bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.