bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐A‐12A‐11: Improved Airway Measurement Accuracy for Low Dose Quantitative CT (qCT) Using Statistical (ASIR), at Reduced DFOV, and High Resolution Kernels in a Phantom and Swine Model
Authors: Rodriguez A , Ranallo F , Judy P , Gierada D , Fain S 
Published: July 2014

Purpose:To improve the accuracy of airway measures and reduce x‐ray dose in quantitative CT (qCT) of the lung.Methods:qCT was performed on an airway phantom (COPD gene) and a swine model. The airway phantom was used to identify expected improvements in spatial resolution and noise tradeoffs for choice of CT reconstruction kernel and display FOV (DFOV).Results in the phantom were then applied to the swine model. Images were obtained via helical scans that were performed using a GE Discovery CT750 HD with the COPD Gene/SARP protocols except tube current was varied from 790 to 50 mA, and lung inflation was fixed via mechanical ventilator. CT data were reconstructed using STANDARD (Std) and Bone reconstruction kernels for FBP and 100% ASIR at half and full DFOV. Inner Diameter(ID) and wall area percent (WA%) of selected airway sizes were matched across the swine lung and phantom and performed using Airway Inspector (Surgical Planning Laboratory, Brigham and Women's Hospital, Boston, MA). For the swine lung the CT acquisition at 790 mA with ASIR and Bone kernel at half DFOV parameters was the reference standard for comparison to the lower dose scans. Results:The accuracy of the ID and WA% improved by ∼24% and ∼23% for half DFOV with the Bone kernel and ASIR reconstruction for both phantom and swine models for all airways investigated. For low dose qCT, the difference between the reference standard measures and the measures obtained with half DFOV with Bone+ASIR remained much lower than those using current clinical parameters (FBP at full DFOV) even at one fourth the dose for all airways investigated as is shown in Table 1.Conclusion:The combination of ASIR with higher spatial resolution reconstruction shows promise to reduce x‐ray dose and improve qCT of lung airways.NIBIB PB EB 1010 159 JKS Recovery Quantitative Imaging Biomarker Alliance (QIBA) U10 HL109168 SARP
TU‐C‐BRE‐06: Effect of Implementing In‐House Treatment Couch Model On Patient Specific QA for Pinnacle SmartArc Treatment Plans
Authors: Ellis A , Jacqmin D , McDonald D , Peng J , Koch N , Ashenafi M , Vanek K 
Published: July 2014

Purpose:Failure to model the treatment couch during VMAT QA planar dose calculation may Result in discrepancies between measured and calculated dose. These discrepancies are due to beam attenuation by the treatment couch that is not included in dose calculation. This work evaluates effects of accounting for this attenuation on patient specific VMAT QA results using an in‐house created Varian Exact couch model in PinnaclMethods:Patient specific VMAT QA results for 13 Pinnacle SmartArc plans generated for treatment on a Varian iX accelerator were studied. These plans included 3 treatment sites (7 H' N, 5 brain, 1 prostate). A Pinnacle model for Varian Exact couch was created in‐house to replace the CT simulator couch. Composite arc planar doses were calculated with no couch present (NC) and with the Exact couch model (CM) in place for each plan. QA measurements were taken using IBA Matrixx Evolution ion chamber array set up in IBA MultiCube and were compared to each planar dose. Gamma passing criteria of both 3%/3mm and 2%/2mm tolerances were used.Results:Over all treatment sites, increases in gamma passing rates from NC to CM ranged from ‐0.4% to +27.3% at 3%/3mm and +0.1% to +30.5% at 2%/2mm. Mean increases in passing rates were +3.7% and +5.3% for 3%/3mm and 2%/2mm tolerances, respectively. Site‐specific mean increases (NC to CM) in gamma passing rates were +4.4%, +3.4%, +0.4% (3%/3mm tolerance) and +6.9%, +3.7%, and +2.9% at (2%/2mm tolerance) for H' N, brain, and prostate, respectively.Conclusion:Results support use of a couch model when generating planar dose for patient specific VMAT QA analysis. The improvements were most noticeable at 2%/2mm tolerance and for the H' N and brain sites. Eliminating treatment couch beam attenuation as a source of discrepancy in QA measurements may improve the ability to recognize otherwise masked delivered dose errors.
TU‐C‐BRE‐08: IMRT QA: Selecting Meaningful Gamma Criteria Based On Error Detection Sensitivity
Authors: Steers J , Fraass B 
Published: July 2014

Purpose:To develop a strategy for defining meaningful tolerance limits and studying the sensitivity of IMRT QA gamma criteria by inducing known errors in QA plans.Methods:IMRT QA measurements (ArcCHECK, Sun Nuclear) were compared to QA plan calculations with induced errors. Many (>24) gamma comparisons between data and calculations were performed for each of several kinds of cases and classes of induced error types with varying magnitudes (e.g. MU errors ranging from ‐10% to +10%), resulting in over 3,000 comparisons. Gamma passing rates for each error class and case were graphed against error magnitude to create error curves in order to represent the range of missed errors in routine IMRT QA using various gamma criteria.Results:This study demonstrates that random, case‐specific, and systematic errors can be detected by the error curve analysis. Depending on location of the peak of the error curve (e.g., not centered about zero), 3%/3mm threshold=10% criteria may miss MU errors of up to 10% and random MLC errors of up to 5 mm. Additionally, using larger dose thresholds for specific devices may increase error sensitivity (for the same X%/Ymm criteria) by up to a factor of two. This analysis will allow clinics to select more meaningful gamma criteria based on QA device, treatment techniques, and acceptable error tolerances.Conclusion:We propose a strategy for selecting gamma parameters based on the sensitivity of gamma criteria and individual QA devices to induced calculation errors in QA plans. Our data suggest large errors may be missed using conventional gamma criteria and that using stricter criteria with an increased dose threshold may reduce the range of missed errors. This approach allows quantification of gamma criteria sensitivity and is straightforward to apply to other combinations of devices and treatment techniques.
TU‐C‐BRE‐11: 3D EPID‐Based in Vivo Dosimetry: A Major Step Forward Towards Optimal Quality and Safety in Radiation Oncology Practice
Authors: Mijnheer B , Mans A , Olaciregui‐Ruiz I , Rozendaal R , Spreeuw H , van Herk M 
Published: July 2014

Purpose:To develop a 3D in vivo dosimetry method that is able to substitute pre‐treatment verification in an efficient way, and to terminate treatment delivery if the online measured 3D dose distribution deviates too much from the predicted dose distribution.Methods:A back‐projection algorithm has been further developed and implemented to enable automatic 3D in vivo dose verification of IMRT/VMAT treatments using a‐Si EPIDs. New software tools were clinically introduced to allow automated image acquisition, to periodically inspect the record‐and‐verify database, and to automatically run the EPID dosimetry software. The comparison of the EPID‐reconstructed and planned dose distribution is done offline to raise automatically alerts and to schedule actions when deviations are detected. Furthermore, a software package for online dose reconstruction was also developed. The RMS of the difference between the cumulative planned and reconstructed 3D dose distributions was used for triggering a halt of a linac.Results:The implementation of fully automated 3D EPID‐based in vivo dosimetry was able to replace pre‐treatment verification for more than 90% of the patient treatments. The process has been fully automated and integrated in our clinical workflow where over 3,500 IMRT/VMAT treatments are verified each year. By optimizing the dose reconstruction algorithm and the I/O performance, the delivered 3D dose distribution is verified in less than 200 ms per portal image, which includes the comparison between the reconstructed and planned dose distribution. In this way it was possible to generate a trigger that can stop the irradiation at less than 20 cGy after introducing large delivery errors.Conclusion:The automatic offline solution facilitated the large scale clinical implementation of 3D EPID‐based in vivo dose verification of IMRT/VMAT treatments; the online approach has been successfully tested for various severe delivery errors.
TU‐C‐BRF‐01: Innovation in Medical Physics and Engineering
Authors: Mohan R , Pelc N , Jaffray D , Mackie T 
Published: July 2014

We seek to heighten the awareness of the role of research and innovation that leads to clinical advances in the field of medical physics and engineering. Marie Curie (discovery and use of radium) and Harold Johns (Co‐60 tele‐therapy) in radiotherapy, and pioneers in imaging (Allan Cormack and Godfrey Hounsfield for the CT and Paul Lauterbur, Peter Mansfield for MRI, etc.) were scientists often struggling against great odds. Examples of more recent innovations that are clearly benefitting our patients include IMRT, Image Guided Radiation Therapy and Surgery, Particle Therapy, Quantitative imaging, amongst others.We would also like to highlight the fact that not all of the discovery and engineering that we benefit from in today’s world, was performed at research institutions alone. Rather, companies often tread new ground at financial and reputational risk. Indeed the strength of the private sector is needed in order to bring about new advances to our practice. The keys to long term success in research and development may very well include more public and private research spending. But, when more investigators are funded, we also need to recognize that there needs to be a willingness on the part of the funding institutions, academic centers and investigators to risk failure for the greater potential achievements in innovation and research. The speakers will provide examples and insight into the fields of innovation and research in medical physics from their own first hand experiences.Learning Objectives:

To obtain an understanding of the importance of research and development towards advances in physics in medicine.
To raise awareness of the role of interdisciplinary collaborations in translational research and innovation.
To highlight the importance of entrepreneurships and industrial‐institutional research partnerships in fostering new ideas and their commercial success.
To recognize and account for the risk of failure for the greater potential achievements in innovation and research.
TU‐C‐17A‐01: A Data‐Based Development for Pratical Pareto Optimality Assessment and Identification
Authors: Ruan D , Qi S , DeMarco J , Kupelian P , Low D 
Published: July 2014

Purpose:To develop an efficient Pareto optimality assessment scheme to support plan comparison and practical determination of best‐achievable practical treatment plan goals.Methods:Pareto efficiency reflects the tradeoffs among competing target coverage and normal tissue sparing in multi‐criterion optimization (MCO) based treatment planning. Assessing and understanding Pareto optimality provides insightful guidance for future planning. However, current MCO‐driven Pareto estimation makes relaxed assumptions about the Pareto structure and insufficiently account for practical limitations in beam complexity, leading to performance upper bounds that may be unachievable. This work proposed an alternative data‐driven approach that implicitly incorporates the practical limitations, and identifies the Pareto frontier subset by eliminating dominated plans incrementally using the Edgeworth Pareto hull (EPH). The exactness of this elimination process also permits the development of a hierarchical procedure for speedup when the plan cohort size is large, by partitioning the cohort and performing elimination in each subset before a final aggregated elimination. The developed algorithm was first tested on 2D and 3D where accuracy can be reliably assessed. As a specific application, the algorithm was applied to compare systematic plan quality for lower head‐and‐neck, amongst 4 competing treatment modalities.Results:The algorithm agrees exactly with brute‐force pairwise comparison and visual inspection in low dimensions. The hierarchical algorithm shows sqrt(k) folds speedup with k being the number of data points in the plan cohort, demonstrating good efficiency enhancement for heavy testing tasks. Application to plan performance comparison showed superiority of tomotherapy plans for the lower head‐and‐neck, and revealed a potential nonconvex Pareto frontier structure.Conclusion:An accurate and efficient scheme to identify Pareto frontier from a plan cohort has been developed. This implementation would guide generating good yet practically achievable plan quality goals for further planning. The observation of a systematic performance bias and a nonconvex Pareto frontier warrants further investigation.
TU‐C‐17A‐02: An Efficient Method for Making IMRT Treatment Planning Optimizations Results Robust Against Geometrical Uncertainties
Authors: Xie Y , Chen Y , Wickerhauser M , Deasy J 
Published: July 2014

Purpose:Systematic and random geometrical shifts of target and normal tissues can lead to delivered doses being significantly worse than planned dose distributions. We developed new methods to make treatment plans robust against geometrical uncertainties.Methods:We used prioritized prescription algorithms as a basis for development. Under the assumption that each structure moves rigidly, a 3‐dimentional vector with a certain probability distribution was used to characterize the position of each structure. One important metric (e.g. mean dose for bladder) was selected to characterize the treatment quality for each structure. An additional optimization step was added to optimize both the mean of each priority Objectives: function as well as the variance values, with respect to the uncertainty probability distributions, while retaining the good quality of the initial optimization results. This method was tested on 10 de‐identified prostate treatment cases.Results:Our optimization algorithm gave very good robust treatment plans consistently for the tested 10 prostate treatment cases, with almost the same robust quality as the traditional CTV‐PTV method on targets but with much lower and more robust doses to sensitive structures, especially on structures very close or overlapping with PTV (the rectum and bladder). The method is computationally efficient: in average the method adds only 3 minutes to the overall optimization time (less than 5 minutes total time on a quad‐core processor.)Conclusion:An optimization algorithm, compatible with prioritized (or lexicographic) optimization, was designed to generate robust treatment plans against motion uncertainties. The method optimizes both the mean and variance values of relevant dose metrics (e.g. bladder mean dose) for all interesting structures. This new algorithm theoretically works for any existing IMRT optimization algorithm and thus has great potential to improve treatment plan robustness.
TU‐C‐17A‐05: Dose Domain Optimization of MLC Leaf Patterns for Highly Complicated 4Ï€ IMRT Plans
Authors: Nguyen D , Yu V , Ruan D , Semwal H , O’Connor D , Cao M , Low D , Sheng K 
Published: July 2014

Purpose:Highly conformal non‐coplanar 4π radiotherapy plans typically require more than 20 intensity‐modulated fields to deliver. A novel method to calculate multileaf collimator (MLC) leaf patterns is introduced to maximize delivery efficiency, accuracy and plan quality.Methods:4 GBM patients, with a prescription dose of 59.4 Gy or 60 Gy, were evaluated using the 4π algorithm using 20 beams. The MLC calculation utilized a least square minimization of the dose distribution, with an anisotropic total variation regularization term to encourage piecewise continuity in the fluence maps. Transforming the fluence to the dose domain required multiplying the fluence with a sparse matrix. Exploiting this property made it feasible to solve the problem using CVX, a MATLAB‐based convex modeling framework. The fluence was stratified into even step sizes, and the MLC segments, limited to 300, were calculated. The patients studied were replanned using Eclipse with the same beam angles.Results:Compared to the original 4π plan, the stratified 4π plan increased the maximum/mean dose for, in Gy, by 1.0/0.0 (brainstem), 0.5/0.2 (chiasm), 0.0/0.0 (spinal cord), 1.9/0.3 (L eye), 0.7/0.2 (R eye), 0.4/0.4 (L lens), 0.3/0.3 (R lens), 1.0/0.8 (L Optical Nerve), 0.5/0.3 (R Optical Nerve), 0.3/0.2 (L Cochlea), 0.1/0.1 (R Cochlea), 4.6/0.2 (brain), 2.4/0.1 (brain‐PTV), 5.1/0.9 (PTV). Compared to Eclipse, which generated an average of 607 segments, the stratified plan reduced (−) or increased (+) the maximum/mean dose, in Gy, by −10.2/−4.1 (brainstem), −10.5/−8.9 (chiasm), +0.0/−0.1 (spinal cord), −4.9/−3.4 (L eye), −4.1/−2.5 (R eye), −2.8/−2.7 (L lens), −2.1/−1.9 (R lens), −7.6/−6.5 (L Optical Nerve), −8.9/−6.1 (R Optical Nerve), −1.3/−1.9 (L Cochlea), −1.8/−1.8 (R Cochlea), +1.7/−2.1 (brain), +3.2/−2.6 (brain‐PTV), +1.8/+0.3 Gy (PTV. The stratified plan was also more homogeneous in the PTV.Conclusion:This novel solver can transform complicated fluence maps into significantly fewer deliverable MLC segments than the commercial system while achieving superior dosimetry.Funding support partially contributed by Varian.
TU‐C‐17A‐07: FusionARC Treatment with Adaptive Beam Selection Method
Authors: Kim H , Li R , Lee R , Xing L 
Published: July 2014

Purpose:Recently, a new treatment scheme, FusionARC, has been introduced to compensate for the pitfalls in single‐arc VMAT planning. It basically allows for the static field treatment in selected locations, while the remaining is treated by single‐rotational arc delivery. The important issue is how to choose the directions for static field treatment. This study presents an adaptive beam selection method to formulate fusionARC treatment scheme.Methods:The optimal plan for single‐rotational arc treatment is obtained from two‐step approach based on the reweighted total‐variation (TV) minimization. To choose the directions for static field treatment with extra segments, a value of our proposed cost function at each field is computed on the new fluence‐map, which adds an extra segment to the designated field location only. The cost function is defined as a summation of equivalent uniform dose (EUD) of all structures with the fluence‐map, while assuming that the lower cost function value implies the enhancement of plan quality. Finally, the extra segments for static field treatment would be added to the selected directions with low cost function values. A prostate patient data was applied and evaluated with three different plans: conventional VMAT, fusionARC, and static IMRT.Results:The 7 field locations, corresponding to the lowest cost function values, are chosen to insert extra segment for step‐and‐shoot dose delivery. Our proposed fusionARC plan with the selected angles improves the dose sparing to the critical organs, relative to static IMRT and conventional VMAT plans. The dose conformity to the target is significantly enhanced at the small expense of treatment time, compared with VMAT plan. Its estimated treatment time, however, is still much faster than IMRT.Conclusion:The fusionARC treatment with adaptive beam selection method could improve the plan quality with insignificant damage in the treatment time, relative to the conventional VMAT.
TU‐C‐17A‐08: Improving IMRT Planning and Reducing Inter‐Planner Variability Using the Stochastic Frontier Method: Validation Based On Clinical and Simulated Data
Authors: Gagne MC , Tremblay D , Varfalvy N , Archambault L 
Published: July 2014

Purpose:Intensity modulated radiation therapy always requires compromises between PTV coverage and organs at risk (OAR) sparing. We previously developed metrics that correlate doses to OAR to specific patients’ morphology using stochastic frontier analysis (SFA). Here, we aim to examine the validity of this approach using a large set of realistically simulated dosimetric and geometric data.Methods:SFA describes a set of treatment plans as an asymmetric distribution with respect to a frontier defining optimal plans. Eighty head and neck IMRT plans were used to establish a metric predicting the mean dose to parotids as a function of simple geometric parameters. A database of 140 parotids was used as a basis distribution to simulate physically plausible data of geometry and dose. Distributions comprising between 20 and 5000 were simulated and the SFA was applied to obtain new frontiers, which were compared to the original frontier.Results:It was possible to simulate distributions consistent with the original dataset. Below 160 organs, the SFA could not always describe distributions as asymmetric: a few cases showed a Gaussian or half‐Gaussian distribution. In order to converge to a stable solution, the number of organs in a distribution must ideally be above 100, but in many cases stable parameters could be achieved with as low as 60 samples of organ data. Mean RMS value of the error of new frontiers was significantly reduced when additional organs are used.Conclusion:The number of organs in a distribution showed to have an impact on the effectiveness of the model. It is always possible to obtain a frontier, but if the number of organs in the distribution is small (< 160), it may not represent de lowest dose achievable. These results will be used to determine number of cases necessary to adapt the model to other organs.
TU‐C‐17A‐09: Multi‐Case Knowledge‐Based IMRT Treatment Planning in Head and Neck Cancer: Are Six Heads Better Than One?
Authors: Grzetic S , Lutzky C , Das S , Lo J 
Published: July 2014

Purpose:HNC IMRT treatment planning is a challenging process that relies heavily on the planner’s experience. Previously, we used the single, best match from a library of manually planned cases to semi‐automatically generate IMRT plans for a new patient. The current multi‐case Knowledge Based Radiation Therapy (MC‐KBRT) study utilized different matching cases for each of six individual organs‐at‐risk (OARs), then combined those six cases to create the new treatment plan.Methods:From a database of 103 patient plans created by experienced planners, MC‐KBRT plans were created for 40 (17 unilateral and 23 bilateral) HNC “query” patients. For each case, 2D beam’s‐eye‐view images were used to find similar geometric “match” patients separately for each of 6 OARs. Dose distributions for each OAR from the 6 matching cases were combined and then warped to suit the query case’s geometry. The dose‐volume constraints were used to create the new query treatment plan without the need for human decision‐making throughout the IMRT optimization. The optimized MC‐KBRT plans were compared against the clinically approved plans and Version 1 (original KBRT) using the dose metrics: mean, median, and maximum (brainstem and cord+5mm) doses.Results:Compared to Version 1, MC‐KBRT had no significant reduction of the dose to any of the OARs in either unilateral/bilateral cases. Compared to the manually‐planned unilateral cases, there was significant reduction of the oral cavity mean/median dose (>2Gy) at the expense of the contralateral parotid. Compared to the manually‐planned bilateral cases, reduction of dose was significant in the ipsilateral parotid, larynx, and oral cavity (>3Gy mean/median) while maintaining PTV coverage.Conclusion:MC‐KBRT planning in head and neck cancer generates IMRT plans with equivalent dose sparing to manually created plans. MC‐KBRT using multiple case matches does not show significant dose reduction compared to using a single match case with dose warping.