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[41] Sochen, N., R. Kimmel, and R. Malladi, “A Geometrical Framework for Low Level
Vision,” IEEE Trans. Image Processing, Vol. 7, 1998, pp. 310–318.
[42] Sapiro, G., “Color Snakes,” Computer Vision and Image Understanding, Vol. 68, 1997,
pp. 247–253.
[43] Caselles, V., et al., “Fixed Dimensional Object Modeling Via Minimal Surfaces,” Proc.
European Conf. Comput. Vision, 1996, pp. 97–106.
[44] Kimmel, R., A. Amir, and A. M. Bruckstein, “Finding Shortest Paths on Surfaces Using
Level Sets Propagation,” IEEE Trans. on Pattern Anal. Machine Intell., Vol. 17, 1995,
pp. 635–640.
[45] DeCarlo D., and J. Gallier, “Topological Evolution of Surfaces,” Graphics Interface, 1996,
pp. 194–203.
[46] Telea, A., “An Image Inpainting Technique Based on the FastMarching Method,” Graphics
Tools, Vol. 9, 2004, pp. 23–24.
[47] Suri, J. S., “Two Dimensional Fast MR Brain Segmentation Using a Region Based Level Set
Approach,” Int. J. Eng. Medicine Biol., Vol. 20, 2001.
[48] Kass, M., A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” Inter. J.
Computer Vision, 1988, pp. 321–331.
[49] Caselles, V., et al., “A Geometric Model for Active Contours in Image Processing,” Numer.
Math., Vol. 66, 1993, pp. 1–31.
[50] Malladi, R., J. A. Sethian, and B. C. Vemuri, “Evolutionary Fronts for Topology-Independ-
ent Shape Modeling and Recovery,” Third European Conf. on Computer Vision, Lecture
Notes in Computer Science, Vol. 800, 1994, pp. 3–13.
[51] Caselles, V., R. Kimmel, and G. Sapiro, “Geodesic Active Contours,” International Journal
of Computer Vision, Vol. 22, 1997, pp. 61–79.
[52] Suri, J. S., “Leaking Prevention inFast Level Sets Using Fuzzy Models: AnApplication in Mr
Brain,” Proc. Int. Conf. Inform. Technol. Biomedicine, 2000, pp. 220–226.
[53] Baillard, C., P. Hellier, and C. Barillot, “Segmentation of 3-D Brain Structures Using Level
Sets,” IRISA, Res. Rep., November 2000.
[54] Leventon, M. E., W. L. Grimson, and O. Faugeras, “Statistical Shape Influence in Geodesic
Active Contours,” Proc. of the IEEE Conference Computer Vision and Pattern Recogni-
tion, Vol. 1, 2000, pp. 316–323.
[55] Cootes, T. F., et al., “Active Shape Models—Their Training and Application,” Comput.
Vision Image Understand., Vol. 61, 1995, pp. 38–59.
[56] Leventon, M. E., et al., “Level Set Based Segmentation with Intensity and Curvature Pri-
ors,” Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, July
2000, pp. 1121–1124.
[57] Zeng, X., et al., “Segmentation and Measurement of the Cortex from 3-D Mr Images Using
Coupled Surfaces Propagation,” IEEE Trans. on Medical Imaging, Vol. 18, September
1999, pp. 927–937.
[58] Mumford, D., and J. Shah, “Optimal Approximations by Piecewise Smooth Functions and
Associated Variational Problems,” Communications on Pure and Applied Mathematics,
Vol. XLII, pp. 577–685, 1989.
[59] Chan, T. F., and L. A. Vese, “Active Contours Without Edges,” IEEE Trans. on Image Pro-
cessing, Vol. 10, 2001, pp. 266–277.
[60] Wang, Y., and L. H.Staib, “BoundaryFinding with Correspondence Using Statistical Shape
Models,” Computer Vision and Pattern Recognition, 1998, pp. 338–345.
[61] S. C. Mitchell, S. C., et al., “3-D Active Appearance Models: Segmentation of Cardiac MR
and Ultrasound Images,” IEEE Trans. on Medical Imaging, Vol. 21, No. 9, 2002,
pp. 1167–1178.
[62] Cootes, T. F., et al., “Active Appearance Models,” IEEE Trans. on Pattern Analysis and
Machine Intelligence, Vol. 23, 2000, pp. 681–685.
344 Prostate Analysis Using Ultrasound Imaging
[63] Malladi, R., and J. A. Sethian, “Image Processing Via Level Set Curvature Flow,” Proc.
National Academy of Sciences, Vol. 92, July 1995, pp. 7046–7050.
[64] Riedmiller, M., and H. Braun, “A Direct Adaptive Method for Faster Backpropagation
Learning: The Rprop Algorithm,” Proc. IEEE Inter. Conf. on Neural Networks, 1993,
pp. 586–591.
[65] Cremers, D., S. J. Osher, and S. Soatto, “Kernel Density Estimation and Intrinsic Alignment
for Knowledge-Driven Segmentation: Teaching Level Sets to Walk,” Pattern Recognition,
Vol. 3175, 2004, pp. 36–44.
[66] Igel, C., M. Toussaint, and W. Weishui, “Rprop Using the Natural Gradient Compared to
Levenberg-Marquardt Optimization,” 4th Inter. Meeting on Constructive Approximation
(IDoMAT 2004), 2004.
[67] Weiss, S. M., and C. A. Kulikowski, Computer Systems That Learn: Classification and Pre-
diction Methods for Statistics, Neural Nets, Machine Learning, and Expert Systems, San
Francisco, CA: Morgan Kaufmann, 1991.
[68] Efron, B., and R. J. Tibshirani, An Introduction to the Bootstrap, London, U.K.: Chapman
& Hall, 1993.
[69] Plutowski, M., S. Sakata, and H. White, “Cross-Validation Estimates Integrated Mean
Squared Error,” in Advances in Neural Information Processing Systems, J. Cowain, G.
Tesaulo, and J. Alspector, (eds.), San Francisco, CA: Morgan Kaufmann, 1994,
pp. 1135–1142.
[70] Shao, J., and D. Tu, The Jackknife and Bootstrap, New York: Springer-Verlag, 1995.
12.6 Conclusions and Future Work 345
PART VI
Application of 3-D Therapeutic and
Image Guidance Ultrasound
CHAPTER 13
HIFU Therapy Planning Using
Pretreatment Imaging and Simulation
Viren R. Amin
13.1 Introduction
High-intensity focused ultrasound (HIFU) is being studied by many research groups
as a potential noninvasive therapy for many clinical problems that otherwise
require surgery or other approaches. The HIFU technology delivers high levels of
ultrasound energy (from 1,000 to 10,000 W/cm
2
at the focal spot compared to less
than 0.1 W/cm
2
for diagnostic ultrasound), focused to a tissue of interest, raising the
tissue temperature to greater than 50°C within seconds. Maintaining this tempera-
ture for 1 to 3 seconds results in cell death, and a single exposure can destroy up to
0.5 mL volume of tissue at focus without significantly damaging the surrounding
tissues. The effects on the tissue at focus for varying energy levels and exposure
times include protein denaturation, coagulation and hemostasis, cavitation, and tis-
sue ablation. The HIFU works similar to using a magnifying glass to concentrate the
sun’s energy to burn a hole in a dry leaf. Just as the hole in the leaf occurs only when
it is at the focus of the magnifying glass, the HIFU also delivers maximum concen-
tration of energy at its focus without significant damage to the overlaying tissues.
Thus, HIFU is emerging as an exciting new therapy option that is trackless, blood-
less, and portable without the need for general anesthesia. It can be used for many
clinical applications including cancer tumors, hemostasis, and cardiology that oth-
erwise require surgery or other approaches. Figure 13.1 shows examples of HIFU
lesions created in a phantom, chicken breast, and pork chop.
Although promising, HIFU is currently limited by issues such as lesion size, its
single exposure, the time required between exposures to allow local tissue cooling,
the inability to treat through air or bone, difficulty monitoring the therapy in real
time, and the inability to plan the dosimetry before therapy. Technological
advances, such as array beam generators and image-based guidance, promise to
overcome many of these problems. The HIFU dosimetry and pretherapy planning
require tools to define the HIFU beam through multiple layers of inhomogeneous
tissues and tissue lesion generation. Such tools are prerequisites to sophisticated
treatment planning using pretherapy imaging (e.g., CT, MRI, or US), similar to
computational dosimetry and pretreatment planning for radiation therapy.
349
HIFU application to tissueablation requireshigh precisionand tools fordosime-
try therapy planning, and real-time feedback of the intended and actual target loca-
tions. Pretreatment planning is an important step for a successful HIFU therapy
outcome. Typically, the therapy planning approach involves the use of pretreatment
imaging data, defining the target and surrounding tissues by manual or semiauto-
matic segmentation, development of a 3-D anatomy model of the region of interest
from segmentation or registration with a reference dataset, simulation of the HIFU
beam and thermal dosimetry around the target tissue, display and 3-D visualization
of imaging and simulation data, and review of the treatment plan options. Recent
developments in therapy planning using imaging are targeted for specific applica-
tions such as prostate cancer using 3-D ultrasound images and uterine fibroids using
MRI. However, significant developments have been accomplished in image
guidance and feedback during the delivery of HIFU treatments.
This chapter reviews recent work toward therapy planning and presents
approaches for developing strategies for HIFU therapy. It describes general and
target-specific techniques and software tools for HIFU treatment planning using
pretherapy imaging (e.g., MRI, CT, or US) similar to dosimetry and planning for
radiation therapy. An understanding of this subject matter aids development of opti-
mized, high-precision HIFU dosimetry and patient-specific planning strategies for
applications such as tumor ablation. It also potentially reduces the guesswork on
dosage parameters and thereby reduces the overall treatment duration and exposure
to nontarget tissues.
350 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
(c) (d)
(a) (b)
Figure 13.1 Examples of HIFU effects on tissue-mimicking phantom, chicken breast, and pork
chop. (a) Localized HIFU lesion inside a translucent tissue-mimicking phantom. (b) An example of
HIFU lesions in chicken breast using a 1.561-MHz HIFU transducer driven with continuous sinusoi-
dal waves for durations of 60 (left lesion) and 45 (right lesion) seconds. (c) An example of HIFU
lesion in pork chop using 60 seconds of HIFU exposure. (d) Same as (c) but with 30-second dura-
tion.
13.2 Recent Developments in HIFU and Therapy Planning
The progress on HIFU and current research focus can be summarized into five cate-
gories: (1) in vitro and in vivo study of HIFU applications to specific problems and
organs, (2) therapy guidance technology development (e.g., US and MRI guidance
of HIFU therapy), (3) HIFU instrumentation (transducer and equipment), (4) theo-
retical and experimental study of mechanism of tissue effects by HIFU, and (5)
therapy planning.
13.2.1 HIFU Applications
Many HIFU applications have been studied and reported in the recent literature.
Some examples include the following:
•
Ablation of cancer tumors in the prostate [1, 2], uterine fibroids [3], breast
[4], liver [5], and rectum [6]. An overview of 5-year clinical trials in 1,038
patients with solid carcinoma in China is reported in [7].
•
Brain tumor ablation via the trans-skull approach [8, 9] and via the
craniotomy approach [10, 11] as an aid to surgery or as a less invasive sur-
gery.
•
An overview of the scientific and engineering advances that are allowing the
growth in clinical focused ultrasound applications is presented by Clement
[12].
•
Homeostasis of blood vessels[13], parenchyma of liver [14], and spleen [15].
•
Heart diseases such as revascularization of myocardium [16], hypertrophic
cardiomyopathy [17], and creating communication in the atria septum [18].
•
Blood–brain barrier opening [19] and fetal tissues [20].
13.2.2 HIFU Therapy Guidance
Image-based guidance andmonitoring ofHIFU treatments has been addressed signifi-
cantly in recent reports. Techniques based on real-time MRI, US, and CT have been
developed to monitor tissue and temperature changes during HIFU application. The
MRI thermometry relies on proton resonance frequency shift methods to characterize
tissue damage and temperature around the HIFU focus [4, 21]. A general method for
temperature control based on Fourier transformation of the bioheat equation is pro-
posed that takes into account heat diffusion, energy absorption, and temperature dis-
tribution derived from rapid continuous MRI temperature mapping [22].
McDannold et al. [23] demonstrated the needand usefulness of quantitative tempera-
ture mapping during HIFU surgery by analyzing MRI-based temperature imaging
data from the treatments of 62 fibroids in 50 women to test theability of thermal dose
maps acquired during treatment and to predict the resulting nonperfused area in con-
trast-enhanced images acquired immediately after treatment.
Examples of ultrasound-based HIFU monitoring techniques include real-time
visualization using gated US imaging [24], elastography for better detection of coag-
ulated tissue damage [2], US-stimulated acoustic emission or vibroacoustography
13.2 Recent Developments in HIFU and Therapy Planning 351
for localizing temperature elevation [25], differential attenuation imaging to charac-
terize HIFU-induced lesions [26], US phase-contrast transmission imaging [27, 28],
and radiation-force imaging by means of echo-shift estimations [29].
Additionally, CT imaging has also been proposed to monitor and guide HIFU
treatment [30]; however, MRI and US have been increasingly used for monitoring
HIFU therapy in research and early clinical trials. When possible, MRI thermometry
is considered the gold standard for quantifying temperature changes during HIFU
exposure in clinical settings.
13.2.3 HIFU Transducers and Instrumentation
Recent developments in HIFU transducers and instrumentation include an array
transducer [31], solid aluminum cone surgical applicator [32], split-focus trans-
ducer [33], parabolic reflection device for mechanically variable focal lengths [34],
HIFU synchronization with ultrasound imaging [24], and robotics for HIFU deliv-
ery [35, 36]. Similarly, different tissue effects are observed by different shapes and
duty cycles of the signal driving the transducers. Such developments allow for vari-
ous applications of HIFU but create challenges when modeling the HIFU beam and
its effects on tissues.
13.2.4 Theoretical and Experimental Study of the HIFU Mechanism
The biological effects of HIFU have been studied since the 1950s [37]. HIFU tissue
damage and ablation occurs primarily via two mechanisms: thermal energy absorp-
tion and cavitation and its related phenomena. Traditionally, the thermal mode has
been used in understanding lesion size and shape predicted by models. With cavita-
tion, a series of bioeffects can occur. These include shock wave propagation, bubble
generation, higher absorption and a higher bulk temperature near the focus point,
higher scattering that blocks ultrasound propagation, and subsequent power
deposition.
Nonlinear effects in focused ultrasound beams are of importance for the intensi-
ties used intherapeutic applications [38]. However, linear wave propagation models
are commonly applied to study the effects of overlying tissues (in which the field
intensities are substantially lower than those in the focal zone) on the HIFU focus.
Such models help determine the probability density function for the distribution of
absorbed energy and the resulting temperature rise in the target tissue at the focus
point. Experimental and computational studies to examine the impact of phase
aberration through overlying inhomogeneous tissues on ultrasound beam intensity
distribution have been reported [39]. The effect of inhomogeneous overlying tissues
on the heating patterns and steady-state temperature distributions have been studied
analytically using a random-phase screen model [40]. These model findings
suggested the presence of many hot and cold spots throughout the medium.
Recent work has focused on understanding this complex interacting nonlinear
phenomena at the very high intensities present at the focus point, evolution of cavi-
tations and bubbles, increased focal heating from nonlinearity, and tissue lesion for-
mation [41–43]. Curiel et al. [44] adapted the bioheat transfer equation (BHTE) to
take into account the activity of cavitation bubbles generated during HIFU expo
-
352 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
sure. This modeling was used to predict the lesions produced by three different
transducer geometries, and such predictions matched experimental results signifi-
cantly better than the BHTE without bubble effects [45].
Several researchers have worked on the problem of computationally modeling
the nonlinearly coupled equation set describing the interaction of tissue with HIFU.
Curra et al. [38] provided a summary of these modeling efforts. This is a challenging
computational problem complicated by the inability to directly measure the impact
of the acoustic wave on tissue during exposure. Because all direct measurement
techniques (e.g., thermocouples) alter the impact of the acoustic wave, measure-
ments are done outside the area of greatest interest or are implied after the fact by
observing the tissue damage done. In addition, the speckled distribution of acoustic
energy, the formation of bubbles, and the energy absorption rate are nonlinearly
coupled together [38]. One group of researchers has taken the first steps toward
addressing these phenomena and used the results to “optimize the firing session
parameters for dynamically focused HIFU treatment” [46, 47]. The researchers
noted several weaknesses in the models relating to nonlinear coupling and tempera-
ture effects, and the density of the bubbles. In spite of this, reasonable estimates of
tissue were made, and the researchers felt that additional work was warranted to
develop a model-based approach to HIFU treatments.
Deposition of energy within tissue and the subsequent heat transfer within the
tissue are complex and time-consuming computational problems. A complete
model requires coupling of the equation set for the sound propagation model with
the heat transfer equations within the tissue and blood. Simultaneous determination
of multiple parameters is a challenging inverse problem because of a discontinuous
search space and the nonlinear nature of the problem. Mahoney et al. [45] proposed
a method that uses actual measured ultrasound field distributions in combination
with backward projection to calculate the temperature elevation and potentially
improve treatment planning.
13.2.5 HIFU Dosimetry and Therapy Planning
A systematic approach for developing pretreatment planning and HIFU dose calcu-
lations for specific target locations using simulations and experimental validation is
important. A thorough understanding of the HIFU lesion mechanism, target identi-
fication, tools for imaging and visualization, and validated modeling tools are pre-
requisites to sophisticated treatment planning, similar to dosimetry and planning
for radiation therapy. This approach will help HIFU technology to achieve its full
potential and high precision for emerging complex applications.
Huber et al. [4] reported possibly the first MRI-guided HIFU in a malignant
tumor in humans. The main objective of the study was to integrate MRI thermome-
try (phase imaging to estimate the temperature-dependent, proton-resonant fre-
quency shift) to guide the HIFU treatment (as a neoadjuvant approach preceding
open surgical removal) of a well-circumscribed invasive ductal carcinoma of the
breast. Although the therapy regimen used was sufficient to homogeneously induce
tumor cell death, they concluded that a tighter scanning pattern of the ultrasound
foci, higher intensity, or longer sonication per pulse might enhance the instanta
-
neous antitumor effect in breast cancer. They suggested that the issue of optimal
13.2 Recent Developments in HIFU and Therapy Planning 353