bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐G‐18C‐08: Real Time Tumor Imaging Using a Novel Dynamic Keyhole MRI Reconstruction Technique
Authors: Lee D , Greer P , Pollock S , Whelan B , Kim T , Keall P 
Published: July 2014

Purpose:To test the hypothesis that the novel Dynamic Keyhole MRI reconstruction technique can accelerate image acquisition whilst maintaining high image quality for lung cancer patients.Methods:18 MRI datasets from 5 lung cancer patients were acquired using a 3T MRI scanner. These datasets were retrospectively reconstructed using (A) The novel Dynamic Keyhole technique, (B) The conventional keyhole technique and (C) the conventional zero filling technique. The dynamic keyhole technique in MRI refers to techniques in which previously acquired k‐space data is used to supplement under sampled data obtained in real time. The novel Dynamic Keyhole technique utilizes a previously acquired a library of kspace datasets in conjunction with central k‐space datasets acquired in realtime. A simultaneously acquired respiratory signal is utilized to sort, match and combine the two k‐space streams with respect to respiratory displacement. Reconstruction performance was quantified by (1) comparing the keyhole size (which corresponds to imaging speed) required to achieve the same image quality, and (2) maintaining a constant keyhole size across the three reconstruction methods to compare the resulting image quality to the ground truth image.Results:(1) The dynamic keyhole method required a mean keyhole size which was 48% smaller than the conventional keyhole technique and 60% smaller than the zero filling technique to achieve the same image quality. This directly corresponds to faster imaging. (2) When a constant keyhole size was utilized, the Dynamic Keyhole technique resulted in the smallest difference of the tumor region compared to the ground truth.Conclusion:The dynamic keyhole is a simple and adaptable technique for clinical applications requiring real‐time imaging and tumor monitoring such as MRI guided radiotherapy. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by a factor of five compared with full k‐space methods.
WE‐G‐18C‐09: Separating Perfusion and Diffusion Components From Diffusion Weighted MRI of Rectum Tumors Based On Intravoxel Incoherent Motion (IVIM) Analysis
Authors: Tyagi N , Wengler K , Mazaheri Y , Hunt M , Deasy J , Gollub M 
Published: July 2014

Purpose:Pseudodiffusion arises from the microcirculation of blood in the randomly oriented capillary network and contributes to the signal decay acquired using a multi‐b value diffusion weighted (DW)‐MRI sequence. This effect is more significant at low b‐values and should be properly accounted for in apparent diffusion coefficient (ADC) calculations. The purpose of this study was to separate perfusion and diffusion component based on a biexponential and a segmented monoexponential model using IVIM analysisMethods.The signal attenuation is modeled as S(b) = S0[(1−f)exp(−bD) + fexp(−bD*)]. Fitting the biexponetial decay leads to the quantification of D, the true diffusion coefficient, D*, the pseudodiffusion coefficient, and f, the perfusion fraction. A nonlinear least squares fit and two segmented monoexponential models were used to derive the values for D, D*,‘and f. In the segmented approach b = 200 s/mm2 was used as the cut‐off value for calculation of D. DW‐MRI's of a rectum cancer patient were acquired before chemotherapy, before radiation therapy (RT), and 4 weeks into RT and were investigated as an example case.Results:Mean ADC for the tumor drawn on the DWI cases was 0.93, 1.0 and 1.13 10−3×mm2/s before chemotherapy, before RT and 4 weeks into RT. The mean (D.10−3 × mm2/s, D* 10−3 × mm2/s, and f %) based on biexponential fit was (0.67, 18.6, and 27.2%), (0.72, 17.7, and 28.9%) and (0.83,15.1, and 30.7%) at these time points. The mean (D, D* f) based on segmented fit was (0.72, 10.5, and 12.1%), (0.72, 8.2, and 17.4%) and (.82, 8.1, 16.5%)Conclusion:ADC values are typically higher than true diffusion coefficients. For tumors with significant perfusion effect, ADC should be analyzed at higher b‐values or separated from the perfusion component. Biexponential fit overestimates the perfusion fraction because of increased sensitivity to noise at low b‐values.
WE‐G‐9A‐01: Radiation Oncology Outcomes Informatics
Authors: Mayo C , Miller R , Sloan J , Wu Q , Howell R 
Published: July 2014

The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. “How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?” To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics.Learning Objectives:

Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database.
Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures.
Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi‐disciplinary effort to aggregate data for all patients.
Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.
TH‐A‐18A‐01: Innovation in Clinical Breast Imaging
Authors: Liu B , Yang K , Yaffe M , Chen J 
Published: July 2014

Several novel modalities have been or are on the verge of being introduced into the breast imaging clinic. These include tomosynthesis imaging, dedicated breast CT, contrast‐enhanced digital mammography, and automated breast ultrasound, all of which are covered in this course.Tomosynthesis and dedicated breast CT address the problem of tissue superimposition that limits mammography screening performance, by improved or full resolution of the 3D breast morphology. Contrast‐enhanced digital mammography provides functional information that allows for visualization of tumor angiogenesis. 3D breast ultrasound has high sensitivity for tumor detection in dense breasts, but the imaging exam was traditionally performed by radiologists. In automated breast ultrasound, the scan is performed in an automated fashion, making for a more practical imaging tool, that is now used as an adjunct to digital mammography in breast cancer screening.This course will provide medical physicists with an in‐depth understanding of the imaging physics of each of these four novel imaging techniques, as well as the rationale and implementation of QC procedures. Further, basic clinical applications and work flow issues will be discussed.Learning Objectives:

To be able to describe the underlying physical and physiological principles of each imaging technique, and to understand the corresponding imaging acquisition process.
To be able to describe the critical system components and their performance requirements.
To understand the rationale and implementation of quality control procedures, as well as regulatory requirements for systems with FDA approval.
To learn about clinical applications and understand risks and benefits/strength and weakness of each modality in terms of clinical breast imaging.
TH‐A‐19A‐01: An Open Source Software for Proton Treatment Planning in Heterogeneous Medium
Authors: Desplanques M , Wang K , Phillips J , Gueorguiev G , Baroni G , Sharp G 
Published: July 2014

Purpose:Due to its success in Radiation Oncology during the last decade, interest in proton therapy is on the rise. Unfortunately, despite the global enthusiasm in the field, there is presently no free, multiplatform and customizable Treatment Planning System (TPS) providing proton dose distributions in heterogenous medium. This restricts substantially the progress of clinical research for groups without access to a commercial Proton TPS. The latest implementation of our pencil beam dose calculation algorithm for proton beams within the 3D Slicer open‐source environment fulfills all the conditions described above.Methods:The core dose calculation algorithm is based on the Hong algorithm (1), which was upgraded with the Kanematsu theory describing the evolution of the lateral scattering of proton beamlets in heterogeneous medium. This algorithm deals with both mono‐energetic beams and Spread Out Bragg Peak (SOBP). In order to be user‐friendly, we provide a graphical user interface implemented with the Qt libraries, and visualization with the 3D Slicer medical image analysis software. Two different pencil beam algorithms were developed, and the clinical proton beam line at our facility was modeled.Results:The dose distributions provided by our algorithms were compared to dose distributions coming from both commercialized XiO TPS and literature (dose measurements, GEANT4 and MCNPx) and turned out to be in a good agreement, with maximum dose discrepancies of 5% in homogeneous phantoms and 10% in heterogeneous phantoms. The algorithm of SOBP creation from an optimized weigthing of mono‐energetic beams results in flat SOBP.Conclusion:We hope that our efforts in implementing this new, open‐source proton TPS will help the research groups to have a free access to a useful, reliable proton dose calculation software.(1) L. Hong et al., A pencil beam algorithm for proton dose calculations, Phys. Med. Biol. 41 (1996) 1305–1330.This project is paid for by NCI/MGH Federal share as a research grant.
TH‐A‐19A‐03: Impact of Proton Dose Calculation Method On Delivered Dose to Lung Tumors: Experiments in Thorax Phantom and Planning Study in Patient Cohort
Authors: Grassberger C , Daartz J , Dowdell S , Ruggieri T , Sharp G , Paganetti H 
Published: July 2014

Purpose:Evaluate Monte Carlo (MC) dose calculation and the prediction of the treatment planning system (TPS) in a lung phantom and compare them in a cohort of 20 lung patients treated with protons.Methods:A 2‐dimensional array of ionization chambers was used to evaluate the dose across the target in a lung phantom. 20 lung cancer patients on clinical trials were re‐simulated using a validated Monte Carlo toolkit (TOPAS) and compared to the TPS.Results:MC increases dose calculation accuracy in lung compared to the clinical TPS significantly and predicts the dose to the target in the phantom within ±2%: the average difference between measured and predicted dose in a plane through the center of the target is 5.6% for the TPS and 1.6% for MC. MC recalculations in patients show a mean dose to the clinical target volume on average 3.4% lower than the TPS, exceeding 5% for small fields. The lower dose correlates significantly with aperture size and the distance of the tumor to the chest wall (Spearman's p=0.0002/0.004). For large tumors MC also predicts consistently higher V5 and V10 to the normal lung, due to a wider lateral penumbra, which was also observed experimentally. Critical structures located distal to the target can show large deviations, though this effect is very patient‐specific.Conclusion:Advanced dose calculation techniques, such as MC, would improve treatment quality in proton therapy for lung cancer by avoiding systematic overestimation of target dose and underestimation of dose to normal lung. This would increase the accuracy of the relationships between dose and effect, concerning tumor control as well as normal tissue toxicity. As the role of proton therapy in the treatment of lung cancer continues to be evaluated in clinical trials, this is of ever‐increasing importance.This work was supported by National Cancer Institute Grant R01CA111590.
TH‐A‐19A‐04: Latent Uncertainties and Performance of a GPU‐Implemented Pre‐Calculated Track Monte Carlo Method
Authors: Renaud M , Roberge D , Seuntjens J 
Published: July 2014

Purpose:Assessing the performance and uncertainty of a pre‐calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre‐generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre‐generated track bank size can be selected for a desired dose calculation uncertainty.Methods:Particle tracks were pre‐generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implemented on the CUDA programming framework. GPU‐PMC dose distributions were compared to benchmark dose distributions simulated using general‐purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories.Results:GPU‐PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance‐to‐agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively.Conclusion:The GPU‐PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU‐PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams.This work was supported in part by FRSQ‐MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP‐211360).
TH‐A‐19A‐08: Intel Xeon Phi Implementation of a Fast Multi‐Purpose Monte Carlo Simulation for Proton Therapy
Authors: Souris K , Lee J , Sterpin E 
Published: July 2014

Purpose:Recent studies have demonstrated the capability of graphics processing units (GPUs) to compute dose distributions using Monte Carlo (MC) methods within clinical time constraints. However, GPUs have a rigid vectorial architecture that favors the implementation of simplified particle transport algorithms, adapted to specific tasks. Our new, fast, and multipurpose MC code, named MCsquare, runs on Intel Xeon Phi coprocessors. This technology offers 60 independent cores, and therefore more flexibility to implement fast and yet generic MC functionalities, such as prompt gamma simulations.Methods:MCsquare implements several models and hence allows users to make their own tradeoff between speed and accuracy. A 200 MeV proton beam is simulated in a heterogeneous phantom using Geant4 and two configurations of MCsquare. The first one is the most conservative and accurate. The method of fictitious interactions handles the interfaces and secondary charged particles emitted in nuclear interactions are fully simulated. The second, faster configuration simplifies interface crossings and simulates only secondary protons after nuclear interaction events. Integral depth‐dose and transversal profiles are compared to those of Geant4. Moreover, the production profile of prompt gammas is compared to PENH results.Results:Integral depth dose and transversal profiles computed by MCsquare and Geant4 are within 3%. The production of secondaries from nuclear interactions is slightly inaccurate at interfaces for the fastest configuration of MCsquare but this is unlikely to have any clinical impact. The computation time varies between 90 seconds for the most conservative settings to merely 59 seconds in the fastest configuration. Finally prompt gamma profiles are also in very good agreement with PENH results.Conclusion:Our new, fast, and multi‐purpose Monte Carlo code simulates prompt gammas and calculates dose distributions in less than a minute, which complies with clinical time constraints. It has been successfully validated with Geant4.This work has been financialy supported by InVivoIGT, a public/private partnership between UCL and IBA.
TH‐A‐19A‐09: Towards Sub‐Second Proton Dose Calculation On GPU
Authors: da Silva J 
Published: July 2014

Purpose:To achieve sub‐second dose calculation for clinically relevant proton therapy treatment plans. Rapid dose calculation is a key component of adaptive radiotherapy, necessary to take advantage of the better dose conformity offered by hadron therapy.Methods:To speed up proton dose calculation, the pencil beam algorithm (PBA; clinical standard) was parallelised and implemented to run on a graphics processing unit (GPU). The implementation constitutes the first PBA to run all steps on GPU, and each part of the algorithm was carefully adapted for efficiency. Monte Carlo (MC) simulations obtained using Fluka of individual beams of energies representative of the clinical range impinging on simple geometries were used to tune the PBA. For benchmarking, a typical skull base case with a spot scanning plan consisting of a total of 8872 spots divided between two beam directions of 49 energy layers each was provided by CNAO (Pavia, Italy). The calculations were carried out on an Nvidia Geforce GTX680 desktop GPU with 1536 cores running at 1006 MHz.Results:The PBA reproduced within ±3% of maximum dose results obtained from MC simulations for a range of pencil beams impinging on a water tank. Additional analysis of more complex slab geometries is currently under way to fine‐tune the algorithm. Full calculation of the clinical test case took 0.9 seconds in total, with the majority of the time spent in the kernel superposition step.Conclusion:The PBA lends itself well to implementation on many‐core systems such as GPUs. Using the presented implementation and current hardware, sub‐second dose calculation for a clinical proton therapy plan was achieved, opening the door for adaptive treatment. The successful parallelisation of all steps of the calculation indicates that further speedups can be expected with new hardware, brightening the prospects for real‐time dose calculation.This work was funded by ENTERVISION, European Commission FP7 grant 264552.
TH‐A‐19A‐11: Validation of GPU‐Based Monte Carlo Code (gPMC) Versus Fully Implemented Monte Carlo Code (TOPAS) for Proton Radiation Therapy: Clinical Cases Study
Authors: Giantsoudi D , Schuemann J , Dowdell S , Jia X , Jiang S , Paganetti H 
Published: July 2014

Purpose:For proton radiation therapy, Monte Carlo simulation (MCS) methods are recognized as the gold‐standard dose calculation approach. Although previously unrealistic due to limitations in available computing power, GPU‐based applications allow MCS of proton treatment fields to be performed in routine clinical use, on time scales comparable to that of conventional pencil‐beam algorithms. This study focuses on validating the results of our GPU‐based code (gPMC) versus fully implemented proton therapy based MCS code (TOPAS) for clinical patient cases.Methods:Two treatment sites were selected to provide clinical cases for this study: head‐and‐neck cases due to anatomical geometrical complexity (air cavities and density heterogeneities), making dose calculation very challenging, and prostate cases due to higher proton energies used and close proximity of the treatment target to sensitive organs at risk. Both gPMC and TOPAS methods were used to calculate 3‐dimensional dose distributions for all patients in this study. Comparisons were performed based on target coverage indices (mean dose, V90 and D90) and gamma index distributions for 2% of the prescription dose and 2mm.Results:For seven out of eight studied cases, mean target dose, V90 and D90 differed less than 2% between TOPAS and gPMC dose distributions. Gamma index analysis for all prostate patients resulted in passing rate of more than 99% of voxels in the target. Four out of five head‐neck‐cases showed passing rate of gamma index for the target of more than 99%, the fifth having a gamma index passing rate of 93%.Conclusion:Our current work showed excellent agreement between our GPU‐based MCS code and fully implemented proton therapy based MC code for a group of dosimetrically challenging patient cases.
TH‐A‐BRE‐01: The Status of Intensity Modulated Proton and Ion Therapy
Authors: Dong L , Zhu X , Unkelbach J , Schulte R 
Published: July 2014

IMRT with photons has become a radiation therapy standard of care for many cancer treatment sites. The situation is quite different with intensity modulated particle (protons and ion) radiation therapy (IMPT). With the rapid development of beam scanning techniques and many of the newer proton facilities exclusively offering active beam scanning as their radiation delivery technique, it is timely to give an update on the status and challenges of IMPT. The leading principle in IMPT is to aim at the target from several, not necessarily coplanar, directions with multiple pencil beams that are modulated in their intensity and adjusted in their energy such that a desired dose distribution or, more generally, a desired bio‐effective dose distribution is achieved. Different from low‐LET photons, the varying relative biological effectiveness (RBE) along the beam path adds an additional dimension to the treatment planning process and will require biophysical modeling at least for carbon ion therapy. IMPT involves computationally challenging tasks, yet it needs to be very fast in order to be clinically relevant. To make IMPT computationally tractable, robust and efficient optimization methods are required. Lastly, IMPT planning is very sensitive to accurate knowledge of relative stopping and scattering powers of the intervening tissues as well as intra‐ and inter‐fraction motion. Robust planning methods are being developed in order to obtain IMPT plans that are less sensitive against such uncertainties. This therapy symposium will present an update on the current status and emerging developments of IMPT from the medical physics perspective.Learning Objectives:

Become familiar with current delivery techniques for IMPT and their limitations.
Understand the basics of dose calculational algorithms and commissioning of IMPT.
Learn how to assess the accuracy of planning and delivery of IMPT treatments.
Get an overview of currently used and emerging optimization techniques.
Learn about the pro or contra arguments for using IMPT for moving targets.