bluuebunny/crossref_metadata_2025 huggingface dataset:
MO‐D‐BRC‐04: Multiple‐Criteria Optimization Planning
Authors: Donaghue J.
Published: July 2016

Treatment planning is a central part of radiation therapy, including delineation in tumor volumes and critical organs, setting treatment goals of prescription doses to the tumor targets and tolerance doses to the critical organs, and finally generation of treatment plans to meet the treatment goals. National groups like RTOG have led the effort to standardize treatment goals of the prescription doses to the tumor targets and tolerance doses to the critical organs based on accumulated knowledge from decades of abundant clinical trial experience. The challenge for each clinical department is how to achieve or surpass these set goals within the time constraints of clinical practice. Using fifteen testing cases from different treatment sites such as head and neck, prostate with and without pelvic lymph nodes, SBRT spine, we will present clinically utility of advanced planning tools, including knowledge based, automatic based, and multiple criteria based tools that are clinically implemented.

The objectives of this session are:

Understand differences among these three advanced planning tools

Provide clinical assessments on the utility of the advanced planning tools

Discuss clinical challenges of treatment planning with large variations in tumor volumes and their relationships with adjacent critical organs.

Ping Xia received research grant from Philips. Jackie Wu received research grant from Varian; P. Xia, Research support by Philips and Varian; Q. Wu, NIH, Varian Medical
MO‐D‐BRB‐01: Fundamental Aspects of SBRT
Authors: Yin F.
Published: July 2016

Increased use of SBRT and hypofractionation in radiation oncology practice has posted a number of challenges to medical physicist, ranging from planning, image‐guided patient setup and on‐treatment monitoring, to quality assurance (QA) and dose delivery. This symposium is designed to provide current knowledge necessary for the safe and efficient implementation of SBRT in various linac platforms, including the emerging digital linacs equipped with high dose rate FFF beams. Issues related to 4D CT, PET and MRI simulations, 3D/4D CBCT guided patient setup, real‐time image guidance during SBRT dose delivery using gated/un‐gated VMAT/IMRT, and technical advancements in QA of SBRT (in particular, strategies dealing with high dose rate FFF beams) will be addressed. The symposium will help the attendees to gain a comprehensive understanding of the SBRT workflow and facilitate their clinical implementation of the state‐of‐art imaging and planning techniques.

Learning Objectives:

Present background knowledge of SBRT, describe essential requirements for safe implementation of SBRT, and discuss issues specific to SBRT treatment planning and QA.

Update on the use of multi‐dimensional and multi‐modality imaging for reliable guidance of SBRT.

Discuss treatment planning and QA issues specific to SBRT.

Provide a comprehensive overview of emerging digital linacs and summarize the key geometric and dosimetric features of the new generation of linacs for substantially improved SBRT.

NIH/NCI; Varian Medical Systems; F. Yin, Duke University has a research agreement with Varian Medical Systems. In addition to research grant, I had a technology license agreement with Varian Medical Systems
MO‐D‐BRB‐03: Quality Assurance of SBRT
Authors: Roper J.
Published: July 2016

Increased use of SBRT and hypofractionation in radiation oncology practice has posted a number of challenges to medical physicist, ranging from planning, image‐guided patient setup and on‐treatment monitoring, to quality assurance (QA) and dose delivery. This symposium is designed to provide current knowledge necessary for the safe and efficient implementation of SBRT in various linac platforms, including the emerging digital linacs equipped with high dose rate FFF beams. Issues related to 4D CT, PET and MRI simulations, 3D/4D CBCT guided patient setup, real‐time image guidance during SBRT dose delivery using gated/un‐gated VMAT/IMRT, and technical advancements in QA of SBRT (in particular, strategies dealing with high dose rate FFF beams) will be addressed. The symposium will help the attendees to gain a comprehensive understanding of the SBRT workflow and facilitate their clinical implementation of the state‐of‐art imaging and planning techniques.

Learning Objectives:

Present background knowledge of SBRT, describe essential requirements for safe implementation of SBRT, and discuss issues specific to SBRT treatment planning and QA.

Update on the use of multi‐dimensional and multi‐modality imaging for reliable guidance of SBRT.

Discuss treatment planning and QA issues specific to SBRT.

Provide a comprehensive overview of emerging digital linacs and summarize the key geometric and dosimetric features of the new generation of linacs for substantially improved SBRT.

NIH/NCI; Varian Medical Systems; F. Yin, Duke University has a research agreement with Varian Medical Systems. In addition to research grant, I had a technology license agreement with Varian Medical Systems
MO‐DE‐201‐00: Medical Physics Leadership Academy
Authors: Clements Jessica
Published: July 2016

Medical physicists possess technical knowledge and problem solving skills required to deliver high quality diagnosis and treatment in the clinic, to provide excellence in research, or to transfer expertise in industrial or regulatory environments. As the healthcare industry rapidly evolves, medical physicists must make a case for ensuring recognition of their value. One of many ways the AAPM is working to ensure stewardship of the profession is through development of the Medical Physics Leadership Academy (MPLA).

This session will provide an introduction to the MPLA and one of its focus areas of emotional intelligence. The session will make the case for emotional competencies in addition to technical competencies. MPLA work group representatives will share consolidated results from conducting 360 feedback assessments used to measure emotional competencies of medical physicists. Some emotional competencies contribute more to professional success than others, and the session will explain how to overcome shortcomings. Two specific emotional competencies will be covered: selfawareness and empathy.

Learning Objectives:

Understand the importance of medical physics leadership in the future of health care

Understand emotional intelligence and emotional competencies and ways they can be measured

Utilize self‐awareness and empathy to improve management of our own emotions and others
MO‐DE‐BRA‐03: The Ottawa Medical Physics Institute (OMPI): A Practical Model for Academic Program Collaboration in a Multi‐Centre City
Authors: McEwen M , Rogers D , Johns P
Published: July 2016

Purpose:

To build a world‐class medical physics educational program that capitalizes on expertise distributed over several clinical, government, and academic centres. Few if any of these centres would have the critical mass to solely resource a program.

Methods:

In order to enable an academic program, stakeholders from five institutions made a proposal to Carleton University for a) a research network with defined membership requirements and a process for accepting new members, and b) a graduate specialization (MSc and PhD) in medical physics. Both proposals were accepted and the program has grown steadily. Our courses are taught by medical physicists from across the collaboration. Our students have access to physicists in: clinical radiotherapy (the Ottawa Cancer Centre treats 4500 new patients/y), radiology, cardiology and nuclear medicine, Canada's primary standards dosimetry laboratory, radiobiology, and university‐based medical physics research. Our graduate courses emphasize the foundational physics plus applied aspects of imaging, radiotherapy, and radiobiology. Active researchers in the city‐wide volunteer‐run network are appointed as adjunct professors by Physics, giving them access to national funding competitions and partial student funding through teaching assistantships while opening up facilities in their institutions for student thesis research.

Results:

The medical physics network has grown to ∼40 members from eight institutions and includes five full‐time faculty in Physics and 17 adjunct research professors. The graduate student population is ∼20. Our graduates have proceeded to a spectrum of careers. Our alumni list includes a CCPM Past‐President, the current COMP President, many clinical physicists, and the heads of at least three major clinical medical physics departments. Our PhD was Ontario's first CAMPEP‐accredited program.

Conclusion:

A self‐governing volunteer network is the foundational element that enables an MSc/PhD medical physics program in a city with multiple physicist employers. It enriches graduate education with an unusually broad range of expertise.
MO‐DE‐BRA‐05: EUTEMPE‐RX: Combining E‐Learning and Face‐To‐Face Training to Build Expert Knowledge, Skills and Competences for Medical Physicists in Diagnostic and Interventional Radiology
Authors: Bosmans H , Van Peteghem N , Mackenzie A , Vano E , Creten S , Borowski M , Christofides S , Caruana C
Published: July 2016

Purpose:

In 2013, the EURATOM authorities of the European Commission decided to support the Horizon2020 project submission ‘EUTEMPE‐RX’ that aimed for a new set of course modules to train medical physicists in diagnostic and interventional radiology to expert level with small group deep learning. Each module would consist of 2 phases: an e‐learning and a face‐to‐face phase, each phase requiring typically 40h of participant time.

Methods:

The European Federation (EFOMP) and 13 European partners, all of them selected for their excellent scientific and/or educational skills, led the 12 course modules. A quality manual ensured the quality of course content and organization. Educational workshops familiarized the teachers with e‐learning techniques and methods for assessment. Content was set in accordance with the EC document RP174 that lists learning outcomes in terms of knowledge, skills and competences (KSCs) for different specialties and levels of medical physics. Surveys for stake holder satisfaction were prepared.

Results:

Today the course modules are being realized. The modules cover most of the KSCs in RP174 document. Teachers have challenged the participants with unique tasks: case studies in medical physics leadership, Monte Carlo simulation of a complete x‐ray imaging chain, development of a task specific QA protocol, compilation of optimization plans, simulation tasks with anthropomorphic breast models, etc. Participants undertook practical sessions in modern hospitals and visited a synchrotron facility, a calibration lab, screening organizations, etc. Feedback form quality surveys was very positive and constructive. A sustainability plan has been worked out.

Conclusion:

The modules have enabled the participants to develop their KSCs and cope with challenges in medical physics. The sustainability plan will be implemented to continue the unique combined e‐learning and face to face training at high level training in diagnostic and interventional radiology also beyond Europe.

The EUTEMPE‐RX project is funded by the Horizon2020 ‐ EURATOM program of the European Commission
TU‐AB‐BRC‐12: Optimized Parallel MonteCarlo Dose Calculations for Secondary MU Checks
Authors: French S , Bellor M , Nazareth D
Published: July 2016

Purpose:

Secondary MU checks are an important tool used during a physics review of a treatment plan. Commercial software packages offer varying degrees of theoretical dose calculation accuracy, depending on the modality involved. Dose calculations of VMAT plans are especially prone to error due to the large approximations involved. Monte Carlo (MC) methods are not commonly used due to their long run times. We investigated two methods to increase the computational efficiency of MC dose simulations with the BEAMnrc code. Distributed computing resources, along with optimized code compilation, will allow for accurate and efficient VMAT dose calculations.

Methods:

The BEAMnrc package was installed on a high performance computing cluster accessible to our clinic. MATLAB and PYTHON scripts were developed to convert a clinical VMAT DICOM plan into BEAMnrc input files. The BEAMnrc installation was optimized by running the VMAT simulations through profiling tools which indicated the behavior of the constituent routines in the code, e.g. the bremsstrahlung splitting routine, and the specified random number generator. This information aided in determining the most efficient compiling parallel configuration for the specific CPU's available on our cluster, resulting in the fastest VMAT simulation times. Our method was evaluated with calculations involving 108 – 109 particle histories which are sufficient to verify patient dose using VMAT.

Results:

Parallelization allowed the calculation of patient dose on the order of 10 – 15 hours with 100 parallel jobs. Due to the compiler optimization process, further speed increases of 23% were achieved when compared with the open‐source compiler BEAMnrc packages.

Conclusion:

Analysis of the BEAMnrc code allowed us to optimize the compiler configuration for VMAT dose calculations. In future work, the optimized MC code, in conjunction with the parallel processing capabilities of BEAMnrc, will be applied to provide accurate and efficient secondary MU checks.
TU‐A‐BRB‐00: PANEL DISCUSSION: SBRT/SRS Case Studies ‐ Brain and Spine
Authors: Schlesinger David
Published: July 2016

Brain stereotactic radiosurgery (SRS) and spine stereotactic body radiation therapy (SBRT) are commonly treated by a multidisciplinary team of neurosurgeons, radiation oncologists, and medical physicists. However the treatment objectives, constraints, and technical considerations involved can be quite different between the two techniques. In this interactive session an expert panel of speakers will present clinical brain SRS and spine SBRT cases in order to demonstrate real‐world considerations for ensuring safe and accurate treatment delivery and to highlight the significant differences in approach for each treatment site. The session will include discussion of topic such as clinical indications, immobilization, target definition, normal tissue tolerance limits, and beam arrangements.

Learning Objectives:

Understand the differences in indications and dose/fractionation strategies for intracranial SRS and spine SBRT.

Describe the different treatment modalities which can be used to deliver intracranial SRS and spine SBRT.

Cite the major differences in treatment setup and delivery principles between intracranial and spine treatments.

Identify key critical structures and clinical dosimetric tolerance levels for spine SBRT and intracranial SRS.

Understand areas of ongoing work to standardize intracranial SRS and spine SBRT procedures.

Schlesinger: Research support: Elekta Instruments, AB; D. Schlesinger, Elekta Instruments, AB ‐ research support; B. Winey, No relevant external funding for this subject.
TU‐A‐BRB‐01: Panel Member
Authors: Schlesinger D.
Published: July 2016

Brain stereotactic radiosurgery (SRS) and spine stereotactic body radiation therapy (SBRT) are commonly treated by a multidisciplinary team of neurosurgeons, radiation oncologists, and medical physicists. However the treatment objectives, constraints, and technical considerations involved can be quite different between the two techniques. In this interactive session an expert panel of speakers will present clinical brain SRS and spine SBRT cases in order to demonstrate real‐world considerations for ensuring safe and accurate treatment delivery and to highlight the significant differences in approach for each treatment site. The session will include discussion of topic such as clinical indications, immobilization, target definition, normal tissue tolerance limits, and beam arrangements.

Learning Objectives:

Understand the differences in indications and dose/fractionation strategies for intracranial SRS and spine SBRT.

Describe the different treatment modalities which can be used to deliver intracranial SRS and spine SBRT.

Cite the major differences in treatment setup and delivery principles between intracranial and spine treatments.

Identify key critical structures and clinical dosimetric tolerance levels for spine SBRT and intracranial SRS.

Understand areas of ongoing work to standardize intracranial SRS and spine SBRT procedures.

Schlesinger: Research support: Elekta Instruments, AB; D. Schlesinger, Elekta Instruments, AB ‐ research support; B. Winey, No relevant external funding for this subject.
TU‐A‐BRB‐02: Panel Member
Authors: Ma L.
Published: July 2016

Brain stereotactic radiosurgery (SRS) and spine stereotactic body radiation therapy (SBRT) are commonly treated by a multidisciplinary team of neurosurgeons, radiation oncologists, and medical physicists. However the treatment objectives, constraints, and technical considerations involved can be quite different between the two techniques. In this interactive session an expert panel of speakers will present clinical brain SRS and spine SBRT cases in order to demonstrate real‐world considerations for ensuring safe and accurate treatment delivery and to highlight the significant differences in approach for each treatment site. The session will include discussion of topic such as clinical indications, immobilization, target definition, normal tissue tolerance limits, and beam arrangements.

Learning Objectives:

Understand the differences in indications and dose/fractionation strategies for intracranial SRS and spine SBRT.

Describe the different treatment modalities which can be used to deliver intracranial SRS and spine SBRT.

Cite the major differences in treatment setup and delivery principles between intracranial and spine treatments.

Identify key critical structures and clinical dosimetric tolerance levels for spine SBRT and intracranial SRS.

Understand areas of ongoing work to standardize intracranial SRS and spine SBRT procedures.

Schlesinger: Research support: Elekta Instruments, AB; D. Schlesinger, Elekta Instruments, AB ‐ research support; B. Winey, No relevant external funding for this subject.
TU‐AB‐BRA‐01: Abdominal Synthetic CT Generation in Support of Liver SBRT Dose Calculation
Authors: Bredfeldt JS , Liu L , Feng M , Cao Y , Balter J
Published: July 2016

Purpose:

To demonstrate and validate a technique for generating MRI‐derived synthetic CT volumes (MRCTs) in support of adaptive liver SBRT.

Methods:

Under IRB approval, ten hepatocellular carcinoma patients were scanned using a single MR sequence (T1 Dixon‐VIBE), yielding inherently‐registered water, fat, and T1‐weighted images. Air‐containing voxels were identified by intensity thresholding. The envelope of the anterior vertebral bodies was segmented from the fat image by fitting a shape model to vertebral body candidate voxels, then using level sets to expand the contour outward. Fuzzy‐C‐Means (FCM) was then used to classify each non‐air voxel in the image as fat, water, bone, or marrow. Bone and marrow only were classified within the vertebral body envelope. The MRCT was created by integrating the product of the FCM class probability with the assigned class density for each voxel. The resulting MRCTs were deformably aligned with planning CTs and 2‐ARC SBRT VMAT plans were optimized on the MRCT density maps. Fluence was copied onto the CT density grids and dose recalculated.

Results:

The MRCTs faithfully reproduced most of the features visible in the corresponding CT image volumes, with exceptions of ribs and posterior spinous processes. The liver, vertebral bodies, kidneys, spleen and cord all had median HU differences of less than 75 between MRCT and CT images. PTV D99% values had an average 0.2% difference (standard deviation: 0.46%) between calculations on MRCT and CT density grids. The maximum difference in dose to 0.1cc of the PTV was 0.25% (std:0.49%). OAR dose differences were similarly small (mean:0.03Gy, std:0.26Gy). The largest normal tissue complication percentage (NTCP) difference was 1.48% (mean:0.06%, std:0.54%).

Conclusions:

MRCTs from a single abdominal imaging sequence are promising for use in SBRT dose calculation. Future work will focus on extending models to better define bones in the upper abdomen.

Supported by NIHR01EB016079 and NIH1L30CA199594‐01
TU‐AB‐BRA‐03: Atlas‐Based Algorithms with Local Registration‐Goodness Weighting for MRI‐Driven Electron Density Mapping
Authors: Farjam R , Tyagi N , Veeraraghavan H , Apte A , Zakian K , Hunt M , Deasy J
Published: July 2016

Purpose:

To develop image‐analysis algorithms to synthesize CT with accurate electron densities for MR‐only radiotherapy of head & neck (H&N) and pelvis anatomies.

Methods:

CT and 3T‐MRI (Philips, mDixon sequence) scans were randomly selected from a pool of H&N (n=11) and pelvis (n=12) anatomies to form an atlas. All MRIs were pre‐processed to eliminate scanner and patient‐induced intensity inhomogeneities and standardize their intensity histograms. CT and MRI for each patient were then co‐registered to construct CT‐MRI atlases. For more accurate CT‐MR fusion, bone intensities in CT were suppressed to improve the similarity between CT and MRI. For a new patient, all CT‐MRI atlases are deformed onto the new patients’ MRI initially. A newly‐developed generalized registration error (GRE) metric was then calculated as a measure of local registration accuracy. The synthetic CT value at each point is a 1/GRE‐weighted average of CTs from all CT‐MR atlases. For evaluation, the mean absolute error (MAE) between the original and synthetic CT (generated in a leave‐one‐out scheme) was computed. The planning dose from the original and synthetic CT was also compared.

Results:

For H&N patients, MAE was 67±9, 114±22, and 116±9 HU over the entire‐CT, air and bone regions, respectively. For pelvis anatomy, MAE was 47±5 and 146±14 for the entire and bone regions. In comparison with MIRADA medical, an FDA‐approved registration tool, we found that our proposed registration strategy reduces MAE by ∼30% and ∼50% over the entire and bone regions, respectively. GRE‐weighted strategy further lowers MAE by ∼15% to ∼40%. Our primary dose calculation also showed highly consistent results between the original and synthetic CT.

Conclusion:

We've developed a novel image‐analysis technique to synthesize CT for H&N and pelvis anatomies. Our proposed image fusion strategy and GRE metric help generate more accurate synthetic CT using locally more similar atlases (Support: Philips Healthcare).

The research is supported by Philips HealthCare
TU‐AB‐BRA‐05: Optimization of a Novel Large Field of View Distortion Phantom for MR‐Only Treatment Planning
Authors: Price R.G. , Knight R.A. , Hwang K. , Bayram E. , Glide‐Hurst C.
Published: July 2016

Purpose:

MR‐only treatment planning requires images of high geometric fidelity, particularly for large fields of view (FOV). However, the availability of large FOV distortion phantoms with accompanying analysis software is currently limited to in‐house solutions. This work sought to optimize the design of a modular distortion phantom that can accommodate several bore configurations and implement distortion corrections.

Methods:

1.0T MR and CT images were acquired of twelve urethane foam samples of various densities and strengths. Samples were precision machined to accommodate 6 mm diameter paintballs used as landmarks and signal‐to‐noise ratio was calculated. Final material candidates were selected by balancing strength, machinability, weight, and cost. Bore sizes and minimum aperture width resulting from couch position were tabulated from the literature for 13 systems across 4 vendors. Bore geometry and couch position were simulated in MATLAB to generate machine‐specific models to optimize the phantom build. Previously developed software for distortion characterization (control point detection, polynomial fitting, and correction map generation) was modified for several magnet geometries and integrated into Slicer, a widely available image analysis toolkit.

Results:

All foam samples provided sufficient image contrast with paintball landmarks. Urethane foam with a compressive strength of ∼1000psi and density of 20lb/ft3 was chosen for its accurate machinability and relatively light weight. A modular phantom design was optimized to accommodate all magnet geometries with the following parameters: 15 foam plates, 55×55×37.5cm, 5,082 landmarks, and 66 lbs. To accommodate wide bore magnets (>70 cm), an extended build spanned 55×55×50cm over 20 plates with 7,497 landmarks and weighing 97lbs. Distortion characterization software was implemented as an external module into Slicer's plugin framework.

Conclusion:

We optimized the design and implementation of a modular, extendable distortion phantom to support an MR‐only workflow and MR‐IGRT. The phantom will be useful for future multi‐institution collaborations and cross‐validation studies.

The submitting institution has research agreements with Philips Healthcare. Research sponsored by a Henry Ford Health System Internal Mentored Grant. Technical support and initial distortion phantom design was provided by GE Healthcare.
TU‐AB‐BRA‐11: Indications for Online Adaptive Radiotherapy Based On Dosimetric Consequences of Interfractional Pancreas‐To‐Duodenum Motion in MRI‐Guided Pancreatic Radiotherapy
Authors: Mittauer K , Rosenberg S , Geurts M , Bassetti M , Chen I , Henke L , Olsen J , Kashani R , Wojcieszynski A , Harari P , Labby Z , Hill P , Paliwal B , Parikh P , Bayouth J
Published: July 2016

Purpose:

Dose limiting structures, such as the duodenum, render the treatment of pancreatic cancer challenging. In this multi‐institutional study, we assess dosimetric differences caused by interfraction pancreas‐to‐duodenum motion using MR‐IGRT to determine the potential impact of adaptive replanning.

Methods:

Ten patients from two institutions undergoing MRI‐guided radiotherapy with conventional fractionation (n=5) or SBRT (n=5) for pancreatic cancer were included. Initial plans were limited by duodenal dose constraints of 50 Gy (0.5 cc)/31 Gy (0.1 cc) for conventional/SBRT with prescriptions of 30 Gy/5 fractions (SBRT) and 40–50 Gy/25 fractions (conventional). Daily volumetric MR images were acquired under treatment conditions on a clinical MR‐IGRT system. The correlation was assessed between interfractional GTV‐to‐duodenum positional variation and daily recalculations of duodenal dose metrics. Positional variation was quantified as the interfraction difference in Hausdorff distance from simulation baseline (ΔHD) between the GTV and proximal duodenal surface, or volume overlap between GTV and duodenum for cases with HD0=0 (GTV abutting duodenum). Adaptation was considered indicated when daily positional variations enabled dose escalation to the target while maintaining duodenal constraints.

Results:

For fractions with ΔHD>0 (n=14, SBRT only), the mean interfraction duodenum dose decrease from simulation to treatment was 44±53 cGy (maximum 136 cGy). A correlation was found between ΔHD and dosimetric difference (R2=0.82). No correlation was found between volume of overlap and dosimetric difference (R2=0.31). For 89% of fractions, the duodenum remained overlapped with the target and the duodenal dose difference was negligible. The maximum observed indication for adaptation was for interfraction ΔHD=11.6 mm with potential for adaptive dose escalation of 136 cGy.

Conclusion:

This assessment showed that Hausdorff distance was a reasonable metric to use to determine the indication for adaptation. Adaptation was potentially indicated in 11% of the treatments (fractions where GTV‐to‐duodenum distance increased from simulation), with a feasible average dose escalation of 7.0%.

MB, LH, JO, RK, PP: research and/or travel funding from ViewRay Inc. PP: research grant from Varian Medical Systems and Philips Healthcare
TU‐AB‐202‐01: Multi‐Institutional Validation Study of Commercially Available Deformable Image Registration Software for Thoracic Images
Authors: Kadoya N , Nakajima Y , Saito M , Miyabe Y , Kurooka M , Kito S , Sasaki M , Fujita Y , Arai K , Tani K , Yagi M , Wakita A , Tohyama N , Jingu K
Published: July 2016

Purpose:

The purpose of this study was to assess the accuracy of commercially available deformable image registration (DIR) software for thoracic images on multi‐institution. Furthermore, we determined the variation in the DIR accuracy among institutions due to different DIR algorithms and DIR procedures.

Methods:

Thoracic four‐dimensional (4D) CT images of ten patients with esophagus or lung cancer were used. Datasets for these patients were provided by DIR‐lab (dir‐lab.com) and included a coordinate list of anatomical landmarks (300 bronchial bifurcations) that had been manually identified. DIR was performed between peak‐inhale and peak‐exhale images. DIR registration error was determined by calculating the difference at each landmark point between displacement calculated by DIR software and that calculated by the landmark.

Results:

Eleven institutions participated in this study: Four institutions used RayStation (RaySearch Laboratories, Stockholm, Sweden), five institutions used MIM software (MIM Software Inc, Cleveland, OH) and three institutions used Velocity (Varian Medical Systems, Palo Alto, CA). The range of average absolute registration errors over all cases were 0.48–1.51 mm (right‐left), 0.53–2.86 mm (anterior‐posterior), 0.85–4.46 mm (superiorinferior) and 1.26–6.20 mm (three‐dimensional [3D]). For each DIR software, the average 3D registration error (range) was 3.28mm (1.26–3.91 mm) for RayStation; MIM was 3.29mm (2.17–3.61 mm); Velocity was 5.01mm (4.02–6.20 mm). These results showed that there was moderate variation among institutions, even though the DIR software was same.

Conclusion:

We evaluated the commercially available DIR software using thoracic 4D CT images on multi‐center. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both DIR software and DIR procedure. Our results could be helpful for establishment of prospective clinical trials and widespread use of DIR software. In addition, in clinical care, we should try to find the optimal DIR procedure, when DIR was performed using thoracic 4D‐CT data to calculate the accumulated dose.
TU‐AB‐202‐03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT
Authors: Kim H , Chen J , Phillips J , Kirby N
Published: July 2016

Purpose:

Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel‐specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron‐emission tomography (PET) with DIR.

Methods:

Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET‐CT image set. A deformation was digitally applied to the diagnostic CT to create the planning CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT.

Results:

The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient‐based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT‐predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch.

Conclusion:

The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.
TU‐AB‐202‐05: GPU‐Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling
Authors: Zhong Z , Zhuang L , Gu X , Wang J , Chen H , Zhen X
Published: July 2016

Purpose:

Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real‐time and high‐quality image segmentation, especially in a large datasets of 4D‐CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature‐based tetrahedral meshing and GPU‐based parallelization.

Methods:

The first step is to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D‐CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D‐CT can be obtained by matching each phase of the target 4D‐CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability.

Results:

A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel‐based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10‐phase 4D‐CT with a phase dimension of 256×256×150.

Conclusion:

The proposed 4D DIR method uses feature‐based mesh and GPU‐based parallelism, which demonstrates the capability to compute both high‐quality image and motion results, with significant improvement on the computational speed.
TU‐AB‐202‐06: Quantitative Evaluation of Deformable Image Registration in MRI‐Guided Adaptive Radiation Therapy
Authors: Mooney K , Zhao T , Duan Y , Zhang M , Green O , Mutic S , Yang D
Published: July 2016

Purpose:

To assess the performance of the deformable image registration algorithm used for MRI‐guided adaptive radiation therapy using image feature analysis.

Methods:

MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt‐60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC) for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC.

Results:

Image feature analysis produced between 2000–9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55).

Conclusions:

Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap.

We have a research agreement with ViewRay Inc.
TU‐AB‐202‐07: A Novel Method for Registration of Mid‐Treatment PET/CT Images Under Conditions of Tumor Regression for Patients with Locally Advanced Lung Cancers
Authors: Sharifi Hoda , Zhang Hong , Jin Jian‐Yyue , Kong Feng‐Ming , Chetty Indrin J , Zhong Hualiang
Published: July 2016

Purpose:

In PET‐guided adaptive radiotherapy (RT), changes in the metabolic activity at individual voxels cannot be derived until the duringtreatment CT images are appropriately registered to pre‐treatment CT images. However, deformable image registration (DIR) usually does not preserve tumor volume. This may induce errors when comparing to the target. The aim of this study was to develop a DIR‐integrated mechanical modeling technique to track radiation‐induced metabolic changes on PET images.

Methods:

Three patients with non‐small cell lung cancer (NSCLC) were treated with adaptive radiotherapy under RTOG 1106. Two PET/CT image sets were acquired 2 weeks before RT and 18 fractions after the start of treatment. DIR was performed to register the during‐RT CT to the pre‐RT CT using a B‐spline algorithm and the resultant displacements in the region of tumor were remodeled using a hybrid finite element method (FEM). Gross tumor volume (GTV) was delineated on the during‐RT PET/CT image sets and deformed using the 3D deformation vector fields generated by the CT‐based registrations. Metabolic tumor volume (MTV) was calculated using the pre‐ and during–RT image set. The quality of the PET mapping was evaluated based on the constancy of the mapped MTV and landmark comparison.

Results:

The B‐spline‐based registrations changed MTVs by 7.3%, 4.6% and −5.9% for the 3 patients and the correspondent changes for the hybrid FEM method −2.9%, 1% and 6.3%, respectively. Landmark comparisons were used to evaluate the Rigid, B‐Spline, and hybrid FEM registrations with the mean errors of 10.1 ± 1.6 mm, 4.4 ± 0.4 mm, and 3.6 ± 0.4 mm for three patients. The hybrid FEM method outperforms the B‐Spline‐only registration for patients with tumor regression

Conclusion:

The hybrid FEM modeling technique improves the B‐Spline registrations in tumor regions. This technique may help compare metabolic activities between two PET/CT images with regressing tumors.

The author gratefully acknowledges the financial support from the National Institutes of Health Grant
TU‐AB‐202‐08: Generating Organ Surfaces to Overcome Random Contouring Errors and Slice Thickness Variations On Multimodality Images
Authors: Oh S , Weiss E , Christensen G , Hugo G , Williamson J
Published: July 2016

Purpose:

To propose a new method that generates smooth organ surface meshes that overcomes random contouring errors and volume averaging artifacts for hollow organs.

Methods:

The proposed contour‐to‐smooth mesh generation method (CSMG) consists of two parts; 1) reconstructing the inner and outer surfaces of the hollow organ with triangular meshes based on parametric active surface (PAS) and 2) modifying the reconstructed inner surface in order to satisfy minimum wall thickness constraints and matching the patient's known organ wall volume. In both steps, PAS evolved gradually to physician contours by minimizing defined energy functions. CSMG surface meshes were evaluated using two digital phantoms and 72 contours from 3 cervix cancer patients (4 images per patient, 3 organs per image, 2 inner‐and‐outer‐wall contours per organ). The CSMG and treatment planning system (TPS) surface meshes were compared using Gaussian curvature, Dice conformity index (DCI) and signed minimum distance (SMD). DCI and SMD were calculated between the physician contours and the contour extracted by averaging the surface over the slice thickness.

Results:

CSMG meshes were smoother and more continuous than TPS surface meshes. The DCI differences between CSMG and TPS were 0.2%, 0.9% and 1.5% on average for bladder, rectum and vagina, respectively. TPS meshes had larger average SMDs (0.44±0.64, 0.43±0.62 and 0.42±0.61 mm for the three organs) compared to CSMG meshes (0.01±0.84, −0.05±0.93 and 0.13±0.91 mm). Larger grand mean and smaller pooled standard deviation of TPS SMDs implied that TPS overfitted the noise on contours but PM smoothed out the noise. The wall thicknesses were 2.7±1.3, 2.6±1.1 and 1.6±0.5 mm for bladder, rectum and vagina, respectively. The wall volume differed from reference volume less than ±2.5%.

Conclusion:

The proposed method reconstructs the smooth and continuous organ surface from sparsely sampled data points without introducing large systematic errors.

This work was supported in part by Varian Research agreement. The authors have the following disclosures: NIH (Williamson, Hugo, Christensen, and Weiss), Philips research agreement (Hugo and Weiss), Varian license agreement (Hugo and Weiss), Roger Koch fund (Christensen), UpToDate royalties (Weiss). There is no COI.
TU‐AB‐202‐11: Tumor Segmentation by Fusion of Multi‐Tracer PET Images Using Copula Based Statistical Methods
Authors: Lapuyade‐Lahorgue J , Ruan S , Li H , Vera P
Published: July 2016

Purpose:

Multi‐tracer PET imaging is getting more attention in radiotherapy by providing additional tumor volume information such as glucose and oxygenation. However, automatic PET‐based tumor segmentation is still a very challenging problem. We propose a statistical fusion approach to joint segment the sub‐area of tumors from the two tracers FDG and FMISO PET images.

Methods:

Non‐standardized Gamma distributions are convenient to model intensity distributions in PET. As a serious correlation exists in multi‐tracer PET images, we proposed a new fusion method based on copula which is capable to represent dependency between different tracers. The Hidden Markov Field (HMF) model is used to represent spatial relationship between PET image voxels and statistical dynamics of intensities for each modality. Real PET images of five patients with FDG and FMISO are used to evaluate quantitatively and qualitatively our method. A comparison between individual and multi‐tracer segmentations was conducted to show advantages of the proposed fusion method.

Results:

The segmentation results show that fusion with Gaussian copula can receive high Dice coefficient of 0.84 compared to that of 0.54 and 0.3 of monomodal segmentation results based on individual segmentation of FDG and FMISO PET images. In addition, high correlation coefficients (0.75 to 0.91) for the Gaussian copula for all five testing patients indicates the dependency between tumor regions in the multi‐tracer PET images.

Conclusion:

This study shows that using multi‐tracer PET imaging can efficiently improve the segmentation of tumor region where hypoxia and glucidic consumption are present at the same time. Introduction of copulas for modeling the dependency between two tracers can simultaneously take into account information from both tracers and deal with two pathological phenomena. Future work will be to consider other families of copula such as spherical and archimedian copulas, and to eliminate partial volume effect by considering dependency between neighboring voxels.
TU‐AB‐202‐12: A Novel Method to Map Endoscopic Video to CT for Treatment Planning and Toxicity Analysis in Radiation Therapy
Authors: Ingram W , Yang J , Beadle B , Wendt R , Rao A , Court L
Published: July 2016

Purpose:

Endoscopic examinations are routine procedures for head‐and‐neck cancer patients. Our goal is to develop a method to map the recorded video to CT, providing valuable information for radiotherapy treatment planning and toxicity analysis.

Methods:

We map video frames to CT via virtual endoscopic images rendered at the real endoscope's CT‐space coordinates. We developed two complementary methods to find these coordinates by maximizing real‐to‐virtual image similarity:(1)Endoscope Tracking: moves the virtual endoscope frame‐by‐frame until the desired frame is reached. Utilizes prior knowledge of endoscope coordinates, but sensitive to local optima. (2)Location Search: moves the virtual endoscope along possible paths through the volume to find the desired frame. More robust, but more computationally expensive. We tested these methods on clay phantoms with embedded markers for point mapping and protruding bolus material for contour mapping, and we assessed them qualitatively on three patient exams. For mapped points we calculated 3D‐distance errors, and for mapped contours we calculated mean absolute distances (MAD) from CT contours.

Results:

In phantoms, Endoscope Tracking had average point error=0.66±0.50cm and average bolus MAD=0.74±0.37cm for the first 80% of each video. After that the virtual endoscope got lost, increasing these values to 4.73±1.69cm and 4.06±0.30cm. Location Search had point error=0.49±0.44cm and MAD=0.53±0.28cm. Point errors were larger where the endoscope viewed the surface at shallow angles<10 degrees (1.38±0.62cm and 1.22±0.69cm for Endoscope Tracking and Location Search, respectively). In patients, Endoscope Tracking did not make it past the nasal cavity. However, Location Search found coordinates near the correct location for 70% of test frames. Its performance was best near the epiglottis and in the nasal cavity.

Conclusion:

Location Search is a robust and accurate technique to map endoscopic video to CT. Endoscope Tracking is sensitive to erratic camera motion and local optima, but could be used in conjunction with anchor points found using Location Search.
TU‐A‐206‐01: Professional Liability Insurance ‐ Protecting You and Your Practice
Authors: Fuller G.
Published: July 2016

Professional liability insurance is an important tool to manage risks to a medical physicist's assets that can result from errors or omissions in the course of professional practice. AAPM has partnered with Mercer Consumer to offer coverage tailored to the professional activities and specific risks encountered in medical physics practice. This program has specific eligibility criteria, benefit options, exclusions, and enrollment options for both self‐employed medical physicists and those who practice in an employment setting.

There are important nuances to the form of coverage chosen and the determination of the coverage period for individual policies. The need for and role of professional liability insurance varies for each individual physicist, and one of the most important factors is the physicist's employment or self‐employment status and involvement in consulting activities.

This session will provide an overview of the AAPM‐sponsored program (administered by Mercer) and the various options available to consulting and employed physicists. The fundamental concepts of professional liability insurance in general will be reviewed, and the roles of occurrence and claims‐made coverage forms within the AAPM program will be explained. Information drawn from claims data from the AAPM program will be used to illustrate the role of professional liability insurance benefits in realistic cases involving losses and liabilities due to errors and omissions in medical physics practice.

Learning Objectives:

Explain how professional liability insurance protects a medical physicist in the event of a lawsuit.

Understand the basic concepts of the professional liability insurance coverage forms: occurrence and claims‐made.

Explain AAPM's professional liability insurance program for the consulting and non‐consulting medical physicist.

Understand the recent claim/expense data from AAPM's professional liability insurance program.

Graham Fuller is an employee of Mercer Consumer.