bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH‐A‐BRF‐02: BEST IN PHYSICS (JOINT IMAGING‐THERAPY) – Modeling Tumor Evolution for Adaptive Radiation Therapy
Authors: Liu Y , Chan TCY , Lee CG , Cho YB , Islam MK 
Published: July 2014

Purpose:To develop mathematical models of tumor geometry changes under radiotherapy that may support future adaptive paradigms.Methods:A total of 29 cervical patients were scanned using MRI, once for planning and weekly thereafter for treatment monitoring. Using the tumor volumes contoured by a radiologist, three mathematical models were investigated based on the assumption of a stochastic process of tumor evolution. The “weekly MRI” model predicts tumor geometry for the following week from the last two consecutive MRI scans, based on the voxel transition probability. The other two models use only the first pair of consecutive MRI scans, and the transition probabilities were estimated via tumor type classified from the entire data set. The classification is based on either measuring the tumor volume (the “weekly volume” model), or implementing an auxiliary “Markov chain” model. These models were compared to a constant volume approach that represents the current clinical practice, using various model parameters; e.g., the threshold probability β converts the probability map into a tumor shape (larger threshold implies smaller tumor). Model performance was measured using volume conformity index (VCI), i.e., the union of the actual target and modeled target volume squared divided by product of these two volumes.Results:The “weekly MRI” model outperforms the constant volume model by 26% on average, and by 103% for the worst 10% of cases in terms of VCI under a wide range of β. The “weekly volume” and “Markov chain” models outperform the constant volume model by 20% and 16% on average, respectively. They also perform better than the “weekly MRI” model when β is large.Conclusion:It has been demonstrated that mathematical models can be developed to predict tumor geometry changes for cervical cancer undergoing radiotherapy. The models can potentially support adaptive radiotherapy paradigm by reducing normal tissue dose.This research was supported in part by the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF) and the MITACS Accelerate Internship Program.
TH‐A‐17A‐01: Innovation in PET Instrumentation and Applications
Authors: Casey M , Miyaoka R , Shao Y 
Published: July 2014

Innovation in PET instrumentation has led to the new millennium revolutionary imaging applications for diagnosis, therapeutic guidance, and development of new molecular imaging probes, etc. However, after several decades innovations, will the advances of PET technology and applications continue with the same trend and pace? What will be the next big thing beyond the PET/CT, PET/MRI, and Time‐of‐flight PET? How will the PET instrumentation and imaging performance be further improved by novel detector research and advanced imaging system development? Or will the development of new algorithms and methodologies extend the limit of current instrumentation and leapfrog the imaging quality and quantification for practical applications?The objective of this session is to present an overview of current status and advances in the PET instrumentation and applications with speakers from leading academic institutes and a major medical imaging company. Presenting with both academic research projects and commercial technology developments, this session will provide a glimpse of some latest advances and challenges in the field, such as using semiconductor photon‐sensor based PET detectors to improve performance and enable new applications, as well as the technology trend that may lead to the next breakthrough in PET imaging for clinical and preclinical applications. Both imaging and image‐guided therapy subjects will be discussed.Learning Objectives:

Describe the latest innovations in PET instrumentation and applications
Understand the driven force behind the PET instrumentation innovation and development
Learn the trend of PET technology development for applications
TH‐A‐18C‐01: Design Optimization of Segmented Scintillators for Megavoltage Cone‐ Beam CT
Authors: Liu L , Antonuk L , El‐Mohri Y , Zhao Q , Jiang H 
Published: July 2014

Purpose:Active matrix flat‐panel imagers incorporating thick, segmented scintillators for megavoltage cone‐beam CT (MV CBCT) imaging have demonstrated strong potential for facilitating soft‐tissue visualization at low, clinically practical doses. In order to identify scintillator design parameters that optimize performance for this purpose, a modeling technique which includes both radiation and optical effects and which lends itself to computationally practical implementation has been developed and explored.Methods:A hybrid modeling technique, based on Monte Carlo event‐by‐event simulation of radiation transport and separate determination of optical effects, was devised as an alternative to computationally prohibitive event‐by‐ event simulations of both radiation and optical transport. The technique was validated against empirical results from a previously reported 1.13 cm thick, 1.016 mm element‐to‐element pitch BGO scintillator prototype. Using this technique, the contrast‐to‐noise ratio (CNR) and spatial resolution performance of numerous scintillator designs, with thicknesses ranging from 0.5 to 6 cm and pitches ranging from 0.508 to 1.524 mm, were examined.Results:CNR and spatial resolution performance for the various scintillator designs demonstrate complex behavior as scintillator thickness and pitch are varied – exhibiting a clear trade‐off between these two imaging metrics up to a thickness of ˜3 cm. Based on these results, an optimization map highlighting those regions of design that provide a balance between these metrics was created. The map indicates that, for a given set of optical parameters, scintillator thickness and pitch can be judiciously chosen to maximize performance without resorting to thicker, more costly scintillators.Conclusion:Modeling radiation and optical effects in thick, segmented scintillators through use of a hybrid modeling technique provides a practical way to gain insight as to how to optimize the performance of such devices for radiotherapy imaging. Assisted by such modeling, the development of practical designs should greatly facilitate low‐dose, soft tissue visualization through MV CBCT imaging.This project was supported in part by NIH grant R01 CA051397.
TH‐A‐18C‐02: An Electrostatic Model for Assessment of Joint Space Morphology in Cone‐Beam CT
Authors: Cao Q , Thawait G , Gang G , Zbijewski W , Riegel T , Demehri S , Siewerdsen J 
Published: July 2014

Purpose:High‐resolution cone‐beam CT (CBCT) of the extremities presents a potentially valuable basis for image‐based biomarkers of arthritis, trauma, and risk of injury. We present a new method for 3D joint space analysis that exploits the high isotropic spatial resolution of CBCT and is sensitive to small changes in disease‐related morphology.Methods:The approach uses an “electrostatic” model in which joint surfaces (e.g., distal femur and proximal tibia) are labeled as charge densities between which the electric field is solved by approximation to the Laplace equation. The method yields a unique solution determined by the field lines across the “capacitor” and is hypothesized to be more sensitive than conventional (Sharp) scores and immune to degeneracies that limit simple distance‐along‐axis or closest‐point analysis. The algorithm was validated in CBCT phantom images and applied in two clinical scenarios: osteoarthritis (OA, change in loadbearing tibiofemoral joint space); and assessment of injury risk (correlation of 3D joint space to tibial slope).Results:Joint space maps computed from the electrostatic model were accurate to within the voxel size (0.26 mm). The method highlighted subtle regions of morphological change that would likely be missed by conventional scalar metrics. Regions of subtle cartilage erosion were well quantified, and the method confidently discriminated OA and non‐OA cohorts. 3D joint space maps correlated well with tibial slope and provide a new basis for principal component analysis of loadbearing injury risk. Runtime was less than 5 min (235×235×121 voxel subvolume in Matlab).Conclusion:A new method for joint space assessment was reported as a possible image‐based biomarker of subtle articular change. The algorithm yields accurate quantitation of the joint in a manner that is robust against operator and patient setup variation. The method shows promising initial results in ongoing trials of CBCT in osteoarthritis, rheumatoid arthritis, and injury risk assessment.Research supported by R01 and R21 grants from the National Institutes of Health, academic‐industry partnership with Carestream Health, and a grant from the US Army Natick Soldier Research, Development & Engineering Center.
TH‐A‐18C‐03: Noise Correlation in CBCT Projection Data and Its Application for Noise Reduction in Low‐Dose CBCT
Authors: ZHANG H , Ouyang L , Huang J , Ma J , Chen W , Wang J 
Published: July 2014

Purpose:To study the noise correlation properties of cone‐beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics‐based projection restoration algorithm for noise reduction in low‐dose CBCT.Methods:In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on‐board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least‐squares (PWLS) criterion for noise reduction of low‐dose CBCT.Results:The analyses of the repeated measurements show that noise correlation coefficients are non‐zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first‐ and second‐ order neighbors are about 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS‐Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS‐Dia) at the matched resolution.Conclusion:Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics‐based projection restoration algorithm for low‐dose CBCT.
TH‐A‐18C‐09: Ultra‐Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma
Authors: Sisniega A , Zbijewski W , Stayman J , Yorkston J , Aygun N , Koliatsos V , Siewerdsen J 
Published: July 2014

Purpose:Application of cone‐beam CT (CBCT) to low‐contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high‐quality CBCT imaging of acute brain injury.Methods:The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC‐GPU), with baseline simulation speed of ˜1e7 photons/sec. MC‐GPU is accelerated by a novel, GPU‐optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed‐up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source‐detector distance 700 mm, source‐axis distance 480 mm).Results:For a fixed run‐time of ˜1 sec/projection, GPU‐optimized VR reduces the noise in MC‐GPU scatter estimates by a factor of 4. For scatter correction, MC‐GPU with VR is executed with 4‐fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run‐time per scan, and de‐noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast‐tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction.Conclusion:Acceleration of MC‐GPU achieved through GPU‐optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications.Research grant from Carestream Health. JY is an employee of Carestream Health.
TH‐A‐18C‐10: Dynamic Intensity Weighted Region of Interest Imaging
Authors: Pearson E , Pan X , Pelizzari C 
Published: July 2014

Purpose:For image guidance tasks full image quality is not required throughout the entire image. With dynamic filtration of the kV imaging beam the noise properties of the CT image can be locally controlled, providing a high quality image around the target volume with a lower quality surrounding region while providing substantial dose sparing to the patient as well as reduced scatter fluence on the detector.Methods:A dynamic collimation device with 3mm copper blades has been designed to mount in place of the bowtie filter on the On‐Board Imager (Varian Medical Systems). The beam intensity is reduced by 95% behind the copper filters and the aperture is controlled dynamically to conformally illuminate a given ROI during a standard cone‐beam CT scan. A data correction framework to account for the physical effects of the collimator prior to reconstruction was developed. Furthermore, to determine the dose savings and scatter reduction a monte carlo model was built in BEAMnrc with specifics from the Varian Monte Carlo Data Package. The MC model was validated with Gafchromic film.Results:The reconstructed image shows image quality comparable to a standard scan in the specified ROI, with higher noise and streaks in the outer region but still sufficient information for alignment to high contrast structures. The monte carlo modeling showed that the scatter‐to‐primary ratio was reduced from 1.26 for an unfiltered scan to 0.45 for an intensity weighted scan, suggesting that image quality may be improved in the inner ROI. Dose in the inner region was reduced 10–15% due to reduced scatter and by as much as 75% in the outer region.Conclusion:Dynamic intensity‐weighted ROI imaging allows reduction of imaging dose to sensitive organs away from the target region while providing images that retain their utility for patient setup and procedure guidance.Funding was provided in part by Varian Medical Systems and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599.The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of any of the supporting organizations.
TH‐A‐9A‐02: BEST IN PHYSICS (THERAPY) – 4D IMRT Planning Using Highly‐ Parallelizable Particle Swarm Optimization
Authors: Modiri A , Gu X , Sawant A 
Published: July 2014

Purpose:We present a particle swarm optimization (PSO)‐based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs).Methods:The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well‐suited for such large problems. A summed fluence map was created using an in‐house B‐spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)‐based IMRT plan.Results:The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV‐based plan. The dose‐sparing achieved via PSO‐4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%.Conclusion:Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta
TH‐C‐9A‐01: Lean Tools and Methods
Authors: Rangaraj D , Chan K , Boddu S , Pawlicki T , Dieterich S 
Published: July 2014

Lean thinking has revolutionized the manufacturing industry. Toyota has pioneered and leveraged this aspect of Lean thinking. Application of Lean thinking and Lean Six Sigma techniques into Healthcare and in particular in Radiation Oncology has its merits and challenges. To improve quality, safety and patient satisfaction with available resources or reducing cost in terms of time, staff and resources is demands of today's healthcare. Radiation oncology treatment involves many processes and steps, identifying and removing the non‐value added steps in a process can significantly improve the efficiency. Real projects undertaken in radiation oncology department in cutting down the procedure time for MRI guided brachytherapy to 40% less using lean thinking will be narrated. Simple Lean tools and techniques such as Gemba walk, visual control, daily huddles, standard work, value stream mapping, error‐proofing, etc. can be applied with existing resources and how that improved the operation in a Radiation Oncology department's two year experience will be discussed. Lean thinking focuses on identifying and solving the root‐cause of a problem by asking “Why” and not “Who” and this requires a culture change of no blame. Role of leadership in building lean culture, employee empowerment and trains and develops lean thinkers will be presented. Why Lean initiatives fail and how to implement lean successfully in your clinic will be discussed.Learning Objectives:

Concepts of lean management or lean thinking.
Lean tools and techniques applied in Radiation Oncology.
Implement no blame culture and focus on system and processes.
Leadership role in implementing lean culture.
Challenges for Lean thinking in healthcare
TH‐E‐19A‐01: Quality and Safety in Radiation Therapy
Authors: Ford E , Ezzell G , Miller B , Yorke E 
Published: July 2014

Clinical radiotherapy data clearly demonstrate the link between the quality and safety of radiation treatments and the outcome for patients. The medical physicist plays an essential role in this process. To ensure the highest quality treatments, the medical physicist must understand and employ modern quality improvement techniques. This extends well beyond the duties traditionally associated with prescriptive QA measures. This session will review the current best practices for improving quality and safety in radiation therapy.General elements of quality management will be reviewed including: what makes a good quality management structure, the use of prospective risk analysis such as FMEA, and the use of incident learning. All of these practices are recommended in society‐level documents and are incorporated into the new Practice Accreditation program developed by ASTRO. To be effective, however, these techniques must be practical in a resource‐limited environment. This session will therefore focus on practical tools such as the newly‐released radiation oncology incident learning system, RO‐ILS, supported by AAPM and ASTRO.With these general constructs in mind, a case study will be presented of quality management in an SBRT service. An example FMEA risk assessment will be presented along with incident learning examples including root cause analysis.As the physicist's role as “quality officer” continues to evolve it will be essential to understand and employ the most effective techniques for quality improvement. This session will provide a concrete overview of the fundamentals in quality and safety.Learning Objectives:

Recognize the essential elements of a good quality management system in radiotherapy.
Understand the value of incident learning and the AAPM/ASTRO ROILS incident learning system.
Appreciate failure mode and effects analysis as a risk assessment tool and its use in resource‐limited environments.
Understand the fundamental principles of good error proofing that extends beyond traditional prescriptive QA measures.
TH‐E‐16A‐01: Building, Maintaining and Improving Physics Residency Program
Authors: Klein E , Nye J 
Published: July 2014

In this session we will discuss the key steps for building, maintaining and improving a physics residency program. The initial momentum starts with a secure commitment of; a program director, physics faculty, physicians, key technical personnel, and, of course, administration. The ultimate goal is to build a strong clinical physicist who practice independently and/or work in an interdisciplinary team. Part of the initiation is to find out the right balance of training, resident empowerment, and service contributed back to the clinic. Funding of residencies will be discussed including obstacles and benefits to residents participating in clinical service and establishing hub and spoke training collaborations. Another key step that will be discussed is the recruitment of resident candidates. Namely, where the residents are coming from, what their educational background is, and most importantly during interviews, how to gauge what makes an ideal resident. The key to maintenance of a residency program is to not allow stagnation, but rather to constantly update. Examples will be given of evolution of programs in terms of the didactic and clinical rotational components. In addition, how to allocate the resident developmental work, whether it be clinical or benchmark, without disrupting training. We will also discuss how to evaluate faculty on the ownership they take regarding resident training. We will discuss both therapy and imaging programs. Finally, we will share experiences on steps to continually improve one's program.Learning Objectives:

To obtain from the experience of long standing programs the lessons learned.
To find out what historically has worked out to be ideal resident candidates.
Guidelines for continually improving a program.