the angular interval is a variable number of steps, provided the step
size remains proportional to .
Errors R0 in R0 and in will cause relative errors L/L, A/A and V /V in image
length, area and volume, as well as a transverse deviation h from linearity in the image of a
line of length L (defined so that h vanishes at the image endpoints), due to the nonlinearity of
the embedding transformation. It is found that all three relative errors, as well as h/L, vary
closely as
K
R0
=P
+Q
K = L, A, V
(12)
K
R
where |P | 1, |Q| 1 and R is the average radius of the object from the axis, which is
usually well approximated by the radius of the object’s centroid. Hence, within these linear
approximations
K R0 +
K = L, A, V .
(13)
K R Three-dimensional ultrasound imaging
R85
For line images, the coefficients P and Q for L/L and h/L are simple functions of the line’s
orientation and size, and the relative location of the point at which h is to be calculated. For
area and volume, P = Q = 1, and the exact ratio of image size K to object size K0 is given
by the formula
K
R0
1+
K = A, V .
(14)
= 1+
K0
R
Moreover, this formula applies to cross-sectional areas in any orientation.
Tong et al (1998) also describe methods for using simple phantoms to calibrate R0 and accurately. Moreover, the linear length, area and volume formulae were verified numerically,
using simulated images, and experimentally, using phantom images (and the calibrated values
of R0 and ), within an experimental uncertainty of less than 0.5%.
6. 3D ultrasound image display
Over the past decade, many algorithms and software utilities have been developed to manipulate
3D images interactively using a variety of visualization tools (Udupa 1999, Udupa et al 1991,
Robb 1995). Although the quality of the 3D image depends critically on the method of image
acquisition and the fidelity of the 3D image reconstruction, the viewing technique used to
display the 3D image often plays a dominant role in determining the information transmitted
to the operator. There are many approaches for displaying 3D images, which differ in the
details of the display implementation. Many of these techniques have been investigated for
use with 3D ultrasound images, and these can be divided into three broad classes: surface
rendering (SR), multiplanar reformatting (MPR) and volume rendering (VR).
6.1. Surface rendering (SR)
A common 3D display technique used in medical imaging is based on visualizing the surfaces
of organs. Because the 3D image must first be reduced to a description of surfaces, image
classification and segmentation steps precede the rendering. In the first step, each voxel (or
voxel group) in the 3D image must be either manually or automatically classified as to which
structure it (or they) belongs (Bezdek et al 1993). Organ boundaries are then identified using
manual contouring (Coppini et al 1995, Neveu et al 1994, Lobregt and Viergever 1995),
or computer-based segmentation techniques (Gill et al 2000). Once the organs have been
classified and segmented, the boundary surfaces are each represented by a mesh and then
texture mapped with a colour and texture which appropriately represents the corresponding
anatomical structure.
Because early 3D ultrasound imaging techniques did not have the benefit of the
high computational speed now available in inexpensive computers, only simple wire-frame
rendering techniques were used at first. These early approaches were used for displaying
the foetus (Brinkley et al 1984, Sohn et al 1989, Sohn and Rudofsky 1989, Brinkley et al
1982), various abdominal structures (Sohn and Grotepass 1989, Sohn et al 1988, Sohn 1989),
and the endocardial and epicardial surfaces of the heart (Moritz et al 1983, Coppini et al
1995, Fine et al 1988, Nixon et al 1983, Martin et al 1990, Linker et al 1986, Sawada et al
1983). More recent approaches use more complex surface representations, which are texturemapped, shaded and illuminated, so that both the surface topography and its 3D geometry
are more easily comprehended. These approaches typically allow user-controlled motion for
viewing the anatomy from various perspectives. This approach has been used successfully
by many investigators in the rendering of 3D echocardiographic (Greenleaf et al 1993, Ofili
R86
A Fenster et al
and Nanda 1994, Ross et al 1993, Delabays et al 1995, Wang et al 1994, McCann et al 1998,
Belohlavek et al 1994, Delabays et al 1995) and obstetric images (Nelson and Pretorius 1992,
Lees 1992).
6.2. Multiplanar reformatting (MPR)
Rather than displaying only the surfaces of structures, as in the SR technique, the MPR
technique provides the viewer with planar cross-sectional images extracted from the 3D image.
Thus, this approach requires that either a voxel-based 3D image be first reconstructed, or that
an algorithm be used that can extract an arbitrarily oriented plane from the original set of
acquired 2D images (Robb 1995). Because the extracted plane will generally not coincide
with one of these original image planes, interpolation is required to provide an image similar
in appearance to an acquired 2D image. Thus, the extracted image resembles a conventional
2D ultrasound image, which is already familiar to the operator; the operator can then easily
position and orient it to the optimal image plane for the examination. This technique is easy to
learn and is therefore preferred by radiologists. Two MPR techniques have been used to view
3D ultrasound images: orthogonal planes and cube-view.
6.2.1. Orthogonal planes. In this approach, computer–user interface tools provide three
perpendicular planes which are displayed on the screen simultaneously, with graphical cues
indicating their relative orientations. The operator can then select any single or multiple planes
and move them within the 3D image volume to provide a cross-sectional view at any desired
location or orientation, including oblique views (Nelson and Pretorius 1992, Zosmer et al
1996, Gerscovich et al 1994, Kuo et al 1992, Kirbach and Whttingham 1994).
6.2.2. Cube-view. In this approach, illustrated by figures 2, 3, 4, 6(a) and 10(a), the 3D image
is presented as a polyhedron which represents the boundaries of the reconstructed volume. Each
face of the multifaceted polyhedron is rendered with the appropriate ultrasound image for that
plane, using a texture-mapping technique (Fenster et al 1995, Tong et al 1996, Robb 1995).
With the computer–user interface tools provided, the operator can select any face, and move
it in or out, parallel to the original face or reorient it obliquely, at any angle to the original
face, while the appropriate ultrasound data are continuously texture-mapped in real time onto
the new face. In addition, the entire polyhedron may be arbitrarily rotated in any direction
to obtain the desired orientation of the 3D image. In this way, the operator always has 3D
image-based cues relating the plane being manipulated to the anatomy being viewed (Rankin
et al 1993, Fenster et al 1995, Picot et al 1993, Blake et al 2000).
6.3. Volume rendering (VR)
The volume rendering technique presents a display of the entire 3D image after it has been
projected onto a 2D plane, with the image projection typically being accomplished via raycasting techniques (Tuy and Tuy 1984, Levoy 1990a, b). The 3D image voxels that intersect
each ray are weighted and summed to achieve the desired result in the rendered image.
Although many VR algorithms have been developed, 3D ultrasound imaging currently makes
use of two main approaches: maximum (minimum) intensity projection and translucency
rendering.
6.3.1. Maximum (minimum) intensity projection (MIP). A common approach is to display
only the maximum (minimum) voxel intensity along each ray. This approach is easy to
Three-dimensional ultrasound imaging
R87
Figure 10. 3D image of a foetal face, displayed using (a) the multiplanar reformatting cube-view
approach, and (b) the translucency rendering approach, done with ray-casting. In (a) the 3D image
is presented as a polyhedron with the appropriate 2D image texture-mapped onto each face. This
polyhedron can then be arbitrarily ‘sliced’ and rotated to reveal the profile of the foetal face. In (b),
the amniotic fluid has been rendered as transparent, and tissues have been rendered as translucent
to opaque, as a function of their voxel intensity.
implement and fast to compute, allowing real-time manipulation of the MIP image on
inexpensive computers. Excellent results are typically achieved when the image information
is sparse, such as it is in 3D power Doppler images (see figure 7) (Guo and Fenster 1996,
Downey and Fenster 1995b).
6.3.2. Translucency rendering. This is the VR approach most commonly used in 3D
ultrasound imaging. In this approach, the accumulated luminance C for a given ray is calculated
by tracing the ray through the 3D image and adding the contribution from each voxel along
the ray. Tracing the ray forwards, towards the projected image plane, the ray-tracing equation
is
C = C[1 − α(i)] + c(i)α(i)
(15)
where C and C are respectively the accumulated luminance not including and including the
contribution from the ith voxel, α(i) is the opacity (1 minus the transmittance) of the ith voxel,
and c(i) is its luminance. The parameters α(i) and c(i) are generally functions of the ith voxel
intensity, which are chosen to control the specific type of rendered image desired. Thus, if
α(i) = 0, the ith voxel is transparent, while if α(i) = 1, then it is opaque or luminescent,
depending on the value of c(i).
However, the contribution of voxels far from the projected image plane is typically
negligible, due to the high total opacity (low total transmittance) of the intervening voxels.
Thus, it is often preferred to trace the ray backwards, away from the projected image plane.
In this case, the ray-tracing equation becomes
C = C + c(i)α(i)[1 − A(i)]
(16)
R88
A Fenster et al
where C and C are respectively the accumulated luminance not including and including the
contribution from the ith voxel (now numbered in reverse order), and A(i) is the accumulated
opacity at the ith voxel, given by
[1 − A(i)] =
[1 − α(j )]
(17)
j <i
so that [1 − A(i)] represents the total transmittance between the ith voxel and the projected
image plane. In practice, the backwards ray-tracing equation is iterated until [1 − A(i)] is less
than some predefined level, at which point C is output as the display luminance.
Because VR preserves much of the information in the 3D image, and projects it onto a 2D
image plane after nonlinear processing, the projected image is, at times, difficult to interpret.
Thus, this approach is best suited for imaging simple anatomical structures, in which clutter
is either absent or has been removed, and to which depth cues and shading have been added.
Excellent results have been achieved in displaying foetal (Nelson and Pretorius 1992, 1995,
Nelson and Elvins 1993, Pretorius and Nelson 1994, 1995, Nelson et al 1996) and vascular
anatomy (Downey and Fenster 1995a, Fine et al 1991), as demonstrated by figure 10(b).
7. Measurement accuracy with 3D ultrasound
An important advantage of 3D ultrasound imaging is the ability to measure the length, area
or volume of organs or lesions in arbitrary orientations. While a qualitative visual assessment
is often valuable, quantitative data provide a more accurate basis for decision making and
comparisons against previous studies or reference data-bases (Elliott et al 1996, Rivera et al
1994, Hodges et al 1994, Hughes et al 1996, Riccabona et al 1995, 1996a, Nadkarni et al
2000a, Favre et al 1995, Gilja et al 1995, Sivan et al 1997).
In many applications, the goal is the measurement of the volume of the organ or lesion,
rather than its diameter or perimeter. Since conventional ultrasound imaging provides only
2D images, volume must be estimated from measurements of diameter in two or three userselected planes and by assuming some idealized shape. This approach results in inaccuracy and
variability. Since 3D ultrasound images provide a complete view of the structure, they should
provide a basis for more accurate and precise estimates of volume, especially for complex
structures.
7.1. Distance measurement
With a 3D image, distance measurements need not be made in the plane of an acquired 2D
image, but instead can be made in any orientation. Thus, for this measurement to be accurate,
the reconstruction process must not distort the anatomical geometry. With proper calibration
and reconstruction, accurate distance measurements can be made.
Tong et al (1996) have evaluated the accuracy of distance measurements using a 3D wire
phantom composed of four layers of surgical wires, with a separation of 10.00 mm between
adjacent wires within each layer, and a separation of 10.00 mm between adjacent layers.
The wire phantom, immersed in a 7% glycerol solution, was imaged with a 3D transrectal
ultrasound (3D TRUS) system, which uses the mechanical tilt-scanning approach described
by figure 1(c) and in more detail by figure 9. The results of this study showed that with proper
calibration of the parameters R0 and , which specify the scanning geometry, as shown in
figure 9, this 3D ultrasound imaging system can provide distance measurements with an error
of less than about 1.0%.
Three-dimensional ultrasound imaging
R89
7.2. Measurement of cross-sectional area using 3D ultrasound
Using the multiplanar reformatting (MPR) technique, the 3D image can be ‘sliced’ in any
orientation, to obtain the optimal cross section for the organ measurement. The desired area
can then be outlined either manually, using planimetric techniques (Tong et al 1996, Elliot
et al 1996), or automatically, using segmentation algorithms (Gill et al 2000, Mao et al 2000,
Ladak et al 2000). The area of the region is then found by counting the pixels in the outlined
region and multiplying by the pixel area.
7.3. Volume measurement using 3D ultrasound
The availability of 3D images allows the measurement of organ volume using either manual or
algorithmic techniques. Automated and semiautomated segmentation algorithms are described
below in section 8, and are still under development in many laboratories. The alternative
manual technique (manual planimetry) makes use of MPR viewing to ‘slice’ the 3D image
into a series of uniformly spaced parallel 2D images. In each 2D image, the cross-sectional
area of the organ is manually outlined on the computer screen, and the areas are then summed
and multiplied by the interslice distance to obtain an estimate of the organ volume (Elliot et al
1996).
7.3.1. In vitro measurements of volume accuracy. Tong et al (1996) used a 3D TRUS system
(described in figures 1(c) and 9) to evaluate the accuracy of volume measurements in vitro.
Five balloons filled with various known volumes of 7% glycerol solution were imaged, and
their volumes were measured using the manual planimetry method. A comparison of measured
to true volumes yielded an rms error of 0.9%.
7.3.2. Volume measurements of prostates in vitro. Rather than using idealized volume shapes
such as balloons, Elliot et al (1996) compared prostate volumes measured using manual
planimetry of 3D TRUS images with their true volumes. For this study, six prostates were
harvested from fresh cadavers, fixed and immersed in a 7% glycerol solution for imaging.
The MPR technique was then used to ‘slice’ the reconstructed prostate images into 20 to 30
transaxial slices 2–5 mm apart, depending on the prostate size. A comparison of measured to
true volumes yielded an rms error of 2.6%.
7.3.3. Volume measurements in vivo. A number of studies have been published demonstrating
that 3D ultrasound can be used to accurately measure volumes for a variety of organs (Nelson
et al 1999). Brunner et al (1995) showed an increased accuracy over 2D techniques in follicular
volume measurements. Chang et al (1997) showed accurate volume measurement results for
foetal heart volumes across a range of gestational ages. Riccabona et al (1996b) showed
accurate volume measurements by comparing voided urine volumes and measured bladder
volumes before voiding. Hughes et al (1996) measured a variety of organ volumes, and found
that the errors ranged from 2% to 6%. Favre et al (1995) reported that in using 3D ultrasound
for estimation of foetal weight by measuring foetal arm and thigh circumference, the mean
error was −1.4% for macrosomic foetuses.
7.3.4. Effect of interslice distance on planimetric volume measurements. Volume
measurement by manual planimetry requires that the 3D image be ‘sliced’ into individual
parallel slices, and the boundary of the anatomical structure be outlined in every slice. Thus,
while a small interslice distance increases accuracy, it also increases the number of slices that
R90
A Fenster et al
must undergo the time-consuming and tedious process of manual planimetry. It is therefore
important to determine the maximum interslice distance that still provides an accurate volume
estimate. Although irregular or complex structures require the use of small interslice distances,
using an interslice distance of less than the elevational resolution will not result in increased
accuracy. Thus, the optimal interslice distance will depend on the elevational resolution of the
transducer and the complexity of the organ.
Elliot et al (1996) have reported on a study examining the effect of the interslice distance
on measurement of prostate volume. In this study, prostate volume was measured in a 3D
TRUS image by usual manual planimetry with interslice distances from 1 mm to 15 mm. The
results showed that the measured prostate volume was constant up to an interslice distance of
8 mm, but underestimated for greater interslice distances. Other reports addressing this issue
have recommended interslice distances ranging from 2 mm to 5 mm for measuring prostate
volume using 2D ultrasound and manual planimetry (Terris and Stamey 1991).
7.3.5. Theoretical variability of planimetric volume measurements. Because volume
measurements in 3D images are usually performed via manual planimetry, Nadkarni et al
(2000b) have derived a simple formula for theoretically estimating the variance of a planimetric
volume measurement, and compared it with experimental variances of volume measurements.
In their approach, the volume variance was estimated by summing the variances of individual
measurements of the cross-sectional area of an object, multiplied by the interslice distance.
For simplicity, it is assumed that n cross-sectional ‘slices’ are taken perpendicular to the axis
of a cylindrically symmetric object of length a. Thus, each ‘slice’ is a circle, with a radius rk ,
k = 1, . . . , n, and an interslice distance x = a/n. Hence, each ‘slice’ is a circular disk of
volume Vk = πrk2 x, k = 1, . . . , n. Then the planimetric estimate of the object volume V is
given by
a
n
n
V =
πr 2 (x) dx ≈
π rk2 x =
Vk .
(18)
0
k=1
k=1
Now, assuming that each radius rk has the same variance σr2 , the variance σk2 in Vk is given by
∂Vk 2 2
2
σr = (2πrk x)2 σr2 = (4π x)Vk σr2 , k = 1, . . . , n.
(19)
σk =
∂rk
Hence, because each slice is measured independently, the variance σV2 in V is given by
n
n
4π aV 2
σV2 =
σk2 = (4πx)
Vk σr2 =
(20)
σr .
n
k=1
k=1
Therefore, the fractional variability of a planimetrically measured volume V can be estimated
as:
4πaσr2
σV
=
.
(21)
V
nV
Thus, the fractional variability in volume measurement using the planimetric method can
be reduced by increasing the number n of independent 2D image ‘slices’ used to make the
measurement. In addition, the fractional variability is proportional to the standard deviation
σr in the radial placement of the boundary contour in each ‘slice’, which depends, in part, on
the image resolution in the 2D image ‘slices’.
As an example, for an object of volume V = 100 cm3 and length a = 5 cm, whose volume
has been planimetrically measured with n = 10 ‘slices’, a distance x = a/n = 5 mm apart,
with a boundary position uncertainty of σr = 1 mm, we estimate from the formula (21) that
the fractional variability in the volume measurement will be 2.5%.
Three-dimensional ultrasound imaging
R91
7.3.6. Intra- and interobserver variability of volume measurements. Volume measurement
with 2D ultrasound requires that the diameter of the organ be measured in at two least images,
and that a particular shape must be assumed. For example, using measurements of the height H ,
width W and length L of a prostate from two orthogonal views, and assuming an ellipsoid shape,
the volume may be estimated from the formula V = (π/6)H W L (Terris and Stamey 1991).
However, organs are not typically well-described by simple geometric shapes, and selection of
the appropriate orthogonal views and chords to measure H , W and L is largely dependent on
observer preference, leading to high intra- and inter-observer variability in volumes estimated
by this 2D method. Because manual planimetry of 3D ultrasound images uses the entire organ
for volume measurement, the intra- and interobserver variability in volumes estimated by this
3D method should be lower.
To compare these two methods, Tong et al (1998) measured their intra- and interobserver
variability. Eight observers (four experienced radiologists and four technicians or graduate
students) measured 15 prostate volumes twice with each method. An analysis of variance
(ANOVA) was then used to determine the intra- and interobserver standard errors of
measurement SEMintra and SEMinter of the prostate volume measurements made using each
method (Eliasziw et al 1994, Mitchell et al 1996).
The ANOVA results shown in table 2 demonstrate that the 3D method reduces SEMintra
by about a factor of three and SEMinter by about a factor of two (Tong et al 1998). Often SEM
values are interpreted in terms of the minimum volume change V detectable at the 95% level
of confidence, which is given by V = 2.77 SEM. These values for V are also given in
table 1. For a single observer, they show that the minimum confidently detectable volume
change is 43% for the 2D method but only 14% for the 3D method.
Table 2. Results of the intra- and interobserver study comparing prostate volume measurements
using 3D ultrasound and the conventional 2D method. V is the minimum relative prostate volume
change that can be detected between successive prostate volume measurements at the 95% level of
confidence (V = 2.77 SEM).
SEM/volume (%)
Intraobserver
Interobserver
V /volume (%)
3D
2D
3D
2D
5.1
11.4
15.5
21.9
14
32
43
61
Also, Chang et al (1997) have compared the reproducibility of the two methods for the
assessment of foetal liver volume, and report that the intra- and interobserver reproducibility
are improved with the 3D method. Their volume measurements had an intraobserver standard
deviation of 8.46 cm3 for the 2D method and 2.15 cm3 for 3D method.
8. Segmentation of 3D ultrasound images
Because the use of manual planimetry for identifying organs and measuring their volumes
is time-consuming and tedious, investigators are developing automated or semiautomated
segmentation techniques. Segmentation is the process of partitioning a digital image into
separate structures. In general, for a technique to be useful, it must be accurate, reproducible
and fast. However, because 3D ultrasound images suffer from artefacts such as shadowing,
speckle and poor contrast, fully automated segmentation may result in unacceptable errors.
Thus, semiautomated approaches may be better, since they allow image-specific initialization
and editing of the results (Ladak et al 2000).
R92
A Fenster et al
Successful segmentation approaches in 3D ultrasound imaging have been based on
deformable models, which make use of geometric objects (1D contours, 2D surfaces or 3D
solids) that deform to fit the desired image features (Lobregt et al 1995, Gill et al 2000, Ladak
et al 2000, McInerney and Terzopoulos 1996). External forces are used to deform the model,
while internal forces within the model provide the desired degree of stiffness, or resistance
to deformation. Gill et al (2000) have used this approach to segment the carotid arteries in
3D B-mode images. Their approach comprises three major steps: (a) interactive placement
of an initial balloon model inside the lumen; (b) automatic inflation of the model towards the
arterial wall and (c) automatic localization of the arterial wall. The model shape is described
by a triangular mesh. The equation of motion for each vertex on the mesh is given by
mi ẍi (t) + vi ẋi (t) + g(xi (t)) = f (xi (t))
(22)
where xi (t), ẋi (t) and ẍi (t) are respectively the vertex 3D position, velocity and acceleration at
time t respectively, mi is its mass, vi is its damping coefficient, g(xi (t)) is the resultant surface
tension at the vertex and f (xi (t)) is a ‘driving’ force. Equilibrium is attained when both ẋi (t)
and ẍi (t) become zero, which can take a very long time. However, by setting mi = 0 and
vi = 1 for the system, the equation of motion reduces to
ẋi (t) = f (xi (t)) − g(xi (t)).
(23)
Equilibrium is then attained when ẋi (t) becomes zero, which can occur quickly, because the
system does not possess inertia. To compute the equilibrium position of each vertex, and hence
the deformed shape of the model, the model is inflated by iteratively updating xi (t) via the
formula
xi (t + t) ≈ xi (t) + ẋi (t)t
(24)
until ẋi (t) is sufficiently small at all vertices. When the model inflation stops, the mesh provides
an approximation of the vessel boundary. At this stage, the ‘driving’ force f (xi (t)) is replaced
by an image-based force fedge (xi (t)), that locally modifies the mesh to conform to fine details
in the 3D image. This force is defined in terms of the potential function P (xi (t)) defined by
(Gill et al 2000):
1
(25)
P (xi (t)) =
|∇(Gσ ∗ I )| + ε
where (Gσ ∗ I ) represents the convolution of a 3D Gaussian smoothing filter of characteristic
width σ with the image intensity I (xi (t)), ∇ denotes the gradient operator and ε is a small
positive constant to prevent division by zero. Minima of the potential function coincide with
the arterial wall. This potential function produces the force field that is used to deform the
model:
fedge (xi (t)) = −kedge ∇P (xi (t)).
(26)
The strength of the force is controlled by the coefficient kedge , the value of which must be
optimized to allow the mesh to conform to the vessel surface.
9. Trends and future developments
Advances in the past 5 years now allow real-time or fast 3D acquisition, real-time
reconstruction, and 3D visualization with real-time image manipulation. The availability
of clinically useful 3D ultrasound systems is allowing users to focus on demonstrating
their clinical utility. Also, advanced visualization and measurement tools are allowing
the examination of complex anatomical structures and the accurate measurement of
Three-dimensional ultrasound imaging
R93
(a)
(b)
Figure 11. 3D image of a prostate after a brachytherapy procedure. The radioactive seeds appear
as bright structures. Panel (a) shows a transverse view from a 3D ultrasound image, and panel (b)
shows the same view from a CT image.
complex volumes. These have been particularly important in obstetrics, cardiology and urology
(Nelson et al 1999, Baba and Jurkovic 1997). Although 3D ultrasound has been shown to be
useful, progress still needs to be made for this technique to become a routine tool. The following
are current trends and needed developments in 3D ultrasonography.
9.1. The use of 3D ultrasound in brachytherapy
Current practice involves acquiring 2D ultrasound images of the prostate at 5 mm intervals.
With these, the dose plan is made and then the radioactive seeds are implanted. The availability
of 3D ultrasonography may allow the development of an intraoperative technique (Blake et al
2000). Using 3D ultrasound, the image of the prostate could be obtained quickly and a dose
plan made with a 3D image of the prostate. After implantation, the brachytherapy seeds
R94
A Fenster et al
could be accurately detected and their locations determined. Views of a patient’s prostate after
brachytherapy seed implantation are shown in figure 11. Although the seeds are difficult to
distinguish, image processing might improve their contrast, which would allow them to be
automatically segmented.
9.2. The need for improved free-hand scanning and 2D arrays
Although free-hand scanning techniques are already in use, they are still subject to potential
artefacts and inaccuracies. Further progress is needed for these techniques to become routine
tools producing high-quality images under all circumstances. In addition, improvements in
2D arrays are still required to produce 3D images whose quality is similar to those currently
being produced with conventional 1D arrays. Nevertheless, 2D arrays producing 3D images
directly are already useful when real-time 3D imaging is required, as in echocardiography.
9.3. The need for improved visualization tools
Current 3D ultrasound imaging systems already make use of real-time visualization tools.
However, these tools are generally difficult to use, and require complicated user interfaces. For
3D ultrasound imaging to become widely accepted, intuitive tools are required to manipulate the
3D image so that the user may view any section of the anatomy in relation to any other sections.
In addition, intuitive real-time tools are needed to produce volume-rendered images. Currently,
the production of volume-rendered images requires multiple parameters to be manipulated.
Techniques are needed that provide immediate optimal rendering, based on both image data
and reference data for the organ being viewed, without significant user intervention.
9.4. Conclusions
Clinical experience with 3D ultrasound has already demonstrated the clear advantages it offers
both for the diagnosis of disease and in providing image guidance for minimally invasive
therapy. Technical advances will improve 3D imaging even further, making it a routine tool.
Although advances are required in both 3D visualization software and hardware, the potential
for innovation is clearly present. Based on the pace of development over the past 5 years, many
major advances can be anticipated over the next 5 years, leading to improvements in existing
applications and to the development of new applications in a wide variety of medical disciplines.
References
Baba K and Jurkovic D 1997 Three-Dimensional Ultrasound in Obstetrics and Gynecology (New York: Parthenon)
Bamber J C, Eckersley R J, Hubregtse P, Bush N L, Bell D S and Crawford D C 1992 Data processing for 3-D
ultrasound visualization of tumour anatomy and blood flow Proc. SPIE 1808 651–63
Belohlavek M, Foley D A, Gerber T C, Greenleaf J F and Seward J B 1994 Three-dimensional reconstruction of color
Doppler jets in the human heart J. Am. Soc. Echocardiogr. 7 553–60
Belohlavek M, Foley D A, Gerber T C, Kinter T M, Greenleaf J F and Seward J B 1993 Three- and four-dimensional
cardiovascular ultrasound imaging: a new era for echocardiography Mayo Clin. Proc. 68 221–40
Bezdek J C, Hall L O and Clarke L P 1993 Review of MR image segmentation techniques using pattern recognition
Med. Phys. 20 1033–48
Blake C, Elliot T, Slomka P, Downey D and Fenster A 2000 Variability and accuracy of measurements of prostate
brachytherapy seed position in vitro using three dimensional ultrasound: an intra- and inter-observer study
Med. Phys. 27 2788–95
Bonilla-Musoles F, Raga F, Osborne N G and Blanes J 1995 Use of three-dimensional ultrasonography for the study
of normal and pathologic morphology of the human embryo and fetus: preliminary report J. Ultrasound Med.
14 757–65
Three-dimensional ultrasound imaging
R95
Brinkley J F, McCallum W D, Muramatsu S K and Liu D Y 1982 Fetal weight estimation from ultrasonic threedimensional head and trunk reconstructions: evaluation in vitro Am. J. Obstet. Gynecol. 144 715–21
——1984 Fetal weight estimation from lengths and volumes found by three-dimensional ultrasonic measurements
J. Ultrasound Med. 3 163–8
Brinkley J F, Muramatsu S K, McCallum W D and Popp R L 1982 In vitro evaluation of an ultrasonic three-dimensional
imaging and volume system Ultrason Imaging 4 126–39
Brunner M, Obruca A, Bauer P and Feichtinger W 1995 Clinical application of volume estimation based on threedimensional ultrasonography Ultrasound Obstet. Gynecol. 6 358–61
Cardinal H N, Gill J and Fenster A 2000 Analysis of geometrical distortion and statistical variance in length, area,
and volume in a linearly scanned 3D ultrasound image IEEE Trans. Med. Imaging 19 632–51
Carson P L, Li X, Pallister J, Moskalik A, Rubin J M and Fowlkes J B 1993 Approximate quantification of detected
fractional blood volume and perfusion from 3-D color flow and Doppler power signal imaging Ultrasonics Symp.
Proc. (Piscataway, NJ: IEEE) pp 1023–6
Chang F M, Hsu K F, Ko H C, Yao B L, Chang C H, Yu C H, Liang R I and Chen H Y 1997 Fetal heart volume
assessment by three-dimensional ultrasound Ultrasound Obstet. Gynecol. 9 42–8
Chin J L, Downey D B, Mulligan M and Fenster A 1998 Three-dimensional transrectal ultrasound guided cryoablation
for localized prostate cancer in nonsurgical candidates: a feasibility study and report of early results J. Urol.
159 910–14
Chin J L, Downey D B, Onik G and Fenster A 1996 Three-dimensional prostate ultrasound and its application to
cryosurgery Tech. Urol. 2 187–93
Chin J L, Downey D B, Tong S, Mclean C A, Elliot T, Fourtier M and Fenster A 1999 Three dimensional transrectal
ultrasound imaging of the prostate: clinical validation Can. J. Urol. 6 720–6
Coppini G, Poli R and Valli G 1995 Recovery of the 3-D shape of the left ventricle from echocardiographic images
IEEE Trans. Med. Imaging 14 301–17
Dabrowski W, Dumore-Buyze J, Cardinal H N and Fenster A 2001 A real vessel phantom for flow imaging: 3D
Doppler ultrasound of steady flow Ultrasound Med. Biol. at press
De Castro S, Yao J and Pandian N G 1998 Three-dimensional echocardiography: clinical relevance and application
Am. J. Cardiol. 81 96G–102G
Delabays A, Pandian N G, Cao Q L, Sugeng L, Marx G, Ludomirski A and Schwartz S L 1995 Transthoracic realtime three-dimensional echocardiography using a fan-like scanning approach for data acquisition: methods,
strengths, problems, and initial clinical experience Echocardiography 12 49–59
Delabays A, Sugeng L, Pandian N G, Hsu T L, Ho S J, Chen C H, Marx G, Schwartz S L and Cao Q L 1995 Dynamic
three-dimensional echocardiographic assessment of intracardiac blood flow jets Am. J. Cardiol. 76 1053–8
Detmer P R, Bashein G, Hodges T, Beach K W, Filer E P, Burns D H and Strandness D E Jr 1994 3D ultrasonic image
feature localization based on magnetic scanhead tracking: in vitro calibration and validation Ultrasound Med.
Biol. 20 923–36
Downey D B, Chin J L and Fenster A 1995c Three-dimensional US-guided cryosurgery Radiology 197(P) 539
Downey D B and Fenster A 1995a Vascular imaging with a three-dimensional power Doppler system Am. J. Roentgenol.
165 665–8
——1995b Three-dimensional power Doppler detection of prostatic cancer Am. J. Roentgenol. 165 741
Downey D B, Nicolle D A and Fenster A 1995a Three-dimensional orbital ultrasonography Can. J. Ophthalmol. 30
395–8
——1995b Three-dimensional ultrasound of the eye Administrative Radiol. J. 14 46–50
Eliasziw M, Young S L, Woodbury M G and Fryday-Field K 1994 Statistical methodology for the concurrent
assessment of interrater and intrarater reliability: using goniometric measurements as an example Phys. Ther. 74
777–88
Elliot T L, Downey D B, Tong S, McLean C A and Fenster A 1996 Accuracy of prostate volume measurements in vitro
using three-dimensional ultrasound Acad. Radiol. 3 401–6
Favre R, Bader A M and Nisand G 1995 Prospective study on fetal weight estimation using limb circumferences
obtained by three-dimensional ultrasound Ultrasound Obstet. Gynecol. 6 140–4
Fenster A 1998 3-D sonography—technical aspects Echoenhancers and Transcranial Color Duplex Sonography
ed U Bogdahn, G Becker and F Schlachetzki (Berlin: Blackwell Science) pp 121–40
Fenster A and Downey D B 1996 3-D ultrasound imaging: A review IEEE Eng. Med. Biol. 15 41–51
Fenster A, Tong S, Sherebrin S, Downey D B and Rankin R N 1995 Three-dimensional ultrasound imaging Proc.
SPIE 2432 176–84
Fine D, Perring S, Herbetko J, Hacking C N, Fleming J S and Dewbury K C 1991 Three-dimensional (3D) ultrasound
imaging of the gallbladder and dilated biliary tree: reconstruction from real-time B-scans Br. J. Radiol. 64
1056–7
R96
A Fenster et al
Fine D G, Sapoznikov D, Mosseri M and Gotsman M S 1988 Three-dimensional echocardiographic reconstruction:
qualitative and quantitative evaluation of ventricular function Comput Methods Programs Biomed. 26 33–43
Fishman E K, Magid D, Ney D R, Chaney E L, Pizer S M, Rosenman J G, Levin D N, Vannier M W, Kuhlman J E
and Robertson D D 1991 Three-dimensional imaging Radiology 181 321–37
Friemel B H, Bohs L N, Nightingale K R and Trahey G E 1998 Speckle decorrelation due to two-dimensional flow
gradients IEEE Trans. Ultrason. Ferroelectr. Frequency Control 45 317–27
Ganapathy U and Kaufman A 1992 3D acquisition and visualization of ultrasound data Proc. SPIE 1808 535–45
Geiser E A, Christie L G Jr, Conetta D A, Conti C R and Gossman G S 1982 A mechanical arm for spatial registration
of two-dimensional echocardiographic sections Cathet. Cardiovasc. Diagn. 8 89–101
Gerscovich E O, Greenspan A, Cronan M S, Karol L A and McGahan J P 1994 Three-dimensional sonographic
evaluation of developmental dysplasia of the hip: preliminary findings Radiology 190 407–10.
Ghosh A, Nanda N C and Maurer G 1982 Three-dimensional reconstruction of echo-cardiographic images using the
rotation method Ultrasound Med. Biol. 8 655–61
Gilja O H, Detmer P R, Jong J M, Leotta D F, Li X N, Beach K W, Martin R and Strandness D E Jr 1997 Intragastric
distribution and gastric emptying assessed by three-dimensional ultrasonography Gastroenterology 113 38–49
Gilja O H, Smievoll A I, Thune N, Matre K, Hausken T, Odegaard S and Berstad A 1995 In vivo comparison of
3D ultrasonography and magnetic resonance imaging in volume estimation of human kidneys Ultrasound Med.
Biol. 21 25–32
Gilja O H, Thune N, Matre K, Hausken T, Odegaard S and Berstad A 1994 In vitro evaluation of three-dimensional
ultrasonography in volume estimation of abdominal organs Ultrasound Med. Biol. 20 157–65
Gill J, Ladak H, Steinman D and Fenster A 2000 Accuracy and variability assessment of semi-automatic technique
for segmentation of the carotid arteries from 3D ultrasound images Med. Phys. 27 1333–42
Greenleaf J F, Belohlavek M, Gerber T C, Foley D A and Seward J B 1993 Multidimensional visualization in
echocardiography: an introduction Mayo Clin. Proc. 68 213–20
Guo Z and Fenster A 1996 Three-dimensional power Doppler imaging: a phantom study to quantify vessel stenosis
Ultrasound Med. Biol. 22 1059–69
Guo Z, Moreau M, Rickey D W, Picot P A and Fenster A 1995 Quantitative investigation of in vitro flow using
three-dimensional colour Doppler ultrasound Ultrasound Med. Biol. 21 807–16
Hodges T C, Detmer P R, Burns D H, Beach K W and Strandness D E Jr 1994 Ultrasonic three-dimensional
reconstruction: in vitro and in vivo volume and area measurement Ultrasound Med. Biol. 20 719–29
Hughes S W, D’Arcy T J, Maxwell D J, Chiu W, Milner A, Saunders J E and Sheppard R J 1996 Volume estimation from
multiplanar 2D ultrasound images using a remote electromagnetic position and orientation sensor Ultrasound
Med. Biol. 22 561–72
King D L, Gopal A S, Sapin P M, Schroder K M and Demaria A N 1993 Three-dimensional echocardiography Am.
J. Card. Imaging 7 209–20
King D L, Harrison MR, King D L Jr, Gopal A S, Kwan O L and DeMaria A N 1992 Ultrasound beam orientation
during standard two-dimensional imaging: assessment by three-dimensional echocardiography J. Am. Soc.
Echocardiogr. 5 569–76
King D L, King D L Jr and Shao M Y 1990 Three-dimensional spatial registration and interactive display of position
and orientation of real-time ultrasound images J. Ultrasound Med. 9 525–32
——1991 Evaluation of in vitro measurement accuracy of a three-dimensional ultrasound scanner J. Ultrasound Med.
10 77–82
Kirbach D and Whittingham T A 1994 3D ultrasound—the kretztechnik voluson approach Eur. J. Ultrasound 1 85–9
Kuo H C, Chang F M, Wu C H, Yao B L and Liu C H 1992 The primary application of three-dimensional
ultrasonography in obstetrics Am. J. Obstet. Gynecol. 166 880–6
Ladak H, Mao F, Wang Y, Downey D B, Steinman D and Fenster A 2000 Prostate boundary segmentation from 2D
ultrasound images Med. Phys. 27 1777–88
Lees W A 1992 3-D ultrasound images optimize fetal review Diagn. Imaging 14 69–73
Leotta D F, Detmer P R and Martin R W 1997 Performance of a miniature magnetic position sensor for threedimensional ultrasound imaging Ultrasound Med. Biol. 23 597–609
Levine R A, Handschumacher M D, Sanfilippo A J, Hagege A A, Harrigan P, Marshall J E and Weyman A E 1989
Three-dimensional echocardiographic reconstruction of the mitral valve, with implications for the diagnosis of
mitral valve prolapse Circulation 80 589–98
Levoy M 1990a Volume rendering, a hybrid ray tracer for rendering polygon and volume data IEEE Comput. Graphics
Appl. 10 33–40
——1990b Efficient ray tracing of volume data ACM Trans. Graphics 9 245–61
Linker D T, Moritz W E and Pearlman A S 1986 A new three-dimensional echocardiographic method of right
ventricular volume measurement: in vitro validation J. Am. Coll. Cardiol. 8 101–6
Three-dimensional ultrasound imaging
R97
Lobregt S and Viergever M A 1995 A discrete dynamic contour model IEEE Trans. Med. Imaging 14 12–24
Mao F, Gill J, Downey D B and Fenster A 2000 Segmentation of carotid artery in ultrasound images: method
development and evaluation technique Med. Phys. 27 1961–70
Martin R W and Bashein G 1989 Measurement of stroke volume with three-dimensional transesophageal ultrasonic
scanning: comparison with thermodilution measurement Anesthesiology 70 470–6
Martin R W, Bashein G, Detmer P R and Moritz W E 1990 Ventricular volume measurement from a multiplanar
transesophageal ultrasonic imaging system: an in vitro study IEEE Trans. Biomed. Eng. 3: 442–9
McCann H A, Chandrasekaran K, Hoffman E A, Sinak L J, Kinter T M and Greenleaf J F 1987 A method for
three-dimensional ultrasonic imaging of the heart in vivo Dynamic Cardiovasc. Imaging 1 97–109
McCann H A, Sharp J C, Kinter T M, McEwan C N, Barillot C and Greenleaf J F 1988 Multidimensional ultrasonic
imaging for cardiology Proc. IEEE 76 1063–73
McInerney T and Terzopoulos D 1996 Deformable models in medical image analysis: a survey Med. Image Anal. 1
91–108
Mitchell J R, Karlik S J, Lee D H, Eliasziw M, Rice G P and Fenster A 1996 The variability of manual and computer
assisted quantification of multiple sclerosis lesion volumes Med. Phys. 23 85–97
Moritz W E, Pearlman A S, McCabe D H, Medema D K, Ainsworth M E and Boles M S 1983 An ultrasonic
technique for imaging the ventricle in three dimensions and calculating its volume IEEE Trans. Biomed. Eng. 30
482–92
Nadkarni S K, Boughner D R, Drangova M and Fenster A 2000a Three-dimensional echocardiography: assessment
of inter- and intra-operator variability and accuracy in the measurement of left ventricular cavity volume and
myocardial mass Phys. Med. Biol. 45 1255–73
——2000b Three-dimensional echocardiography: Assessment of inter- and intra-operator variability and accuracy in
the measurement of left ventricular cavity volumes and myocardial masses Phys. Med. Biol. 45 1255–73
Nelson T R, Downey D B, Pretorius D H and Fenster A 1999 Three-Dimensional Ultrasound (Philadelphia, PA:
Lippincott Williams & Wilkins)
Nelson T R and Elvins T T 1993 Visualization of 3D ultrasound data IEEE Comput. Graphics Appl. 13 50–7
Nelson T R and Pretorius D H 1992 Three-dimensional ultrasound of fetal surface features Ultrasound Obstet. Gynecol.
2 166–74
——1995 Visualization of the fetal thoracic skeleton with three-dimensional sonography: a preliminary report Am.
J. Roentgenol. 164 1485–8
——1998 Three-dimensional ultrasound imaging Ultrasound Med. Biol. 24 1243–70
Nelson T R, Pretorius D H, Sklansky M and Hagen-Ansert S 1996 Three-dimensional echocardiographic evaluation
of fetal heart anatomy and function: acquisition, analysis, and display J. Ultrasound Med. 15 1–9
Neveu M, Faudot D and Derdouri B 1994 Recovery of 3D deformable models from echocardiographic images
Proc. SPIE 2299 367–76
Nixon J V, Saffer S I, Lipscomb K and Blomqvist C G 1983 Three-dimensional echoventriculography Am. Heart. J.
106 435–43
Ofili E O and Nanda N C 1994 Three-dimensional and four-dimensional echocardiography Ultrasound Med. Biol. 20
669–75
Ohbuchi R, Chen D and Fuchs H 1992 Incremental volume reconstruction and rendering for 3D ultrasound imaging
Proc. SPIE 1808 312–23
Onik G M, Downey D B and Fenster A 1996 Three-dimensional sonographically monitored cryosurgery in a prostate
phantom J. Ultrasound Med. 15 267–70
Pandian N G, Nanda N C, Schwartz S L, Fan P, Cao Q L, Sanyal R, Hsu T L, Mumm B, Wollschlager H and
Weintraub A 1992 Three-dimensional and four-dimensional transesophageal echocardiographic imaging of the
heart and aorta in humans using a computed tomographic imaging probe Echocardiography 9 677–87
Picot P A, Rickey D W, Mitchell R, Rankin R N and Fenster A 1991 Three-dimensional colour Doppler imaging of
the carotid artery Proc. SPIE 1444 206–13
——1993 Three-dimensional colour Doppler imaging Ultrasound Med. Biol. 19 95–104
Prager R W, Gee A and Berman L 1998 Stradx: real-time acquisition and visualization of freehand three-dimensional
ultrasound Med. Image Anal. 3 129–40
Pretorius D H and Nelson T R 1994 Prenatal visualization of cranial sutures and fontanelles with three-dimensional
ultrasonography J. Ultrasound Med. 13 871–6
——1995 Fetal face visualization using three-dimensional ultrasonography J. Ultrasound Med. 14 349–56
Pretorius D H, Nelson T R and Jaffe J S 1992 3-dimensional sonographic analysis based on color flow Doppler and
gray scale image data: a preliminary report J. Ultrasound Med. 11 225–32
Raab F H, Blood E B, Steiner T O and Jones H R 1979 Magnetic position and orientation tracking system IEEE Trans.
Aerospace Electron. Syst. 15 709–17
R98
A Fenster et al
Rankin R N, Fenster A, Downey D B, Munk P L, Levin M F and Vellet A D 1993 Three-dimensional sonographic
reconstruction: techniques and diagnostic applications Am. J. Roentgenol. 161 695–702
Reid D B, Douglas M and Diethrich E B 1995 The clinical value of three-dimensional intravascular ultrasound imaging
J. Endovasc. Surg. 2 356–64
Riccabona M, Johnson D, Pretorius D H and Nelson T R 1996 Three dimensional ultrasound: display modalities in
the fetal spine and thorax Eur. J. Radiol. 22 141–5
Riccabona M, Nelson T R and Pretorius D H 1996 Three-dimensional ultrasound: accuracy of distance and volume
measurements Ultrasound Obstet. Gynecol. 7 429–34
Riccabona M, Nelson T R, Pretorius D H and Davidson T E 1995 Distance and volume measurement using threedimensional ultrasonography J. Ultrasound Med. 14 881–6
Rivera J M, Siu S C, Handschumacher M D, Lethor J P, Guerrero J L, Vlahakes G J, Mitchell J D, Weyman A E,
King M E and Levine R A 1994 Three-dimensional reconstruction of ventricular septal defects: validation
studies and in vivo feasibility J. Am. Coll. Cardiol. 23 201–8
Robb R A 1995 Three-dimensional Biomedical Imaging: Principles and Practice (New York: VCH)
Robb R A and Barillot C 1989 Interactive display and analysis of 3-D medical images IEEE Trans. Med. Imaging 8
217–26
Roelandt J R, ten Cate F J, Vletter W B and Taams M A 1994 Ultrasonic dynamic three-dimensional visualization of
the heart with a multiplane transesophageal imaging transducer J. Am. Soc. Echocardiogr. 7 217–29
Ross J J Jr, D’Adamo A J, Karalis D G and Chandrasekaran K 1993 Three-dimensional transesophageal echo imaging
of the descending thoracic aorta Am. J. Cardiol. 71 1000–2
Sawada H, Fujii J, Kato K, Onoe M and Kuno Y 1983 Three dimensional reconstruction of the left ventricle from
multiple cross sectional echocardiograms. Value for measuring left ventricular volume Br. Heart J. 50 438–42
Shattuck D P, Weinshenker M D, Smith S W and von Ramm O T 1984 Explososcan: a parallel processing technique
for high speed ultrasound imaging with linear phased arrays J. Acoust. Soc. Am. 75 1273–82
Sivan E, Chan L, Uerpairojkit B, Chu G P and Reece E A 1997 Growth of the fetal forehead and normative dimensions
developed by three-dimensional ultrasonographic technology J. Ultrasound Med. 16 401–5
Smith W L and Fenster A 2000 Optimum scan spacing for three-dimensional ultrasound by speckle statistics
Ultrasound Med. Biol. 26 551–62
Smith S W, Pavy H G Jr and von Ramm O T 1991 High-speed ultrasound volumetric imaging system. Part I. Transducer
design and beam steering IEEE Trans. Ultrason. Ferroelectr. Frequency Control 38 100–8
Smith S W, Trahey G E and von Ramm O T 1992 Two-dimensional arrays for medical ultrasound Ultrasonic Imaging
14 213–33
Snyder J E, Kisslo J and von Ramm O 1986 Real-time orthogonal mode scanning of the heart. I. System design J. Am.
Coll. Cardiol. 7 1279–85
Sohn C 1989 A new diagnostic technique: three-dimensional ultrasound imaging Ultrasonics International ’89 Conf.
Proc. 1148–53
Sohn C and Grotepass J 1989 Representation tridimensionnelle par ultrason Radiologie J. CEPUR 9 249–53 (in
French)
Sohn C, Grotepass J, Schneider W, Funk A, Sohn G, Jensch P, Fendel H, Ameling W and Jung H 1988 Initial studies
of 3-dimensional imaging using ultrasound Z. Geburtshilfe Perinatol. 192 241–8 (in German)
Sohn C, Grotepass J and Swobodnik W 1989 Possibilities of 3-dimensional ultrasound imaging Ultraschall. Med. 10
307–13 (in German)
Sohn C and Rudofsky G 1989 Die dreidimensionale ultraschalldiagnostik—ein neues verfahren fur die klinische
routine? Ultraschall. Klin. Prax. 4 219–24 (in German)
Sohn C H, Stolz W, Kaufmann M and Bastert G 1992 Three-dimensional ultrasound imaging of benign and malignant
breast tumors—initial clinical experiences Geburts. Frauenheilkd. 52 520–5
Terris M K and Stamey T A 1991 Determination of prostate volume by transrectal ultrasound J. Urol. 145 984–7
Tong S, Cardinal H N, Downey D B and Fenster A 1998 Analysis of linear, area and volume distortion in 3D ultrasound
imaging Ultrasound Med. Biol. 24 355–73
Tong S, Cardinal H N, McLoughlin R F, Downey D B and Fenster A 1998 Intra- and inter-observer variability
and reliability of prostate volume measurement via two-dimensional and three-dimensional ultrasound imaging
Ultrasound Med. Biol. 24 673–81
Tong S, Downey D B, Cardinal H N and Fenster A 1996 A three-dimensional ultrasound prostate imaging system
Ultrasound Med. Biol. 22 735–46
Turnbull D H and Foster F S 1991 Beam steering with pulsed two-dimensional transducer arrays IEEE Trans. Ultrason.
Ferroelectr. Frequency Control 38 320–33
Tuthill T A, Krucker J F, Fowlkes J B and Carson P L 1998 Automated three-dimensional US frame positioning
computed from elevational speckle decorrelation Radiology 209 575–82
Three-dimensional ultrasound imaging
R99
Tuy H K and Tuy L T 1984 Direct 2-D display of 3-D objects IEEE Comput. Graphics Appl. 4 29–33
Udupa J K 1999 Three-dimensional visualization and analysis methodologies: a current perspective Radiographics
19 783–806
Udupa J K, Hung H M and Chuang K S 1991 Surface and volume rendering in three-dimensional imaging: a
comparison J. Digital Imaging 4 159–68
Vannier M W, Marsh J L and Warren J O 1984 Three dimensional CT reconstruction images for craniofacial surgical
planning and evaluation Radiology 150 179–84
von Ramm O T and Smith S W 1990 Real time volumetric ultrasound imaging system Proc. SPIE 1231 15–22
von Ramm O T, Smith S W and Pavy H G J 1991 High-speed ultrasound volumetric imaging system. Part II. Parallel
processing and image display IEEE Trans. Ultrason. Ferroelectr. Frequency Control 38 109–15
Wang X F, Li Z A, Cheng T O, Deng Y B, Zheng L H, Hu G and Lu P 1994 Clinical application of three-dimensional
transesophageal echocardiography Am. Heart. J. 128 380–8
Weiss J L, Eaton L W, Kallman C H and Maughan W L 1983 Accuracy of volume determination by two-dimensional
echocardiography: defining requirements under controlled conditions in the ejecting canine left ventricle
Circulation 67 889–95
Wild J J and Reid J M 1952 Application of echo-ranging techniques to the determination of the structure of biological
tissues Science 115 226–30
Wollschlager H, Zeiher A M, Geibel A, Kasper W and Just H 1993 Transesophageal echo computer tomography of
the heart Cardiac Ultrasound ed J R T C Roelandt et al (London: Churchill Livingstone) pp 181–5
Zosmer N, Jurkovic D, Jauniaux E, Gruboeck K, Lees C and Campbell S 1996 Selection and identification of standard
cardiac views from three-dimensional volume scans of the fetal thorax J. Ultrasound Med. 15 25–32