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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
dosing (e.g., acoustic power, duration, duty cycle, and total number of pulses) had
to be addressed further. This work clearly demonstrated a need for high-precision
dosimetry planning tools based on validated simulation for deep targets in
inhomogeneous tissues.
Some dosimetry work has been done using empirical experimental data analysis
on different tissues. Forexample, Wang et al. [48] demonstrated,with invitro and in
vivo experiments on pig liver, that the HIFU lesion occurs at a so-called “biological
focal region” as a function of acoustic focal region (as measured in water), acoustic
intensity, exposure time, irradiation depth within the tissue or overlying tissues, and
the tissue structure and its functional status.
HIFU applications to brain tumors are especially challenging due to the skull
bone. Recent work on HIFU applications for brain tumor therapy has focused
mainly on trans-skull approaches [8, 9], but HIFU delivery via craniotomy has also
been suggested as an aid to the surgical approach or as a less invasive therapy [10,
11]. In addition to MRI imaging for tumor identification, therapy planning for the
trans-skull approach also involves evaluation of skull bone thickness via CT imag-
ing and calculation of phase correction terms foran array of beamsfor accuratedose
delivery at the target. Additionally, ultrasound imaging is increasingly used in brain
tumor surgeries for intraoperative guidance, and HIFU could be envisioned to treat
the tumor cells remaining in the surgical bed (rather than the applied
chemotherapy).
In summary, recent reports in the literature have addressed HIFU dosimetry and
therapy planning issues. Most advances have been made for HIFU applications to
easily accessible tissues and lesions (e.g., breast and prostate). As discussed earlier, a
significant amount of experimental work has been done in vitro on tissues or in vivo
on exposed organs. However, additional potential for HIFU can be realized with
further development of our understanding of the mechanism of various tissue effects
of HIFU, modeling those effects when HIFU is delivered in vivo through overlying
tissues with blood flow, the availability of such modeling tools, and visualization of
simulation based on patient-specific anatomies. Such an approach will aid in the
development of optimized high-precision HIFU dosimetry and planning strategies
for complex and sensitive applications in cancer, cardiology, and the brain, as well
as emerging areas of targeted delivery or activation of molecules. It also potentially
optimizes HIFU therapy by reducing the guesswork on dosage parameters and
thereby reducing the overall treatment duration and reduced exposure to nontarget
tissues.
13.3 Generalized Strategies for HIFU Therapy Planning
This section describes some generalized strategies and issues to consider when devel-
oping a HIFU therapy planning system. It also presents some approaches to an inte-
grated software platform where diverse imaging, segmentation, HIFU simulation,
visualization, and delivery tools can be implemented and studied further. This
approach is generally similar to that of a sophisticated radiation therapy planning
system (e.g., the Eclipse system from Varian Medical Systems [49]) and would be
significantly beneficial to the HIFU research, industrial, and clinical communities.
354 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
13.3.1 Overview of Generalized Strategy
A complete therapy planning procedure consists of multiple steps. The first step is
pretreatment imaging (MRI, CT, or US). The imaging data are then processed for
segmenting the target and surrounding anatomy (manual or semiautomatic or using
coregistration with a reference dataset) to develop 3-D anatomic models of target
and overlying tissues (voxel and surface models). This information is then used for
ultrasound simulation of beam intensity distribution and HIFU temperature pro-
files. Finally, integrated visualization of 3-D anatomy, ultrasound beam simulation,
and HIFU delivery parameters (e.g., power, duty cycle, and path for multiple foci)
provides an interactive strategy planning tool for studying the approaches of HIFU
delivery. Recent reports describe the use of such an approach for general targets
using MRI images [11] and for specific targets, for example, for prostate cancer
using 3-D US images [50]. Figure 13.2 illustrates an overall approach to HIFU ther-
apy planning simulation for an example case of a through-craniotomy brain tumor
application. (Note that this example is intended to illustrate the steps involved in
generalized HIFU therapy planning system.)
13.3.2 Pretreatment Imaging
The primary purpose of pretreatment imaging is to define the target volume and
surrounding anatomy, especially the overlying tissue layers and critical tissues such
as blood vessel areas to protect from exposure. As discussed in the previous section,
used frequently for diagnosis and full assessment before surgery or treatment.
Often, CT or 3-D US is used for certain applications. Imaging provides a 3-D
dataset with voxel intensities that define tissue properties. It is generally assumed
that the voxel intensities relate to the tissue density and tissue type so that they can
13.3 Generalized Strategies for HIFU Therapy Planning 355
MRI/CT slices
Segmentation
3-D model and
surface generation
HIFU delivery and validation 3-D visualization and
therapy planning
Ultrasound simulation
Figure 13.2 Illustration of overall approach to HIFU therapy planning simulation.
be used for simulation of ultrasound propagation through varying tissue densities or
ultrasound velocities. Imaging techniques such as Doppler, diffusion MRI, and con-
trast imaging can help identify blood vessels, tissue perfusion, and targeted tumor
cells in the region of interest.
13.3.3 Defining the Target and Surrounding Tissues
From each slice of the image dataset, an expert can trace the borders of a target
organ to create a 3-D organ definition. Often, relevant surrounding and overlying
tissues need to be identified and labeled. A manual segmentation approach is very
time consuming, and variability between experts is reported to be up to 15%. A
large amount of literature about the segmentation of the anatomy by different imag-
ing modalities has been published. The challenges in automatic segmentation
include effective representation of the area to be segmented, difficulty in incorporat-
ing variable domain knowledge (underlying anatomy, image processes, and so
forth), and overlapping gray levels and regions. A wide variety of segmentation tech-
niques has been proposed, however, no one standard segmentation technique exists
that can produce satisfactory results for all imaging applications. For an overview of
the fundamentals of medical image segmentation and recent level set–based tech-
niques, refer to [51, 52]. Accurate segmentation is used to derive 3-D models for the
target and relevant surrounding tissues and overlying surface geometry.
Figure 13.3 illustrates an example of the semiautomatic segmentation process
for brain tumor MRI images using a level set–based technique for geometric active
contour algorithms. A user segments an initial slice, and the successive slices are seg-
mented automatically by using parameters calculated from the previous slice. Fully
automatic segmentation is often challenging due to the large variation of the target
356 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
Figure 13.3 Illustration of an example of the semiautomatic segmentation process for brain
tumor MRI images using a level set–based technique for geometric active contour algorithms. The
top panel shows screen shots of the iterative process of active contour evolution and the bottom
panel shows segmentation of successive slices.
shape, intensity, continuity, location, and size and due to the complex anatomic
structures in the neighborhood.
13.3.4 Simulation of HIFU
Information from the 3-D anatomic models of target and overlying tissues (voxel
and surface models) is used for ultrasound simulation of beam intensity distribu-
tion. To quantitatively connect a spatial variation of wave velocity and the resulting
beam amplitude distortion, computational models are employed, as discussed in the
previous section. Figures 13.4 and 13.5 show examples of computed and experi-
mentally measured wavefield distortion due to tissue inhomogeneity. The ultra-
sound beam in a homogeneous medium is shown. An explicit random specification
of acoustic velocity, in which increasing pixel brightness indicates increasing veloc-
ity, is also shown. Velocity values are randomly specified to vary 4% about the
mean. Examples of ultrasound beam distortion due to this and similar realizations
of 3-D random media are shown. These distortions are seen to be substantial, but
their significance for high-intensity focused beams remains to be seen. The simula-
tion tools allow for multilayered 3-D tissue configurations with inhomogeneity in
velocity values and could be used for more realistic scenarios derived from imaging
of the structure of the tissues.
13.3 Generalized Strategies for HIFU Therapy Planning 357
2.2 inch diameter
2.5 inch focus
1 inch diameter
2.5 inch focus
Liver: 1548 m/s
Muscle: 1585 m/s
0% inhomogeneity 1% inhomogeneity 3% inhomogeneity
4cm
4cm
4cm
2cm
2cm
Figure 13.4 An example of computational results of the impact on phase aberration through
overlying inhomogeneous tissue layers. The impact increases as the variation within tissues
increases, which results in blurry and shifted energy focus. (From: [39]. © 2005 American Institute
of Physics. Reprinted with permission.)
From the segmented target volume and ultrasound beam intensity distributions,
the BHTE is applied with given tissue parameters to calculate the temperature rise
for a given HIFU exposure duration. The rate of acoustic energy absorption within
tissue is tightly coupled to the temperature rise and development of oscillating
microbubbles that formwithin the tissue [46]. Computing the impact of HIFU is fur-
ther confounded by phenomena resulting from 3-D structures within the tissue such
as acoustic streaming in the blood and back scattering [38]. Additionally, tissue
changes (e.g., boiling) alter the characteristics of the energy absorption. Recent
efforts attempt to account for the added terms of the BHTE including the volumetric
heat rate generated by the presence of microbubbles [41] and the blood perfusion
losses [53]. Basic principles of cavitation in therapeutic ultrasound have recently
been actively studied [54, 55].
13.3.5 Integration and Visualization
Recent advances in computer graphics capabilities allow for decent visualization of
2-D and 3-D datasets on a desktop computer. A variety of computational visualiza-
tion methods are used in medical applications; [51] provides an overview of many
such techniques. For visualization, ray casting [56] is most commonly used in vol-
ume rendering, but a 3-D texture mapping technique makes the rendering real time,
which is usually impossible with ray casting. Advanced computer graphics tech-
niques are often used to take full advantage of a modern video card’s high speed and
programmable capability. For example, a preintegrated volume rendering technique
[57] improves the rendering quality without sacrificing the rendering performance.
Also, issues such as rendering multivolume data (e.g., voxels from both imaging and
simulation data) need to be considered carefully. The visualization is not limited to
volume data themselves. Many other 2-D or 3-D features can be rendered simulta
-
358 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
Liver
(2 cm)
Muscle
(1 cm)
No
tissue
Before focus At focus Beyond focus
Figure 13.5 Experimental results of mapping ultrasound beam cross sections in low intensity in
water with and without intervening tissue layers (1-cm layer of bovine muscle and 2-cm layer of
bovine liver). The significant effect of phase aberration, caused by tissue inhomogeneity, is
observed as predicted by the simulation. (From: [39]. © 2005 American Instititue of Physics.
Reprinted with permission.)
neously, and that works well along with the partial exposure techniques [58]. Dur-
ing implementation, special care is taken to set rendering states and decide the
rendering order for different features.
Figures 13.6, 13.7, 13.8, and 13.9 show some examples of visualization tech-
niques. Using such visualization tools, the user can register and overlay the HIFU
simulation results on top of the 3-D medical images, and interactively change the
parameters and see the results.
13.3.6 Planning of HIFU Delivery and Control
HIFU treatment options can be explored interactively once the target geometry and
HIFU-related simulation data are precalculated for given ranges of transducer posi-
tion and orientation, HIFU durations, frequency, and power. It may be beneficial if
simulation of an imaging modality (e.g., MRI or ultrasound technique to be used for
guidance during HIFU delivery) can be developed and incorporated in the planning
tool. If accurate, this can provide training for delivery control and guidance.
13.3.7 Overall Software Design and Integration of Tools
Figure 13.10 shows the overall approach and system architecture for the HIFU ther-
apy planning software. Each module surrounding the control center incorporates
one or more individual tools and communicates with the control center. This
approach allows implementation of any individual tool in any programming lan-
guage that can be plugged in dynamically. This software is being developed using
Visual Studio.Net, OpenGL, ITK, and MATLAB programming environments on a
Windows platform.
13.3 Generalized Strategies for HIFU Therapy Planning 359
Figure 13.6 Screen capture for integrated visualization tools, showing segmented brain tumor
inside a rendered volume. (From: [11]. © 2005 American Institute of Physics. Reprinted with per
-
mission.)
As with all software-implemented clinical techniques, the user interface design is
crucial to successful implementation of HIFU planning tools. Using a Windows plat
-
form and Visual Studio.Net technologies, an integrated environment is developed
360 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
Figure 13.7 Examples of advanced visualization techniques. Traditional cube-shaped cutout for
visualizing (left panel), sphere-shaped cutaway view similar to a large craniotomy (middle panel),
and simultaneous rendering of multiple features including volume, cutaway object profile, and
transfer functions (right panel).
Figure 13.8 Examples of visualization with simultaneous rendering of multiple features. Volume
and cutaway object profile (left panel); volume, cutaway object profile, and bounding box (middle
panel); volume, cutaway object profile, transfer functions, and histogram (right panel). (From: [58].
© 2006 SPIE. Reprinted with permission.)
Figure 13.9 Examples of advanced visualization techniques. Real-time, view-oriented 3-D
texture-based volume rendering using graphics Shader Language.
that has a very flexible and user-friendly graphics user interface (Figure 13.11). Dif-
ferent panels/windows can be freely rearranged according to the user’s preference.
Multiple visualization windows can be opened simultaneously, and various 2-D or
3-D visualization windows can be arranged side by side for better comparison. The
integrated system also makes communication among different tools smoother, user
friendly, and resource efficient.
13.3 Generalized Strategies for HIFU Therapy Planning 361
Image
preprocessing
GUIManager
IOManager
VisManager
HIFU
simulation
Image
processing
Control center
3-D tumor and
tissue model
Other visualization
Simulation
Vol rendering
Volume loader
Dicom loader
Image loader
Tool boxes
Output
Volume explorer
Property editor
IVisualization
Interactive
parameter selection
Ultrasound
intensity model
Heat deposited
(BHTE)
Figure 13.10 Overview of architecture for general-purpose HIFU therapy planning software.
Segmentation window Visualization window
Data
manager
Property
grid
Visualization
toolbox
Output
window
Tool
window
Figure 13.11 Screen capture of the integrated software showing multiple panels including seg
-
mentation and visualization.
The current software functionalities include loading and viewing DICOM files
and relevant header information, segmentation, HIFU simulation using an ultra-
sound beam model for layered tissues and coupling with BHTE, and visualization of
3-D datasets. The segmentation capabilities for research include multiple algorithms
for segmentation, interactive change of parameters, monitoring the active contours
during iterative procedures, manual segmentation, and an XML database for
retrieval and comparisons.
An ultrasound model [39] is integrated with this system to allow HIFU dose and
temperature estimations for patient-specific target anatomies. Additional models
discussed in the previous section are being studied for implementation and the soft-
ware architecture allows such modular implementation.
13.3.8 Validation and Clinical Use of the HIFU Therapy Planning System
The utility of any therapy planning and simulation tools must be validated with
HIFU experiments initially on simple layered tissue structures, later with more com-
plex shapes, and finally with in vivo animal models. For successful clinical applica-
tion, the sources of uncertainties in treatment planning must be evaluated. These
include patient localization, patient and organ motion during delivery, positioning
and immobilization, reproducibility, HIFU effects on imaging, localization of target
structures, quality assurance of data transfers from planning system to delivery sys-
tem, and quality assurance of delivery equipment.
13.4 Challenges and Work Ahead
There has been good progress in developing tools for HIFU therapy planning. Fur-
ther work must include in vivo experimental validation of computational models,
understanding of the HIFU mechanism with a wider range of transducer design, fre-
quency, power and duty cycle, validation of the use of an imaging modality for plan-
ning and guidance, incorporation of therapy optimization, control and feedback in
simulation, assessment and use of blood flow and tissue perfusion, integration of
planning and delivery, robotic control, and training and qualification ofthe experts.
Validated computational tools will need to be implemented as software tools to
demonstrate the feasibility of pretreatment planning and a dosimetry approach for
optimizing HIFU delivery to the target tissues for various clinical problems. Finally,
user-friendly software implementation of validated computational models must be
made available for use by the larger research community. Results from computa-
tional models and in vitro and in vivo experiments will provide a strong basis for
HIFU dosimetry and treatment planning. These developments along with robust
image guidance will help optimize HIFU treatment outcomes, for example, by mak-
ing the procedure faster or better controlled, and will potentially allow for treat-
ments in targets that are currently difficult, such as moving organs and targets
behind bone.
362 HIFU Therapy Planning Using Pretreatment Imaging and Simulation
Acknowledgments
The author would like to acknowledge the efforts of graduate students and col-
leagues who have helped through discussions or have worked on different aspects of
HIFU research, some of which is presented in this chapter: Liangshou Wu, Tao
Long, Ronald Roberts, R. Bruce Thompson, Matt Hagge, Mark Bryden, Timothy
Ryken, Scott McClure, and Shahram Vaezy. The author would also like to
acknowledge the financial support in part by a research grant from the Roy J.
Carver Charitable Trust and the Institute of Physical Research and Technology at
Iowa State University. (Note: The content of this chapter represents the opinions
and interpretations of the author and does not necessarily reflect the position of any
colleagues or funding source.)
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Acknowledgments 363