bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐G‐304‐01: FEATURED PRESENTATION: Expanding the Knowledge Base for Data‐Driven Treatment Planning: Incorporating Patient Outcome Models
Authors: Robertson SP , Quon H , Cheng Z , Moore JA , Bowers M , McNutt TR 
Published: June 2015

Purpose:To extend the capabilities of knowledge‐based treatment planning beyond simple dose queries by incorporating validated patient outcome models.Methods:From an analytic, relational database of 684 head and neck cancer patients, 372 patients were identified having dose data for both left and right parotid glands as well as baseline and follow‐up xerostomia assessments. For each existing patient, knowledge‐based treatment planning was simulated for by querying the dose‐volume histograms and geometric shape relationships (overlap volume histograms) for all other patients. Dose predictions were captured at normalized volume thresholds (NVT) of 0%, 10%, 20, 30%, 40%, 50%, and 85% and were compared with the actual achieved doses using the Wilcoxon signed‐rank test. Next, a logistic regression model was used to predict the maximum severity of xerostomia up to three months following radiotherapy. Baseline xerostomia scores were subtracted from follow‐up assessments and were also included in the model. The relative risks from predicted doses and actual doses were computed and compared.Results:The predicted doses for both parotid glands were significantly less than the achieved doses (p < 0.0001), with differences ranging from 830 cGy ± 1270 cGy (0% NVT) to 1673 cGy ± 1197 cGy (30% NVT). The modelled risk of xerostomia ranged from 54% to 64% for achieved doses and from 33% to 51% for the dose predictions. Relative risks varied from 1.24 to 1.87, with maximum relative risk occurring at 85% NVT.Conclusions:Data‐driven generation of treatment planning objectives without consideration of the underlying normal tissue complication probability may Result in inferior plans, even if quality metrics indicate otherwise. Inclusion of complication models in knowledge‐based treatment planning is necessary in order to close the feedback loop between radiotherapy treatments and patient outcomes. Future work includes advancing and validating complication models in the context of knowledge‐based treatment planning.This work is supported by Philips Radiation Oncology Systems.
MO‐G‐304‐02: Knowledge Based DVH Prediction Using a Geometric Dose Transform
Authors: Staub D , Wang J , Jiang S 
Published: June 2015

Purpose:To demonstrate a novel method for predicting patient dose‐volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning.Methods:Our initial demonstration utilized a database of 100 prostate intensity‐modulated radiotherapy (IMRT) data‐sets. Each data‐set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data‐sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD‐DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison.Results:DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method.Conclusion:We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.
MO‐G‐304‐03: Development of Knowledge Models for IMRT Treatment Planning Utilizing a Rapid Learning Approach
Authors: Yuan L , Ge Y , Sheng Y , Yin F , Wu Q J 
Published: June 2015

Purpose:We present an implementation of the knowledge‐based planning method utilizing a rapid learning approach which enables the accurate evolution of models as new data are collected during daily clinical practice.Methods:The efficacy of knowledge‐based planning depends on the accuracy of the knowledge models. Existing methods for knowledge modeling are all based on batch training approach using a static set of training cases. In this study, we investigate a rapid learning approach to train the models using plans from daily clinical practice. A base knowledge model is first established from an existing database. Then each subsequent case continuously evaluates and updates the knowledge model. At each step, evaluation criteria including the studentized residual and the leverage are calculated. They are used to identify outliers from the current model and their causes. Different learning actions are triggered by these criteria. The efficiency and accuracy of the learning approach is quantified by the learning curve. As an initial validation, 100 cases in the pelvic region, including low‐to‐intermediate risk (n=40), high risk (n=20) prostate, and anorectal (n=40) cases are used. The model‐predicted generalized equivalent uniform dose (gEUD) for bladder and rectum are compared with clinical values by a bootstrap validation method.Results:The Median Absolute Differences between the predicted OAR gEUD and the clinical values in all three types of cases gradually decrease as increasing number of training cases are added in training. The knowledge models learned by this method reach comparable level of prediction accuracy at 2.1% of prescription dose as in batch training mode but with less training cases.Conclusion:The rapid learning approach facilitates incremental learning of knowledge models from clinical cases of multiple cancer types with improved efficiency. This approach will realize effective implementation of knowledge‐based planning in clinics where new cases and cancer types continue to accumulate.Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical Systems.
MO‐F‐CAMPUS‐J‐01: Acoustic Range Verification of Proton Beams: Simulation of Heterogeneity and Clinical Proton Pulses
Authors: Jones K , Sehgal C , Avery S 
Published: June 2015

Purpose:Through simulation, to assess acoustic‐based range verification of proton beams (protoacoustics) under clinical conditions.Methods:Pressure waves generated by the energy deposition of a 150 MeV, 8 mm FWHM pulsed pencil proton beam were numerically simulated through twoMethods:1) For a homogeneous water medium, an analytical wave‐equation solution was used to calculate the time‐dependent pressure measured at detector points surrounding the proton Bragg peak. 2) For heterogeneity studies, a CT tissue image was used to calculate the proton dose deposition and define the acoustic properties of the voxels through which numerical pressure wave propagation was simulated with the k‐Wave matlab toolbox. The simulations were used to assess the dependence of the acoustic amplitude and range‐verification accuracy on proton pulse rise time and tissue heterogeneity.Results:As the proton pulse rise time is increased from 1 to 40 µs, the amplitude of the expected acoustic emission decreases (a 60% drop distal to the Bragg peak), the central frequency of the expected signal decreases (from 45 to 6 kHz), and the accuracy of the range‐verification decreases (from <1 mm to 16 mm at 5 cm distal to the Bragg peak). For a 300 nA pulse, the expected pressure range is on the order of 0.1 Pa, which is observable with commercial detectors. For the heterogeneous medium, our test case shows that pressure waves emitted by an anterior pencil beam directed into the abdomen and detected posteriorly can determine the Bragg peak range to an accuracy of <2mm for a 1 µs proton pulse.Conclusion:For proton pulses with fast rise‐times, protoacoustics is a promising potential method for monitoring penetration depth through heterogeneous tissue. The loss of range‐verification accuracy with increasing rise‐times, however, suggests the need for comparisons to modeling to improve accuracy for slower cyclotron proton sources.
MO‐F‐CAMPUS‐J‐04: Radiation Heat Load On the MR System of the Elekta Atlantic System
Authors: Towe S , Roberts D , Overweg J , Van Lanen E 
Published: June 2015

Purpose:The Elekta Atlantic system combines a digital linear accelerator system with a 1.5T Philips MRI machine.This study aimed to assess the energy deposited within the cryostat system when the radiation beam passes through the cryostat. The cryocooler on the magnet has a cooling capacity which is about 1 Watt in excess of the cryogenic heat leak into the magnet's cold mass. A pressure‐controlled heater inside the magnet balances the excess refrigeration power such that the helium pressure in the tank is kept slightly above ambient air pressure. If radiation power is deposited in the cold mass then this heater will need less power to maintain pressure equilibrium and if the radiation heat load exceeds the excess cryocooler capacity the pressure will rise.Methods:An in‐house CAD based Monte Carlo code based on Penelope was used to model the entire MR‐Linac system to quantify the heat load on the magnet's cold mass. These results were then compared to experimental results obtained from an Elekta Atlantic system installed in UMC‐Utrecht.Results:For a field size of 25 cm × 22 cm and a dose rate of 107 mu.min‐1, the energy deposited by the radiation beam led to a reduction in heater power from 1.16 to 0.73 W. Simulations predicted a reduction to 0.69 W which is in good agreement. For the worst case field size (largest) and maximum dose rate the cryostat cooler capacity was exceeded. This resulted in a pressure rise within the system but was such that continuous irradiation for over 12 hours would be required before the magnet would start blowing off helium.Conclusion:The study concluded that the Atlantic system does not have to be duty cycle restricted, even for the worst case non‐clinical scenario and that there are no adverse effects on the MR system.Stephen Towe and David Roberts Both work for Elekta; Ezra Van Lanen works for Philips Healthcare; Johan Overweg works for Philips Innovative Technologies
MO‐F‐CAMPUS‐J‐05: Verification for Prompt Gamma Ray Imaging During Proton Boron Fusion Therapy
Authors: Shin H , Yoon D , Jung J , Kim M , Jang H , Suh T 
Published: June 2015

Purpose:The purpose of this study is to verify the acquisition of the three dimensional single photon emission computed tomography (SPECT) image using prompt gamma ray originated from proton boron fusion therapy (PBFT).Methods:The real‐time imaging system during the PBFT was simulated to acquire the tomographic image of the prompt gamma ray from treated tumor by the proton boron reaction, using Monte Carlo simulation (MCNPX). We acquired percentage depth dose (PDD) of the proton beam in the water phantom including the boron uptake region (BUR), the energy spectra of the prompt gamma ray and tomographic image which can show treated tumor regions. The prompt gamma ray image was reconstructed using maximum likelihood estimation maximization (MLEM) reconstruction algorithm with 64 projections. In addition, in order to evaluate the reconstructed image, the image profiles between BURs were extracted from the image.Results:In the PDD results, the Bragg‐peak was amplified definitely when the proton's maximum dose level was located at the BUR. This amplification is based on the generation of alpha particles. In addition, the prompt gamma ray peak of 719 keV was observed from the energy spectrum. Through the previous process, the tomographic image of prompt gamma ray from the BUR was reconstructed. The line profile was extracted from image including BURs, and it shows high signal‐to‐noise ratio.Conclusion:We confirmed that the real‐time prompt gamma ray image during the PBFT was successfully deducted, and results of quantitative image analysis show good agreement with the original pattern of the BUR. This study first verified the imaging capability of the PBFT‐SPECT system. In conclusion, the PBFT‐SPECT system can realize the treated tumor monitoring during the PBFT.
MO‐F‐CAMPUS‐I‐03: GPU Accelerated Monte Carlo Technique for Fast Concurrent Image and Dose Simulation
Authors: Becchetti M , Tian X , Segars P , Samei E 
Published: June 2015

Purpose:To develop an accurate and fast Monte Carlo (MC) method of simulating CT that is capable of correlating dose with image quality using voxelized phantoms.Methods:A realistic voxelized phantom based on patient CT data, XCAT, was used with a GPU accelerated MC code for helical MDCT. Simulations were done with both uniform density organs and with textured organs. The organ doses were validated using previous experimentally validated simulations of the same phantom under the same conditions. Images acquired by tracking photons through the phantom with MC require lengthy computation times due to the large number of photon histories necessary for accurate representation of noise. A substantial speed up of the process was attained by using a low number of photon histories with kernel denoising of the projections from the scattered photons. These FBP reconstructed images were validated against those that were acquired in simulations using many photon histories by ensuring a minimal normalized root mean square error.Results:Organ doses simulated in the XCAT phantom are within 10% of the reference values. Corresponding images attained using projection kernel smoothing were attained with 3 orders of magnitude less computation time compared to a reference simulation using many photon histories.Conclusion:Combining GPU acceleration with kernel denoising of scattered photon projections in MC simulations allows organ dose and corresponding image quality to be attained with reasonable accuracy and substantially reduced computation time than is possible with standard simulation approaches.
TU‐AB‐BRD‐04: Development of Quality Management Program
Authors: Thomadsen B. 
Published: June 2015

Current quality assurance and quality management guidelines provided by various professional organizations are prescriptive in nature, focusing principally on performance characteristics of planning and delivery devices. However, published analyses of events in radiation therapy show that most events are often caused by flaws in clinical processes rather than by device failures. This suggests the need for the development of a quality management program that is based on integrated approaches to process and equipment quality assurance. Industrial engineers have developed various risk assessment tools that are used to identify and eliminate potential failures from a system or a process before a failure impacts a customer. These tools include, but are not limited to, process mapping, failure modes and effects analysis, fault tree analysis. Task Group 100 of the American Association of Physicists in Medicine has developed these tools and used them to formulate an example risk‐based quality management program for intensity‐modulated radiotherapy. This is a prospective risk assessment approach that analyzes potential error pathways inherent in a clinical process and then ranks them according to relative risk, typically before implementation, followed by the design of a new process or modification of the existing process. Appropriate controls are then put in place to ensure that failures are less likely to occur and, if they do, they will more likely be detected before they propagate through the process, compromising treatment outcome and causing harm to the patient. Such a prospective approach forms the basis of the work of Task Group 100 that has recently been approved by the AAPM. This session will be devoted to a discussion of these tools and practical examples of how these tools can be used in a given radiotherapy clinic to develop a risk based quality management program.Learning Objectives:

Learn how to design a process map for a radiotherapy process
Learn how to perform failure modes and effects analysis analysis for a given process
Learn what fault trees are all about
Learn how to design a quality management program based upon the information obtained from process mapping, failure modes and effects analysis and fault tree analysis.
Dunscombe: Director, TreatSafely, LLC and Center for the Assessment of Radiological Sciences; Consultant to IAEA and VarianThomadsen: President, Center for the Assessment of Radiological SciencesPalta: Vice President of the Center for the Assessment of Radiological Sciences
TU‐A‐213‐01: MBAs for Medical Physicists: Business School Basics
Authors: Kim D. 
Published: June 2015

A broad survey of business schools and their potential to augment the utility of practicing medical physicists will be presented in didactic format. Topics will include: 1) business education vs. medical and scientific education, 2) the MBA degree vs. alternative degrees/certificates, 3) distinguishing features among MBA programs, 4) the MBA admissions process and student experience, 5) a description of the typical MBA curriculum and its relevance to clinical practice, and 6) themes to consider when performing a cost‐benefit analysis of whether or not to enroll in an MBA program.This session is the first of two consecutive sessions exploring the professional impact of supplementing medical physics credentials with formal business education, and is also part of a larger block of sessions concentrating on the Management, Business and Supervision aspects of Medical Physics.Learning Objectives:

To consider medical physics credentials within a broader educational and economic context.
To gain a sense of what is taught in business schools and how medical physicists might become more effective with this knowledge.
To discern market segments among MBA students and product differentiation among MBA programs.
TU‐A‐304‐01: Introduction and Workflow of Image‐Guided SBRT
Authors: Salter B. 
Published: June 2015

Increased use of SBRT and hypo fractionation 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 updated 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 or 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 (3D and 4D) and multi‐modality (CT, beam‐level X‐ray imaging, pre‐ and on‐treatment 3D/4D MRI, PET, robotic ultrasound, etc.) for reliable guidance of 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.
Discuss treatment planning and quality assurance issues specific to SBRT.
Research grant from Varian Medical Systems
TU‐A‐304‐03: Quality Assurance, Safety, and Other Practical Aspects of SBRT
Authors: Benedict S. 
Published: June 2015

Increased use of SBRT and hypo fractionation 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 updated 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 or 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 (3D and 4D) and multi‐modality (CT, beam‐level X‐ray imaging, pre‐ and on‐treatment 3D/4D MRI, PET, robotic ultrasound, etc.) for reliable guidance of 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.
Discuss treatment planning and quality assurance issues specific to SBRT.
Research grant from Varian Medical Systems