bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐CD‐304‐01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Trajectory Modulated Arc Therapy: Development of Novel Arc Delivery Techniques Integrating Dynamic Table Motion for Extended Volume Treatments
Authors: Chin E , Otto K , Hoppe R , Million L , Loo B , Koong A , Xing L , Hsu A , Fahimian B 
Published: June 2015

Purpose:Integration of coordinated robotic table motion with inversely‐planned arc delivery has the potential to resolve table‐top delivery limitations of large‐field treatments such as Total Body Irradiation (TBI), Total Lymphoid Irradiation (TLI), and Cranial‐Spinal Irradiation (CSI). We formulate the foundation for Trajectory Modulated Arc Therapy (TMAT), and using Varian Developer Mode capabilities, experimentally investigate its practical implementation for such techniques.Methods:A MATLAB algorithm was developed for inverse planning optimization of the table motion, MLC positions, and gantry motion under extended‐SSD geometry. To maximize the effective field size, delivery trajectories for TMAT TBI were formed with the table rotated at 270° IEC and dropped vertically to 152.5cm SSD. Preliminary testing of algorithm parameters was done through retrospective planning analysis. Robotic delivery was programmed using custom XML scripting on the TrueBeam Developer Mode platform. Final dose was calculated using the Eclipse AAA algorithm. Initial verification of delivery accuracy was measured using OSLDs on a solid water phantom of varying thickness.Results:A comparison of DVH curves demonstrated that dynamic couch motion irradiation was sufficiently approximated by static control points spaced in intervals of less than 2cm. Optimized MLC motion decreased the average lung dose to 68.5% of the prescription dose. The programmed irradiation integrating coordinated table motion was deliverable on a TrueBeam STx linac in 6.7 min. With the couch translating under an open 10cmx20cm field angled at 10°, OSLD measurements along the midline of a solid water phantom at depths of 3, 5, and 9cm were within 3% of the TPS AAA algorithm with an average deviation of 1.2%.Conclusion:A treatment planning and delivery system for Trajectory Modulated Arc Therapy of extended volumes has been established and experimentally demonstrated for TBI. Extension to other treatment techniques such as TLI and CSI is readily achievable through the developed platform.Grant Funding by Varian Medical Systems.
TU‐CD‐BRD‐04: UCLA Experience, with Focus On Developing Metrics and Using RO‐ILS
Authors: Beron P. 
Published: June 2015

It has long been standard practice in radiation oncology to report internally when a patient's treatment has not gone as planned and to report events to regulatory agencies when legally required. Most potential errors are caught early and never affect the patient. Quality assurance steps routinely prevent errors from reaching the patient, and these “near misses” are much more frequent than treatment errors. A growing number of radiation oncology facilities have implemented incident learning systems to report and analyze both errors and near misses. Using the term “incident learning” instead of “event reporting” emphasizes the need to use these experiences to change the practice and make future errors less likely and promote an educational, non‐punitive environment.There are challenges in making such a system practical and effective. Speakers from institutions of different sizes and practice environments will share their experiences on how to make such a system work and what benefits their clinics have accrued. Questions that will be addressed include:

How to create a system that is easy for front line staff to access
How to motivate staff to report
How to promote the system as positive and educational and not punitive or demeaning
How to organize the team for reviewing and responding to reports
How to prioritize which reports to discuss in depth
How not to dismiss the rest
How to identify underlying causes
How to design corrective actions and implement change
How to develop useful statistics and analysis tools
How to coordinate a departmental system with a larger risk management system
How to do this without a dedicated quality manager
Some speakers’ experience is with in‐house systems and some will share experience with the AAPM/ASTRO national Radiation Oncology Incident Learning System (RO‐ILS). Reports intended to be of value nationally need to be comprehensible to outsiders; examples of useful reports will be shown.There will be ample time set aside for audience members to contribute to the discussion.Learning Objectives:

Learn how to promote the use of an incident learning system in a clinic.
Learn how to convert “event reporting” into “incident learning”.
See examples of practice changes that have come out of learning systems.
Learn how the RO‐ILS system can be used as a primary internal learning system.
Learn how to create succinct, meaningful reports useful to outside readers.
Gary Ezzell chairs the AAPM committee overseeing RO‐ILS and has received an honorarium from ASTRO for working on the committee reviewing RO‐ILS reports.Derek Brown is a director of TreatSafely.org.Brett Miller has previously received travel expenses and an honorarium from Varian.Phillip Beron has nothing to report.
TU‐CD‐BRB‐04: Automated Radiomic Features Complement the Prognostic Value of VASARI in the TCGA‐GBM Dataset
Authors: Velazquez E Rios , Narayan V , Grossmann P , Dunn W , Gutman D , Aerts H 
Published: June 2015

Purpose:To compare the complementary prognostic value of automated Radiomic features to that of radiologist‐annotated VASARI features in TCGA‐GBM MRI dataset.Methods:For 96 GBM patients, pre‐operative MRI images were obtained from The Cancer Imaging Archive. The abnormal tumor bulks were manually defined on post‐contrast T1w images. The contrast‐enhancing and necrotic regions were segmented using FAST. From these sub‐volumes and the total abnormal tumor bulk, a set of Radiomic features quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the post‐contrast T1w images. Minimum‐redundancy‐maximum‐relevance (MRMR) was used to identify the most informative Radiomic, VASARI and combined Radiomic‐VASARI features in 70% of the dataset (training‐set). Multivariate Cox‐proportional hazards models were evaluated in 30% of the dataset (validation‐set) using the C‐index for OS. A bootstrap procedure was used to assess significance while comparing the C‐Indices of the different models.Results:Overall, the Radiomic features showed a moderate correlation with the radiologist‐annotated VASARI features (r = −0.37 – 0.49); however that correlation was stronger for the Tumor Diameter and Proportion of Necrosis VASARI features (r = −0.71 – 0.69). After MRMR feature selection, the best‐performing Radiomic, VASARI, and Radiomic‐VASARI Cox‐PH models showed a validation C‐index of 0.56 (p = NS), 0.58 (p = NS) and 0.65 (p = 0.01), respectively. The combined Radiomic‐VASARI model C‐index was significantly higher than that obtained from either the Radiomic or VASARI model alone (p = <0.001).Conclusion:Quantitative volumetric and textural Radiomic features complement the qualitative and semi‐quantitative annotated VASARI feature set. The prognostic value of informative qualitative VASARI features such as Eloquent Brain and Multifocality is increased with the addition of quantitative volumetric and textural features from the contrast‐enhancing and necrotic tumor regions. These results should be further evaluated in larger validation cohorts.
TU‐CD‐BRA‐01: A Novel 3D Registration Method for Multiparametric Radiological Images
Authors: Akhbardeh A , Parekth VS , Jacobs MA 
Published: June 2015

Purpose:Multiparametric and multimodality radiological imaging methods, such as, magnetic resonance imaging(MRI), computed tomography(CT), and positron emission tomography(PET), provide multiple types of tissue contrast and anatomical information for clinical diagnosis. However, these radiological modalities are acquired using very different technical parameters, e.g.,field of view(FOV), matrix size, and scan planes, which, can lead to challenges in registering the different data sets. Therefore, we developed a hybrid registration method based on 3D wavelet transformation and 3D interpolations that performs 3D resampling and rotation of the target radiological images without loss of informationMethods:T1‐weighted, T2‐weighted, diffusion‐weighted‐imaging(DWI), dynamic‐contrast‐enhanced(DCE) MRI and PET/CT were used in the registration algorithm from breast and prostate data at 3T MRI and multimodality(PET/CT) cases. The hybrid registration scheme consists of several steps to reslice and match each modality using a combination of 3D wavelets, interpolations, and affine registration steps. First, orthogonal reslicing is performed to equalize FOV, matrix sizes and the number of slices using wavelet transformation. Second, angular resampling of the target data is performed to match the reference data. Finally, using optimized angles from resampling, 3D registration is performed using similarity transformation(scaling and translation) between the reference and resliced target volume is performed. After registration, the mean‐square‐error(MSE) and Dice Similarity(DS) between the reference and registered target volumes were calculated.Results:The 3D registration method registered synthetic and clinical data with significant improvement(p<0.05) of overlap between anatomical structures. After transforming and deforming the synthetic data, the MSE and Dice similarity were 0.12 and 0.99. The average improvement of the MSE in breast was 62%(0.27 to 0.10) and prostate was 63%(0.13 to 0.04;p<0.05). The Dice similarity was in breast 8%(0.91 to 0.99) and for prostate was 89%(0.01 to 0.90;p<0.05)Conclusion:Our 3D wavelet hybrid registration approach registered diverse breast and prostate data of different radiological images(MR/PET/CT) with a high accuracy.
TU‐CD‐BRA‐02: Comparing Mutual Information and Gradient Magnitude Metrics for Deformable Image Registration
Authors: Gertsenshteyn I , Tyagi N , Farjam R , Apte A , Sharp G 
Published: June 2015

Purpose:Deformable image registration between CT and MRI remains a challenge due to variability in contrast, homogeneity, and field of view. This study aims to determine whether an optimized Mutual Information (MI) or Gradient Magnitude (GM) metric provides favorable results in the head and neck region.Methods:Head and neck CT and MR images were acquired for 14 patients as part of an MRI simulation study with a dual echo mDixon (in phase) T1 fast field echo sequence. Each pair of scans is registered with MRI as the reference image using the GM and MI metrics, where MI was optimized by controlling histogram parameters. Results are observed visually, and quantitatively measured by comparing Dice coefficient and Hausdorff distance of manually contoured bone structures.Results:Using the optimized GM metric, the average Dice coefficient is 0.52 ± 0.16; the average Hausdorff distance is 7.5 ± 1.9 mm. Using the optimized MI metric, the average Dice coefficient is 0.55 ± 0.085; the average Hausdorff distance is 6.6 ± 1.4 mm.Conclusion:While both metrics performed similarly, the MI metric has a higher Dice coefficient than the GM metric, and a lower Hausdorff distance. This suggests image registration with the Mutual Information metric may be preferred.
TU‐CD‐BRA‐03: Left Ventricle Segmentation with a Coupled Length Regularization and Sparse Shape Prior
Authors: Liu W , Ruan D 
Published: June 2015

Purpose:To perform accurate segmentation on left ventricles from Cine MR imagesMethods:We have developed a novel variational segmentation method that incorporates prior knowledge on both geometrical relations and shapes of the endocardium and epicardium. In contrast to conventional approaches that preserve a constant distance between the endo‐ and epicardial contours, we propose to maintain a smoothly varying distance between the endo‐ and epicardium represented by two separate level set functions, which is more robust to shape variations across slices and phases among different patients. The coupled level set representation further allows us to incorporate a sparse composite shape prior to boost the performance. A robust data fidelity of Gaussian mixture is utilized to represent overlapped intensity distributions of each cardiac region under the condition of insufficient gradient information. We evaluated the proposed method on datasets from MICCAI left ventricle segmentation challenge, and compared our method against other state‐of‐the‐art approaches based on Dice similarity coefficient (DSC).Results:Our method successfully segmented all left ventricles in 15 validation datasets from patients of different pathologies. It achieved competitive if not better DSC accuracy compared to other state‐of‐the‐art methods, with mean DSC of 0.89 and standard deviation of 0.04 on endocardium segmentation, and mean DSC of 0.94 and standard deviation of 0.01 on epicardium segmentation.Conclusion:Introducing anatomy‐specific geometric coupling and sparse composite shape priors on endo‐ and epicardium, in addition to length regularization, into the variational method has demonstrated its advantage in the challenging problem of left ventricle segmentation. A joint left and right ventricle segmentation method is under development.
TU‐CD‐BRA‐07: A Comparison of Demons Image Registration Algorithms to Monitor Longitudinal Changes in Knee Cartilage: Data From the OsteoArthritis Initiative (OAI)
Authors: Hoang U , Sinha U , Sinha U 
Published: June 2015

Purpose:To develop accurate tools to quantify small and localized changes in cartilage morphology to facilitate comparisons between patient cohorts with varying degrees of osteoarthritis (OA) as well as to track longitudinal changes(normal progression and response to treatment).Methods:We implemented and compared the registration accuracy of four variants of the demons algorithm on magnetic resonance cartilage image volumes. The algorithms are four variants of the Demon's algorithm: symmetric simple demons (SMSD), symmetric evolved demons (SMED), inverse‐consistent simple demons (ICSD), and inverse‐consistent evolved demons (ICED). The registration algorithms were also evaluated for the accuracy of the average Jacobians. The average Jacobian of the cartilage was compared to the ratio of volume change for validation. Evaluation was performed on 36 subjects using the baseline and later time point images acquired after 12 months.Results:The symmetric evolved demons algorithm provided the best in registration accuracy evaluated using quantitative metrics of mean squared error and voxel overlap. The symmetric simple demons and symmetric evolved demons performed equally well in terms of the Jacobians.Conclusion:Approximately 27 millions adults age 25 and older diagnosed with OA; however, cartilage loss with disease progression is small and localized to sub‐regions of the cartilage. It is desirable to develop accurate methods that can automatically extract measures of cartilage volume and thickness (global and local). The techniques developed here will be used, in future studies, to explore differences in cohorts segregated by disease severity and correlation of local changes to clinical variables.
TU‐CD‐BRA‐12: Coupling PET Image Restoration and Segmentation Using Variational Method with Multiple Regularizations
Authors: Li L , Lu W , Tan S 
Published: June 2015

Purpose:To propose a new variational method which couples image restoration with tumor segmentation for PET images using multiple regularizations.Methods:Partial volume effect (PVE) is a major degrading factor impacting tumor segmentation accuracy in PET imaging. The existing segmentation methods usually need to take prior calibrations to compensate PVE and they are highly system‐dependent. Taking into account that image restoration and segmentation can promote each other and they are tightly coupled, we proposed a variational method to solve the two problems together. Our method integrated total variation (TV) semi‐blind deconvolution and Mumford‐Shah (MS) segmentation. The TV norm was used on edges to protect the edge information, and the L₂ norm was used to avoid staircase effect in the no‐edge area. The blur kernel was constrained to the Gaussian model parameterized by its variance and we assumed that the variances in the X‐Y and Z directions are different. The energy functional was iteratively optimized by an alternate minimization algorithm. Segmentation performance was tested on eleven patients with non‐Hodgkin's lymphoma, and evaluated by Dice similarity index (DSI) and classification error (CE). For comparison, seven other widely used methods were also tested and evaluated.Results:The combination of TV and L₂ regularizations effectively improved the segmentation accuracy. The average DSI increased by around 0.1 than using either the TV or the L₂ norm. The proposed method was obviously superior to other tested methods. It has an average DSI and CE of 0.80 and 0.41, while the FCM method — the second best one — has only an average DSI and CE of 0.66 and 0.64.Conclusion:Coupling image restoration and segmentation can handle PVE and thus improves tumor segmentation accuracy in PET. Alternate use of TV and L2 regularizations can further improve the performance of the algorithm.This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant No.61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.
TU‐EF‐303‐01: Altered Mechanical Poperties of Cells and Nuclei in Cancer
Authors: Rowat A. 
Published: June 2015

Many medical physics diagnostic and therapeutic developments have significant overlap with biophysical approaches to measuring and modeling molecular and cellular systems. Although these methods may differ dramatically in terms of temporal and spatial scales, many of the underlying concepts are identical, suggesting potential synergies across the scales. For example, while clinical imaging is typically in the (sub)millimeter and second regime, biophysical imaging technologies reach sub‐nanometer and sub‐picosecond scales. Despite these scale differences, the use of molecular labeling, resulting large datasets, and need for quantitative analysis methods are similar, and solutions developed for one application are easily translated to others. Similarly, mathematical models used to understand clinical responses are often based on molecular and cellular networks that have been elucidated by biophysical studies. In turn, such models are directly useful for guiding on‐going biophysical and biomedical research. Relevant biophysical applications are found in the cancer, neuroscience, and cardiac fields, which are also research focus areas for many AAPM members. Presentations by physicists working in biophysics will explore potential synergies with diagnostic and therapeutic medical physics, as well as help identify areas of common interest that would be ripe for novel collaborations.Learning Objectives:

1.To learn about differences in temporal and spatial scales of medical physics and biophysics
To learn about common methods used in medical physics and biophysics
To learn about synergistic opportunities between medical physics and biophysics
TU‐EF‐210‐05: A Fast and Efficient Method for Determining Coagulation Temperatures of Tissue‐Mimicking Thermal Therapy Gel Phantoms: Validated by Magnetic Resonance Thermometry
Authors: Brodin P , Partanen A , Asp P , Branch C , Guha C , Tome W 
Published: June 2015

Purpose:To develop a simple and efficient method for determining coagulation temperatures of transparent thermal therapy gel phantoms. Tissue‐mimicking thermal therapy phantoms (TMTTPs) that coagulate at specific temperatures are valuable tools for developing and evaluating treatment strategies related to therapy using focused ultrasound, laser, microwave or radiofrequency ablation. The specific formula for a TMTTP will depend on the application and as such finding the coagulation temperature for a specific TMTTP is crucial.Methods:We used a previously published TMTTP formula with 2% (w/v) of bovine serum albumin as the temperature sensitive protein. Using the programmable heating settings of a polymerase chain reaction (PCR) machine we heated 50 µl gel samples to various temperatures for 3 min and imaged them to determine the coagulation temperature by quantifying the resulting opacity. These measured coagulation temperatures were then validated using high‐intensity focused ultrasound (HIFU) heating and magnetic resonance thermometry and T2 mapping using a clinical MR‐HIFU system.Results:The PCR heating method produced consistent and reproducible coagulation of the gel samples, and the coagulation temperature was determined by a 7.5% increase in opacity compared to non‐coagulated gel samples. The resulting coagulation temperatures for TMTTPs of increasing pH levels were found to be 43.6 ± 0.4 °C, 53.1 ± 0.8 °C and 60.1 ± 1.0 °C for a pH of 4.25, 4.50 and 4.75, respectively. The corresponding coagulation temperatures determined by MR thermometry were within the range of 50 to 55 °C for pH 4.50 and within 58 to 66 °C for pH 4.75.Conclusion:The PCR heating method provides a fast and reproducible measurement of the coagulation temperature of transparent TMTTPs. The temperatures determined using this method were well within the range of coagulation temperatures determined using the MR‐thermometry validation.Dr. Ari Partanen is a paid employee of Philips Healthcare
TU‐F‐BRB‐01: Resolving and Characterizing Breathing Motion for Radiotherapy with MRI
Authors: Tryggestad E. 
Published: June 2015

The current clinical standard of organ respiratory imaging, 4D‐CT, is fundamentally limited by poor soft‐tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose‐volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft‐tissue contrast. MRI‐based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR‐based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D‐MRI and MRI hybrid treatment machines (i.e. MR‐linac or MR‐Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI‐based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR‐based motion management methods and technologies, the state‐of‐the‐art concerning MRI development and clinical application and the barriers to more widespread adoption.Learning Objectives:

Discuss the need of MR‐based motion management for improving patient care in radiotherapy.
Understand MR techniques for motion imaging and tumor motion characterization.
Understand the current state of the art and future steps for clinical integration.
Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant.