bluuebunny/crossref_metadata_2025 huggingface dataset:
MO‐E‐224C‐03: A 3D Collision Avoidance Tool for External Beam Radiation Therapy Treatment Planning
Authors: Hamza‐Lup F , Davis L , Meeks S , Zeidan O
Published: July 2006

Purpose: We are presenting the design and implementation of a 3D‐graphical tool for the detection of potential collisions of various linac components for patient‐specific external treatment planning Method and Materials: The graphical tool uses the Virtual Reality Modeling Language (VRML) to model the exact geometry of any treatment machine by reading its manufacturer's CAD design files. The robust system is based on VRML and Java programming that allows for accurate simulation of any linac hardware module based on the manufacturer's CAD drawings. Results: The tool predicted eminent collisions between different linac components graphically for a simulated Varian 2100EX for certain gantry and table angles. The collision angles were verified manually on the linac and found to agree with the predicted angles from the tool. Conclusions: We have developed a 3D graphical simulation tool that can be used as a stand alone application to assist in external treatment planning by visually simulating collisions between various linac hardware components. Unlike other anti‐collision methods developed so far in the literature, our tool would be able to model the details of the treatment linac and add‐on devices for patient‐specific setups. Hence, the tool will create patient‐specific realistic collision maps for any external treatment scenario. The tool can be used as a stand‐alone program and it is platform independent.
MO‐E‐224C‐04: Motorized Multi‐Leaf Collimator for Electrons: Measurements with a Prototype and Monte Carlo Simulations
Authors: Tremblay H , Bouchard H , Mercure S , Robichaud J , Beausoleil M , Charette M , Carrier J
Published: July 2006

Purpose: To develop a motorized multi‐leaf collimator for electrons (eMLC) and to compare preliminary measurements to Monte Carlo simulations. Method and Materials: An eMLC has been developed. It is the first prototype with fully motorized capabilities. The eMLC is remotely controlled by the operator using home brewed and fully graphical software running on a Windows workstation. The control workstation is connected to the eMLC's custom‐built electronic controller which keeps track of the component states and executes various low‐level commands, including leaf displacement orders. There are 36 independent brass leaves on each side of the eMLC and the maximum size of the generated field is 25.2 × 19.5 cm2. The interleaf distance is less than 0.03 mm. The eMLC prototype is an add‐on device for the Elekta Precise linac (Elekta Ltd., England). The actual distance from the source to the bottom of the leaves is 95.3 cm. The eMLC has been modeled using the BEAMnrc Monte Carlo toolkit and every possible leaf positions is reproducible with our Monte Carlo model. Results: Various preliminary measurements were performed: open field, closed field, one leaf profile, interleaf leakage, leaf transmission, and comparison with conventional custom cutouts. For all measurements, comparison to Monte Carlo simulations are carried out. The transmission through the leaves is 2.4% for a 12 MeV field at the surface of a water phantom with SSD=100 cm. The interleaf leakage is negligible as no interleaf pattern is detected on a closed field profile for all the investigated energies (up to 12 MeV). Conclusion: The motorized eMLC prototype is versatile and easy to operate with a computer control from outside the treatment room. Possible applications of the eMLC go from simply replacing the conventional custom cutouts to complex usages like MERT or electron arc therapy.

Research sponsored by Elekta Ltd.
MO‐E‐224C‐07: Time‐Resolved 4D Dynamic Arc Therapy
Authors: Yang Y , Yue N , Fu W , Li X , Heron D , Huq M , Xing L
Published: July 2006

Purpose: To develop a 4D dynamic arc therapy with the capability of MLC‐shaped irradiation field tracking for moving tumor targets. Method and Materials: 4D CT images at 10 different breathing phases were acquired and transferred to a treatment planning system. The tumor target was contoured and a 3D conformal arc therapy (3DCAT) plan was generated for each phase. For each 3DCAT plan, the selected control points (gantry angles) and the MLC‐defined conformal apertures were correlated to the patient's respiration phases. A program was developed to obtain a 4D DMLC leaf sequence with the capability of tracking the target motion based on the ten 3DCAT DMLC leaf sequence files. To evaluate the 4D plan, a deformable registration was adopted to combine dose distributions and DVHs and the results were compared with those obtained using the conventional 3D and gating plans. Five lung cancer cases and film measurements embedded in a moving phantom were used to investigate the feasibility of the proposed technique. Results: An efficient 4D dynamic arc therapy was implemented. Experimental measurements indicated that the dose distribution in the moving target delivered using the proposed technique is equivalent to that in a static target delivered using a conventional 3D arc therapy. Compared with the 3D plans, the target received more conformal doses and the sensitive structures, especially the lung, were better spared in the 4D plans for all the test cases. The treatment time for the 4D plan is comparable to the 3DCAT plan, much more efficient than the gating treatment. Conclusion: It is feasible to incorporate the intra‐fraction organ motion into a 4D dynamic arc treatment planning to track the moving targets. Compared with conventional treatment strategies, this technique has great potentials to provide more conformal dose distribution and better sensitive structure sparing with extremely high efficiency.
MO‐E‐224A‐02: Stereotactic Body Radiation Therapy: (1) Highlights of the AAPM Task Group Report 101 On SBRT
Authors: Benedict S
Published: July 2006

The approved charges for the AAPM Task Group 101 entitled, “Stereotactic Body Radiation Therapy”, include: Charge (1): To review the literature and identify the range of historical experiences, reported clinical findings and expected outcomes; Charge (2): To review the relevant commercial products and associated clinical findings for an assessment of system capabilities, technology limitations, and patient related expectations and outcomes; Charge (3): Determine required criteria for setting‐up and establishing an ESRT facility, including protocols, equipment, resources, and QA procedures; Charge (4): Develop consistent documentation for prescribing, reporting, and recording ESRT treatment delivery. This part of the presentation will include highlights from the current TG101 report currently under review, including strategies and requirements for patient immobilization and repositioning, respiratory management, treatment planning, and reporting.
MO‐E‐224A‐03: Stereotactic Body Radiation Therapy: (3) Clinical Experience at MSKCC
Authors: Lovelock D
Published: July 2006

Stereotactic Body Radiotherapy (SBRT) ‐ Clinical Experience from MSKCC SBRT has the potential to maximize the benefit of highly conformal dose delivery. New imaging capabilities and the improved precision of treatment machines is making possible the delivery of high doses to targets while minimizing dose to normal tissue and sparing nearby critical structures. As a result, new clinical techniques are being established to treat disease in soft tissue, such as metastatic disease in liver, lung, and lymph nodes and also to deliver very high doses to bony targets such as the spine. Both hypo‐fractionated and single fraction strategies are used.

The clinical procedures used to treat these sites at Memorial Sloan‐Kettering Cancer Center will be described, along with some preliminary outcomes.
TU‐A‐330D‐1: PET/CT Attenuation Correction and Image Fusion
Authors: Patton J
Published: July 2006

In order for an annihilation event to be accurately detected in a PET scanner, it is necessary for both annihilation photons to pass through the patient without interaction and be detected by two detectors in the scanner. The probability of this occurrence is less than that for a single photon, resulting in significant attenuation effects in a PET scan. In the past, a positron source, such as Ge‐68, was used as a transmission source to provide a transmission map to be used for attenuation correction. The recent integration of PET scanners with state‐of‐the‐art multi‐slice CT scanners has provided the capability for using the CT scan as a high‐quality attenuation map to accomplish the task of attenuation correction. This has resulted in an improvement in the quality of the correction and thus an improvement in overall PET image quality. However this implementation has not been accomplished without additional problems being identified. Specifically beam hardening can cause errors in the attenuation correction and accurate corrections are difficult in regions where there is physiological motion such as the heart and lungs due to difficulties in accurately registered the CT and PET scans in these areas. Close attenuation must be paid to these effects.

Integration of the CT scanner with the PET scanner has provided a significant advance in clinical diagnosis and treatment planning because of the ability to accurately register and display high quality images of anatomy from CT and images of organ function from PET. This makes it possible not only to differentiate malignant from benign lesions but to also precisely localize malignant lesions and differentiate between abnormal uptake and normal physiological uptake of radiopharmaceuticals.
TU‐A‐ValB‐01: Multi‐Slice CT Artifacts and Quality Control
Authors: Cody D
Published: July 2006

This presentation will focus on examples and causes of artifacts produced by modern multi‐slice CT scanners. Many of these artifacts were recognized because of a stringent quality control program implemented in our institution. The design of this quality control program will be described, along with its relative costs and benefits. Extending such a program among scanners produced by different vendors will be discussed.

Learning Objectives:

1. To recognize basic artifacts in patient and phantom images generated by modern multi‐slice CT scanners.

2. To understand some basic requirements of a quality control program for multi‐slice CT scanners.
TU‐A‐230A‐01: Monte Carlo II: MC for Source and Machine Characterization
Authors: Chetty I , Siebers J
Published: July 2006

Monte Carlo algorithms have the ability to accurately model dose distributions for arbitrarily complex treatment delivery scenarios and changing patient geometries. These properties make Monte Carlo ideal for clinical planning, or for benchmarking clinical planning systems. Building on the discussion of the role of Monte Carlo in treatment head simulation (from the first course), this second Monte Carlo course will focus on patient planning applications. The goal of the course is to familiarize clinical medical physicists with the use of Monte Carlo in treatment planning, including advanced treatment techniques such as IMRT and motion compensated 4D treatment, and to discuss how Monte Carlo‐based planning differs from planning with conventional algorithms. We will demonstrate the various approaches used in modeling beam modifying devices, such as the multi‐leaf collimator (MLC), including methods to effectively model the dosimetric consequences of the detailed the MLC geometry. We will review methods used to reduce Monte Carlo‐based clinical dose calculation time, such as variance reduction techniques, and discuss how factors specific to the MC method, such as statistical uncertainties, impact dose distributions and clinical decision making. We will illustrate how a properly benchmarked Monte Carlo algorithm can play a unique role in the modeling of complex delivery procedures, such as IMRT and how Monte Carlo fluence prediction can be combined with analytic dose algorithms to estimate patient dose. Finally, we will show how Monte Carlo can include organ motion in 4D dose calculations without an increase in computation time. In addition to illustrating the role of Monte Carlo in complex treatment planning, a focus of this course will also be to review some of the practical issues associated with the implementation, verification and clinical use of Monte Carlo‐based dose calculation algorithms.

Educational Objectives:

1. To familiarize clinical physicists with some of the issues associated with implementation and experimental verification of Monte Carlo‐based dose algorithms in the clinical setting.

2. To understand the factors specific to Monte Carlo‐based dose algorithms, such variance reduction techniques and statistical uncertainties.

3. To become familiar with the various approaches used in Monte Carlo‐based modeling of the MLC and the tradeoffs associated with these methods.

4. To recognize the potential clinical outcome benefits of Monte Carlo‐based dose distributions.

5. To understand the use, benefits and limitations of the Monte Carlo method for IMRT optimization and QA.

6. To become familiar with the role of the Monte Carlo method in motion compensated (4D) treatment planning.
TU‐B‐330A‐01: Shielding Design Workshop: R/F Rooms
Authors: Martin M , Archer B
Published: July 2006

The application of the structural shielding design techniques and goals as outlined in NCRP Report 147: Structural Shielding Design for Medical X‐ray Imaging Facilities (2004) will be the basis for this practical course. The wide variety of facilities installing medical imaging equipment requires the medical physicist to consider an array of radiation protection concerns for the installation of radiographic/fluoroscopic equipment. To meet the challenge of maintaining construction costs to a minimum while providing adequate radiation shielding protection requires the physicist to utilize all available materials to reduce radiation exposure to surrounding personnel and the public. Estimating future workloads as well as considering current workloads for radiographic/fluoroscopic equipment as the medical imaging community transitions from a film/screen based world to a digital world can present challenges. Practical examples of these methods of structural shielding designs will be explored in this course.

Educational Objectives:

1. Understand the radiation exposure protection limits for surrounding areas of radiographic/fluoroscopic installations.

2. Understand the effectiveness of various shielding materials found in facilities to provide required structural shielding necessary to reduce anticipated radiation exposure levels to acceptable limits.

3. Understand the calculation of anticipated workloads for radiographic/fluoroscopic equipment and the effect of these workloads on structural shielding evaluations.
TU‐B‐ValB‐01: The Principles of Quantitative MRI
Authors: Clarke GD
Published: July 2006

The applications of magnetic resonance imaging (MRI) in biomedicine are undergoing rapid evolution. Typically used to produce images that are viewed and subjectively rated by a radiologist, MRI is now being utilized as a scientific apparatus capable of making noninvasive measurements in living tissues. With care, a significant number of physical and biological measurements can be performed and related to individual pixels and groups of pixels in the MR image. This presentation will address the challenges in obtaining quantitative data from MRI.

The presentation will review the principles of good practice in quantification, including quality assurance, MR data collection, and analysis. Limits on precision and accuracy are discussed and solutions proposed. Three major measurement topics are considered. First, geometrical quantities, such size, position and grouping of structures are discussed. Then, MRI signal‐derived quantities such as proton density and relaxation times are considered. Third, physiological quantities such as diffusion coefficients and measures of blood flow and perfusion are explored. The principles underlying the measurement of each quantity are given along with their biological and medical significance and practical approaches for their measurement. Shortcomings of the measurement processes and a summary of potential clinical applications are also discussed. Pathological observations are also compared with MRI‐derived quantities where appropriate.

This presentation is intended as an introduction to the field of measurement in MRI for anyone who desires to use the scope of modern measurement techniques to quantitatively determine the consequences of disease, its development or its reaction to therapy from MR images. It will be of interest to medical physicists who are considering undertaking quantitative work with MRI, as well as those already in the field.

At the end of this session the attentive participant shall:

1. Appreciate the clinically important quantities that can be measured with MRI.

2. Be familiar with the methods and techniques used for quantitative MRI.

3. Have a basic understanding of the limits on precision and sources of error in quantitative MRI.
TU‐B‐224C‐01: 4D Scanning
Authors: Jiang S
Published: July 2006

The purpose of CT simulation in radiotherapy is to acquire patient geometrical information and to build a patient geometrical model for treatment planning. Errors in patient model caused by motion artifacts will influence all treatment fractions and therefore should be handled carefully. Due to the tumor respiratory motion, the captured tumor position and shape can be heavily distorted. The distortions along the axis of motion could result in either a lengthening or shortening of the target. The center of the imaged target can be displaced by as much as the amplitude of the motion.

A newly developed technique that can reduce motion artifacts and provide patient geometry throughout the whole breathing cycle is called respiration‐correlated or 4D CT scan. The basic idea for 4D CT scan is that, at every position of interest along patient's long axis, images are over‐sampled and each image is tagged with breathing phase information. After the scan is done, images are sorted based on the corresponding breathing phase signals. Thus, many 3D CT sets are obtained, each corresponding to a particular breathing phase, and together constitutes a 4D CT set that covers that the whole breathing cycle. 4D CT scan has been developed at various institutions with slightly different flavors. In this lecture, we will provide an overview of various implementations of 4D CT scan.

4D CT scan can be used to account for respiratory motion to generate images with less distortion than 3D CT scan. 4D images also contain respiratory motion information of tumor and organs that is not available in a 3D CT image. This technology can be used for respiratory‐gated treatment to identify the patient‐specific phase of minimum tumor motion, determine residual tumor motion within the gate interval, and compare treatment plans at different phases. It can also be used for non‐gated treatment planning to define ITV by combining gross tumor volume at all breathing phases or using a method called maximum intensity projection. Of course 4D CT will also play a vital role in the futuristic 4D radiotherapy where the tumor is tracked dynamically during the treatment using multi‐leaf collimator.

Existing problems for 4D CT scan include the increased imaging dose, CT tube heating, and data management. More importantly, one has to keep in mind that 4D CT scan is not really 4D. Temporal information is mapped into one breathing cycle. Irregular respiration will cause artifacts in 4D CT images. Patient coaching can improve the regularity of breathing pattern and thus reduce the residual artifacts. However this issue still deserves further studies.

Educational Objectives:

1. Understand the origin and magnitude of motion artifacts in free breathing helical CT scan.

2. Understand how 4D CT scan works.

3. Understand how 4D CT can be used in radiotherapy.

4. Understand the remaining artifacts in 4D CT scan and possible future improvements.
TU‐B‐230A‐01: Informatics Systems Overview
Authors: Dahl R , Herman M
Published: July 2006

Due to the increasing complexity of radiation therapy, government regulations, and legal liability, computerized radiation oncology information systems are becoming a necessity. Selection of an information system involves understanding of both computer software and hardware issues. Topics such as network infrastructure, software interfaces, and hardware interfaces, which are not part of the normal physics training, must be understood by the medical physicist. As the person with the most technical training in the radiation oncology department they will be called upon to do one or more of the following; specify a system, setup and installation, troubleshoot the system when things go wrong. This course will identify both hardware and software issues to consider when either first implementing a computerized information system or changing to an electronic treatment record. An overview/summary of the commercially available Record & Verify systems will also be presented.

Educational Objectives:

1. Understand basic network infrastructure for both local area networks (LAN) and wide area networks (WAN).

2. Understand interfaces to both hospital information systems and various radiation oncology devices.

3. Understand differences in network requirements for both single department and multi‐department institutions.

4. Understand what is required when migrating from a paper treatment record to an electronic treatment record.

5. Be able to generate specifications for a radiation oncology information system.

6. Understand the personnel requirements for implementing and maintaining a radiation oncology information system.

Conflict of Interest Statement

Michael Herman — Research sponsored by Varian Medical Systems Corporation.
TU‐C‐330A‐01: The History (and Future) of MRI Physics
Authors: Roberts T
Published: July 2006

The history of MRI is now a little over 30 years old. It's impact on the scientific, and in particular medical, community has been immense, and shows no sign of slowing. From the first demonstration in 1973 of the spatial encoding of proton density information in the nuclear magnetic resonance signal, via the application of pulsed magnetic field gradients, a succession of technological developments now makes MRI the modality of choice for a wide range of cross sectional biomedical imaging applications. This talk will offer some insights into the origins of MRI, the rapid development of spin echo and gradient recalled echo, 2D and 3D, imaging and the birth of faster and faster imaging involving multiple echoes (RARE and echo planar imaging). Additionally, interpretation of MRI‐accessible contrasts emerged, offering specific insights into physiology via NMR relaxation time constants and such MRI “flavors” as diffusion weighted imaging. In parallel, MRI‐specific contrast media (“magnetic dyes”) were introduced in the 1980's offering both “enhancement” of MRI signal as well as the opportunity to study dynamic processes (such as bolus tracer passage, with concomitant estimation of perfusion and, later, microvascular permeability). The 1990's saw the continued clinical adoption of MRI as well as the commitment to physiologically‐specific imaging and the introduction of blood oxygenation level dependent (BOLD) contrast and the birth of “functional magnetic resonance imaging, fMRI”, for the spatial mapping of brain functional organization. More recently yet further acceleration in image acquisition speed has been offered by the introduction of multiple (parallel) receiver coil elements and the adoption of the principles of sensitivity encoding. Presently, acceleration factors of ×2 are routine, ×4 are commonplace and ×9 or more are in development. The future offers the possibility of massively parallel acquisitions with effectively single‐shot temporal resolution of the order of milliseconds. While the contrast resolution of MRI is transitioning from anatomy through physiology towards biochemical processes, the speed of MRI is accelerating from static to real‐time, hurdling successive physical and physiological boundaries (such, as involuntary motion, breath‐hold, cardiac cycle and ultimately towards the speed of neuronal processes). The overall vision of this talk is to convey the ongoing development of MRI towards both increasing spatial and temporal resolution as well as towards increasingly specific biological interpretation.

Educational Objectives:

1. To understand the origins and background of today's MRI.

2. To understand the rapid development of MRI acquisition speed.

3. To understand the use of tailored MRI to derive physiologically specific image contrast.

4. To anticipate the continued acceleration of both imaging speed and biological application of MRI.
TU‐C‐330A‐02: Patterns of Brain Tumor Recurrence Predicted From DTI Tractography
Authors: Krishnan A , Asher I , Davis D , Fuller D , Okunieff P , O'Dell W
Published: July 2006

Purpose: Approximately 170,000 new cases of brain metastases and 17,000 cases of primary brain cancer are diagnosed in the United States each year. Stereotactic Radiotherapy (SRT) is fast becoming the method of choice for treatment of non‐superficial brain lesions. SRT treatment plans of malignant and metastatic brain tumors typically incorporate a 2 cm margin to account for microscopic tumor spread, however, distant and/or recurrent tumors sometimes occur. Our hypothesis is that paths of elevated water diffusion may provide a preferred route for transport or migration of cancer cells through an unknown mechanism. If our hypothesis is correct then future SRT treatment would be modified to provide elongated treatment margins along the paths of elevated water diffusion leading from the primary tumor site; thereby reducing the incidence of recurrence and improving clinical outcomes. Method and Materials: MR diffusion tensor imaging datasets were acquired in patient subjects treated with SRT of malignant and/or metastatic brain tumors. DTI was performed using an EPI sequence on a 1.5T clinical GE scanner with 20 serial axial images of voxel dimensions 0.976×0.976×6 mm; TR 10s; TE 89.4 ms; 25 diffusion gradient directions plus 3 reference (b=0) scans. Following SRT, patients were given repeated MRI follow‐ups at regular intervals to identify early tumor recurrence. When recurrent tumors were detected, DTIstudio and FSL software was used to compute paths of preferred water diffusion through the primary tumor site and the site of recurrence. Results: There exists an apparent correlation between patterns of recurrence in the brain and paths of elevated diffusion leading from the primary brain tumor. Conclusions: Our preliminary results on a small number of patient datasets suggest that this hypothesis is correct and further investigation is warranted. Future work will employ a more sophisticated fiber analysis on additional patient images and verification with animal models.
TU‐C‐330D‐02: Digital Breast Tomosynthesis
Authors: Maidment A
Published: July 2006

Both film‐screen and digital mammography are subject to a number of fundamental limitations related to the projection process, whereby 2D images are produced of the 3D breast anatomy. Mammography can produce artifactual densities from the superposition of normal tissues that are separated in space; although only visible in a single view, these often appear sufficiently suspicious to necessitate a biopsy, leading to a loss in specificity. Furthermore, true lesions in mammograms can be masked by superimposed normal tissue and thereby rendered undetectable; this reduces the sensitivity of mammography.

Numerous tomographic methods have been proposed to overcome these limitations, including digital breast tomosynthesis (DBT). DBT is a tomographic imaging technique in which a set of tomographic images can be reconstructed from a limited number of x‐ray projection images. DBT has the potential to mitigate against both the superposition of non‐adjacent tissue (false positive densities) and the masking of real lesions (false negatives) observed in projection mammography, while also providing a simple means of localizing lesions in 3D. A preliminary retrospective study of breast tomosynthesis by Rafferty et al. has demonstrated a 16% increase in sensitivity and 85% decrease in false positives as compared to digital mammography. Our own experience at the University of Pennsylvania with 51 patients has provided supported anecdotal evidence.

DBT also offers the potential for functional imaging. Breast tumor growth and metastasis are accompanied by the development of new blood vessels. We have used a modified GE 2000D under IRB approval, to gain initial experience in contrast‐enhanced DBT (CE‐DBT). To date we have acquired 13 CE‐DBT clinical cases. Suspicious enhancing lesions were demonstrated with CE‐DBT in 10 of 11 cases of pathology‐proven breast cancer. The cases illustrated that CE‐DBT could provide information in concordance with multimodality imaging evaluation. The pre‐contrast tomosynthesis images demonstrated lesion morphology and border characteristics in greater detail than the digital mammography images, and the CE‐DBT data sets demonstrated vascular characteristics of the breast lesions of interest that were consistent with the vascular information provided by MR. In addition, quantitative evaluation of contrast uptake is anticipated to be more easily standardized with CE‐DBT due to the linear relationship between attenuation and contrast concentration compared to MRI.

In this presentation, the following education objectives will be addressed:

1. Review the development and design of digital breast tomosynthesis systems.

2. Evaluate the results of the existing DBT clinical trials.

3. Examine advanced applications of DBT including contrast‐enhanced DBT and CAD.

4. Compare DBT to other x‐ray tomographic imaging modalities.
TU‐C‐330D‐03: Computed Tomography of the Breast: Design, Fabrication, Characterization, and Initial Clinical Testing
Authors: Boone J , Nelson T , Kwan A , Yang K
Published: July 2006

Purpose: Although there is overwhelming evidence that mammographic screening has led to a reduction in breast cancer mortality, most breast imaging experts agree that screening technology could be improved. We have developed a dedicated breast CT scanner which may be appropriate for breast cancer screening in some groups of women. Methods and Materials: A breast CT scanner was designed and fabricated in our laboratory using off‐the‐shelf components (an x‐ray system, flat panel detector, and motor) and custom‐manufactured parts. Image quality was assessed subjectively in terms of artifacts, and by conventional metrics (MTF for spatial resolution, RMS noise, etc.). Radiation dose levels were adjusted to be comparable to two‐view mammography, using a series of physical measurements and Monte Carlo computations. Evaluation in patients has begun with both Phase I and Phase II clinical trials. Results: The spatial resolution of the bCT system exceeds that of commercial scanners, with a 10 percent MTF corresponding to XX inverse mm (center of field, 80 kVp, 500 views). Noise metrics demonstrates that the scanner performs in a quantum limited manner. Ten healthy volunteers have been scanned, and as of this writing 35 women with BIRADS 4 or 5 diagnoses have been scanned. Subjective evaluation of image quality clearly indicates detail not seen mammographically. The volume dataset (300 512 × 512 pixel images) can be displayed in coronal, axial, saggital or any arbitrary view angle. Conclusions: The clinical evaluation of the bCT system is underway, and early subjective results have generated interesting images with excellent anatomical depiction. The use of contrast agents has added a functional component to breast CT imaging. Further patient accrual with subsequent quantitative (ROC) analysis is needed.

Learning Objectives:

1. Status of breast CT implementation at UC Davis.
TU‐C‐ValA‐01: Image Fusion and Deformation Using a Genetic Algorithm
Authors: Ramsey C , Outten S , Seibert R
Published: July 2006

Purpose: To develop an image fusion application utilizing genetic algorithms, segment the fused images using treatment‐planning contours, perform a slice‐by‐slice sub‐fusion of the segmented images in order to measure deformation, and then utilize the measured deformation for adaptive therapy on a helical tomotherapy treatment delivery. Methods and Materials: A reference CT image of a density and spatial resolution phantom was obtained using a MVCT imaging. A secondary MVCT fusion image was obtained with the phantom offset by a known amount with plugs removed or rotated. An image fusion algorithm was created using genetic programming to perform image registration of the MVCT images. Contours of the plugs were used to extract sub‐images that were separately registered and deformed using the genetic algorithm. Adaptive therapy was achieved thorough a treatment delivery sinogram deformation algorithm. The sinogram deformation algorithm was tested using a geometric test case that consisted of a dose triangle with a 5.2‐cm base located inside a dose circle with of 3.14‐cm radius. The test dose pattern was moved by known amounts by deforming the treatment delivery sinogram. Results: The initial genetic fusion of the reference and secondary MVCT images was achieved in approximately 15 generations. The time required to perform the genetic fusion was typically 10 to 15 seconds. The images were fused to within 0.7‐mm of the correct position. At the end of the initial fusion, the genetic algorithm correctly identified one of the plugs as missing in the secondary MVCT dataset. The genetic algorithm correctly segmented the second resolution plug in a sub‐image and deformed it to within 1‐degree and 0.7‐mm of the correct position. The delivery deformation tests moved the dose to within 5‐mm of the desired position. Conclusions: A genetic algorithm has been developed for performing image fusion and simple deformation of defined regions of interest.
TU‐C‐ValA‐02: A Simple Iterative Method to Invert a Deformation Field
Authors: Chen M , Lu W , Ruchala K , Chen Q , Pan Y , Olivera G
Published: July 2006

Purpose: Inversion of a deformation field is applied frequently to map dose and regions of interest to a reference frame. A prevailing naïve approach that takes the opposite displacement of the forward deformation as the displacement of the inverse is mathematically wrong and can cause large errors for large or accumulative deformation. Inversion by “scattered data interpolation” has O(N2) complexity and is difficult to implement. We propose a simple iterative approach with O(N) complexity. Method: Instead of calculating the inverse, we calculate the displacement of the inverse. The displacement of the inverse is iteratively refined through the displacement of the forward map. We prove that such iterative scheme converges exponentially to the true solution when the deformation field is subject to a condition of the Lipschitz type. The Lipschitz type condition essentially states that the difference of the deformation of two points can not be too far. This is a mild restriction on the deformation field and is usually a valid assumption for any deformable registration method with regularization.

Results: We tested the proposed method on both simulated 2D data and real 4D CT data of lung patient. The simulations showed that the proposed method has exponential convergences to the true inverse. For real 4D CT data, the forward deformation field constructed by deformable registration mapped the test phase to the reference phase and the inverse of that deformation field accurately map the reference phase to the test phase. Conclusions: A simple, accurate and fast method for inverting a deformation field is presented. Both the mathematical proof and the simulations showed its exponential convergence. Simulations and real data tests demonstrated its efficacy in medical image analysis and radiotherapy applications. Typically less than 10 iterations are needed to get an inverse deformation field with clinically relavent accuracy.
TU‐C‐ValA‐05: Assessment of a Model‐Based Deformable Image Registration Approach for Radiotherapy Planning
Authors: Kaus M , Brock K , Pekar V , Bzdusek K , Dawson L , Nichol A , Jaffray D
Published: July 2006

Purpose: To assess the accuracy of a surface‐based deformable image registration strategy as a function of the elasticity model for the integration of multi‐modality imaging, image‐guided radiation therapy, and quantification of geometrical change during and following therapy.

Method and Materials: A surface‐model based deformable image registration system has been developed that enables quantitative description of geometrical change in multi‐modal images. Based on the deformation of organ surfaces represented by triangular surface meshes, a volumetric deformation field is derived using different volumetric elasticity models (Thin‐Plate Splines, Wendland functions, Elastic Body Splines) as alternatives to finite‐element modeling. Results: The system was demonstrated on five liver cancer patients, ten prostate cancer patients, thorax in five healthy volunteers, and abdomen in five healthy volunteers. The accuracy of the system was assessed by tracking visible fiducials (bronchial bifurcations in the lung, vessel bifurcations in the liver, implanted gold markers in the prostate). The maximum displacements for lung, liver and prostate were 5.3 cm, 3.2 cm, and 1.8 cm respectively. The largest registration error (direction, mean ± standard deviation) for lung, liver and prostate were (inferior‐superior, −0.21 ± 0.38 cm), (anterior‐posterior, −0.09 ± 0.34 cm), and (left‐right, 0.04 ± 0.38 cm) respectively, which was within the image resolution regardless of the deformation model. The computation time (2.7 GHz Intel Xeon) was on the order of seconds (e.g. 10 seconds for two prostate data sets), and image deformation results could be viewed at interactive speed (less than 1 second for 512×512 voxels). Conclusion: Surface‐based deformable image registration enables the quantification of geometrical change in normal tissue and tumor with acceptable accuracy and speed.
TU‐C‐ValA‐07: Intra‐ and Inter‐Modality Registration of Four‐Dimensional (4D) Images
Authors: Schreibmann E , Thorndyke B , Xing L
Published: July 2006

Purpose: 4D‐imaging techniques such as 4D‐CT/MRI/PET reveal spatial and temporal details of patient's anatomy. Here we develop a 4D‐4D registration method to utilize the 4D data acquired under different conditions or using different modalities. Method: A 4D input (model or reference) consists of a number of 3D sets of images, each representing the patient's anatomy at a phase point. When the patient's breathing pattern is repeatable, the task of 4D‐4D matching is to find the appropriate 3D dataset in the model input for each phase in reference. Instead of exhaustively searching for the best match for each phase, a search algorithm was implemented, which can simultaneously find the matches for all phases with consideration of temporal relationship between the 3D image sets in the inputs. An interpolation scheme capable of deriving an image set based on two temporally adjacent 3D‐datasets was implemented to deal with the situation where the discrete temporal points of the two inputs do not coincide. Digital phantom and patient studies were performed to illustrate the inter‐/intra‐modality 4D‐4D registration technique. Results: In the phantom study where the optimal match is known, the proposed technique was able to reproduce the “ground truth” with high spatial fidelity (<1.5mm). In addition, the technique regenerated all deliberately introduced “missing” 3D images at different phase points in one of the inputs because of the temporal interpolation. In a registration of gated‐MRI and 4DCT, the technique enabled us to optimally select the corresponding CT phase. The technique was also found useful for the registration of two sets of 4DCTs acquired at different time points. In this situation, a spatial accuracy of less than 2.5mm was achieved in all three cases. Conclusions: Automated 4D‐4D registration can find the best possible spatio‐temporal match between the two 4D datasets and may have significant implication for IGRT.
TU‐C‐ValB‐01: Evaluation of Clinical Margins Via Simulation of Patient Setup Errors in 27 Prostate IMRT Plans
Authors: Gordon J , Crimaldi A , Siebers J
Published: July 2006

Purpose: To evaluate: (i) the size of random and systematic setup errors that can be absorbed by 5mm CTV‐to‐PTV margins in prostate IMRT treatment plans; (ii) whether findings are consistent with published margin recipes; (iii) if shifting contours with respect to a static dose distribution accurately predicts dose coverage due to setup errors.

Method and Materials: 27 IMRT treatment plans with 5mm CTV‐to‐PTV margins were utilized. Random setup errors with standard deviations (SDs) of 1.5, 3 and 5mm were simulated by fluence convolution. Systematic errors with the same SDs were simulated using two methods: (a) shifting the isocenter and recomputing dose (isocenter shift), and (b) shifting patient contours with respect to the static dose distribution (contour shift). Maximum tolerated errors were evaluated such that 90% of plans had target coverage greater than a specified minimum. Results: For coverage criteria consistent with margin formulas, plans generated with a 5mm margin were able to absorb SDs >3mm. Most structures, including the prostate CTV, showed close agreement between isocenter and contour shift methods. Exceptions were the nodal CTV and small bowel. For 3mm SDs, contour vs isocenter shift estimates for the percent of plans with acceptable dose differed by >2% for the nodal CTV, and >7% for the small bowel. Contour shift small bowel D30 values differed from isocenter shift values by >120% for some simulated shifts. Conclusion: Published recipes require margins of 8–10mm for 3mm SDs. For the IMRT cases presented here, a 5mm margin would suffice. Approximating structure doses by shifting contours with respect to a static dose distribution was acceptable for most structures, but resulted in significant errors for the nodal CTV and small bowel doses for some shifts due to proximity to high dose gradients. (Work supported by NIH R01CA98524).
TU‐C‐ValB‐02: Patient Specific Differences in Setup Error Variability and Its Effect On Treatment Margins in Fractioned Radiotherapy
Authors: van Herk M , Witte M
Published: July 2006

Purpose: It is often assumed that geometric error distributions in radiotherapy differ from patient to patient. It is however, problematic to substantiate this assumption because, generally, limited measurements are available per patient, giving a high uncertainty in the estimate of the standard deviation (SD). Our aim is to develop a simple method to estimate the true distribution variability based on statistical data analysis of a large patient population and to investigate the effect of detected variability on population based PTV‐margins. Method and Materials: Setup error data of 470 prostate cancer patients (11 portal imaging measurements per patient, used for off‐line corrections), were analyzed for random errors. The SD of the setup error was computed for each patient. The RMS‐values of these numbers estimate the random uncertainty in the patient population. Next, the SD of the SD per patient is computed, containing the real distribution variability diluted by “measurement error in the SD” due to the limited number of samples. To estimate the true distribution variability, a correction is applied for this “measurement error”. Finally, that margin was calculated that encloses the CTV with the 95% isodose for 90% of the population. Results: The true inter‐patient variability is 26% of the SD, found after correcting for a “measurement error” of 18% (11 samples). Inter‐patient distribution variability leads to larger PTV‐margins, partly because the range of dose blurring becomes patient dependent. Assuming normality and the same SD variability in random and systematic errors, the margin for systematic errors increases from 2.5SD to 2.8SD, maintaining the same margin for random errors. Conclusion: Inter‐patient distribution variability exists but only slightly exceeds its measurement error and it is therefore difficult to detect for individual patients. By grouping many patients, it can be detected. A variable distribution requires slightly larger margins than a homogenous one.
TU‐C‐ValB‐06: Intra‐Fraction Motion of Immobilized Intra‐Cranial and Extra‐Cranial Patients Assessed by the CyberKnife Image‐Guidance System
Authors: Hoogeman M , Nuyttens J , Levendag P , Heijmen B
Published: July 2006

Purpose: To quantify intra‐fraction motion of immobilized intra‐cranial and extra‐cranial patients. The data can be used to optimise the intra‐fraction imaging frequency and consequent patient set‐up correction with the CyberKnife image‐guidance system and to establish the required margins in the absence of such a system. Method and Materials: We analysed the intra‐fraction motion of 21 intra‐cranial patients, who were immobilized with a thermoplastic mask and 9 supine and 8 prone treated extra‐cranial patients, who were immobilized with a vacuum bag. The motion was recorded by the CyberKnife image‐guidance system. We analysed the intra‐fraction motion by calculating the mean displacement with the standard deviation (SD) as a function of the time between kV X‐ray localizations. For the three groups separately, we calculated the systematic (overall mean and SD) and the random displacement as a function of the imaging frequency. Results: For all patients, the overall mean displacement was below 0.5 mm (3D vector) over a period of 15 min and hardly increased. The SD of the systematic displacements increased linearly over time for all 3 patient groups. For intra‐cranial, supine and prone treated patients, this SD increased to 0.5, 1.2, and 1.6 mm, respectively, in a period of 15 min. The random displacements for the prone treated patients were significantly higher than for the other groups, namely 1.3 mm (1 SD). This was most pronounced in the AP direction, suggesting that the larger intra‐fraction motion was caused by respiratory motion. Conclusions: Repeated intra‐fraction imaging and consequent patient set‐up correction with an interval of less than 5 min adequately compensates for patient motion during treatment. In the absence of this procedure, intra‐fraction motion has to be accounted for in the PTV margin.