bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐FG‐206‐12: Enhanced Laws Textures: A Potential MRI Surrogate Marker of Hepatic Fibrosis in a Murine Model
Authors: Li B , Yu H , Jara H , Soto J , Anderson S 
Published: July 2016

Purpose:To compare enhanced Laws texture derived from parametric proton density (PD) maps to other MRI‐based surrogate markers (T2, PD, ADC) in assessing degrees of liver fibrosis in a murine model of hepatic fibrosis using 11.7T scanner.Methods:This animal study was IACUC approved. Fourteen mice were divided into control (n=1) and experimental (n=13). The latter were fed a DDC‐supplemented diet to induce hepatic fibrosis. Liver specimens were imaged using an 11.7T scanner; the parametric PD, T2, and ADC maps were generated from spin‐echo pulsed field gradient and multi‐echo spin‐echo acquisitions. Enhanced Laws texture analysis was applied to the PD maps: first, hepatic blood vessels and liver margins were segmented/removed using an automated dual‐clustering algorithm; secondly, an optimal thresholding algorithm was applied to reduce the partial volume artifact; next, mean and stdev were corrected to minimize grayscale variation across images; finally, Laws texture was extracted. Degrees of fibrosis was assessed by an experienced pathologist and digital image analysis (%Area Fibrosis). Scatterplots comparing enhanced Laws texture, T2, PD, and ADC values to degrees of fibrosis were generated and correlation coefficients were calculated. Unenhanced Laws texture was also compared to assess the effectiveness of the proposed enhancements.Results:Hepatic fibrosis and the enhanced Laws texture were strongly correlated with higher %Area Fibrosis associated with higher Laws texture (r=0.89). Only a moderate correlation was detected between %Area Fibrosis and unenhanced Laws texture (r=0.70). Strong correlation also existed between ADC and %Area Fibrosis (r=0.86). Moderate correlations were seen between %Area Fibrosis and PD (r=0.65) and T2 (r=0.66).Conclusions:Higher degrees of hepatic fibrosis are associated with increased Laws texture. The proposed enhancements improve the accuracy of Laws texture. Enhanced Laws texture features are more accurate than PD and T2 in assessing fibrosis, and can potentially serve as an accurate surrogate marker for hepatic fibrosis.
WE‐F‐209‐00: A Practical Guide to Radiation Safety Surveys
Authors: Dillon Chad 
Published: July 2016

Over the past few years, numerous Accreditation Bodies, Regulatory Agencies, and State Regulations have implemented requirements for Radiation Safety Surveys following installation or modification to x‐ray rooms. The objective of this session is to review best practices in performing radiation safety surveys for both Therapy and Diagnostic installations, as well as a review of appropriate survey instruments.This session will be appropriate for both therapy and imaging physicists who are looking to increase their working knowledge of radiation safety surveys.

Learning Objectives:
Identify Appropriate Survey Meters for Radiation Safety Surveys
Develop best practices for Radiation Safety Surveys for Therapy units that include common areas of concern.
Develop best practices for Radiation Safety Surveys of Diagnostic and Nuclear Medicine rooms.
Identify acceptable dose levels and the factors that affect the calculations associated with performing Radiation Safety Surveys.
WE‐FG‐207B‐01: BEST IN PHYSICS (IMAGING): Abdominal CT with Three K‐Edge Contrast Materials Using a Whole‐Body Photon‐Counting Scanner: Initial Results of a Large Animal Experiment
Authors: Lakshmanan M , Symons R , Cork T , Davies‐Venn C , Rice K , Malayeri A , Sandfort V , Bluemke D , Pourmorteza A 
Published: July 2016

Purpose:To demonstrate the feasibility of in vivo three‐material decomposition techniques using photon‐counting CT (PCCT) with possible advantage of resolving arterial and venous flow of an organ simultaneously.Methods:Abdominal PCCT scans were acquired using a prototype whole‐body PCCT with four energy thresholds (25/50/75/90keV) in a canine. Bismuth subsalicylate (60 mg) was administered orally one day prior to scanning. Immediately prior to CT scan, gadoteric acid (60 ml, Dotarem, Guerbet) was intravenously injected, followed in ten minutes by a 20mL injection of iodinated contrast (iopamidol 370 mg/mL, Bracco). Scans were acquired every ∼20 seconds, starting from the time of iodine injection. Linear material decomposition was performed using the least mean squares method to create concentration maps of iodine, gadolinium, and bismuth. The method was calibrated to vials with known concentrations of materials placed next to the animal. The accuracy of this method was tested on vials with known concentrations.Results:The material decomposition algorithm's accuracy was confirmed to be within ±4mM in the test vials. In the animal, we could estimate the concentration of gadolinium in delayed‐enhanced phase (10 minutes post‐injection) in the abdomen. We could follow the wash‐in and wash‐out of iodine in arterial, venous, and excretory flow of the kidneys (20s, 80s, and 120s post‐iodine injection) while gadolinium was present in the delayed‐enhanced phase. Bismuth, which was used as a contrast agent for the gastro‐intestinal tract, was easily differentiable from the other two contrast agents in the small intestine.Conclusion:This study shows the feasibility of using photon‐counting CT with four energy thresholds to differentiate three k‐edge contrast agents in vivo. This can potentially reduce radiation dose to patients by combining arterial and venous phases into a single acquisition.
WE‐FG‐207B‐05: Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT
Authors: Niu S , Zhang Y , Ma J , Wang J 
Published: July 2016

Purpose:To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality.Methods:The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high‐quality images to guide the reconstruction of energy‐specific images. In PICTGV method, the high‐quality image is reconstructed from all detector‐collected X‐ray signals and is referred as the broad‐spectrum image. Distinct from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back‐projection method as well as TGV based method without prior image.Results:On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%.Conclusion:The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.
WE‐FG‐207B‐08: Dual‐Energy CT Iodine Accuracy Across Vendors and Platforms
Authors: Jacobsen M , Wood C , Cody D 
Published: July 2016

Purpose:Although a major benefit of dual‐energy CT is its quantitative capabilities, it is critical to understand how results vary by scanner manufacturer and/or model before making clinical patient management decisions. Each manufacturer utilizes a specific dual‐energy CT approach; cross‐calibration may be required for facilities with more than one dual‐energy CT scanner type.Methods:A solid dual‐energy quality control phantom (Gammex, Inc.; Appleton, WI) representing a large body cross‐section containing three Iodine inserts (2mg/ml, 5mg/ml, 15 mg/ml) was scanned on these CT systems: GE HD‐750 (80/140kVp), prototype GE Revolution CT with GSI (80/140kVp), Siemens Flash (80/140kVp and 100/140kVp), and Philips IQon (120kVp and 140kVp). Iodine content was measured in units of concentration (mg/ml) from a single 5mm‐thick central image. Three to five acquisitions were performed on each scanner platform in order to compute standard deviation. Scan acquisitions were approximately dose‐matched (∼25mGy CTDIvol) and image parameters were as consistent as possible (thickness, kernel, no noise reduction applied).Results:Iodine measurement error ranges were −0.24ߝ0.16 mg/ml for the 2mg/ml insert (−12.0 − 8.0%), −0.28–0.26 mg/ml for the 5mg/ml insert (−5.6 − 5.2%), and −1.16−0.99 mg/ml for the 15mg/ml insert (−7.7 − 6.6%). Standard deviations ranged from 0 to 0.19 mg/ml for the repeated acquisitions from each scanner. The average iodine measurement error and standard deviation across all systems and inserts was −0.21 ± 0.48 mg/ml (−1.5 ± 6.48%). The largest absolute measurement error was found in the 15mg/ml iodine insert.Conclusion:There was generally good agreement in Iodine quantification across 3 dual‐energy CT manufacturers and 4 scanner models. This was unexpected given the widely different underlying dual‐energy CT mechanisms employed. Future work will include additional scanner platforms, independent verification of the Iodine insert standard concentrations (especially the 15 mg/ml insert), and how much measurement variability can be clinically tolerated.This research has been supported by funds from Dr. William Murphy, Jr., the John S. Dunn, Sr. Distinguished Chair in Diagnostic Imaging at MD Anderson Cancer Center.
WE‐G‐204‐00: Post‐Graduate Training of the Next Generation of Academic Medical Physicists
Authors: Hazle John 
Published: July 2016

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference.Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all‐time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases.A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant.Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two‐years of residency training. This full‐time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two‐years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position.In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!
WE‐H‐BRC‐01: Failure Mode and Effects Analysis of Skin Electronic Brachytherapy Using Esteya Unit
Authors: Ibanez‐Rosello B , Bautista‐Ballesteros J , Bonaque J , Lliso F , Carmona V , Gimeno J , Ouhib Z , Perez‐Calatayud J 
Published: July 2016

Purpose:A failure mode and effect analysis (FMEA) of skin lesions treatment process using Esteya™ device (Elekta Brachyterapy, Veenendaal, The Netherlands) was performed, with the aim of increasing the quality of the treatment and reducing the likelihood of unwanted events.Methods:A multidisciplinary team with experience in the treatment process met to establish the process map, which outlines the flow of various stages for such patients undergoing skin treatment. Potential failure modes (FM) were identified and the value of severity (S), frequency of occurrence (O), and lack of detectability (D) of the proposed FM were scored individually, each on a scale of 1 to 10 following TG‐100 guidelines of the AAPM. These failure modes were ranked according to our risk priority number (RPN) and S scores. The efficiency of existing quality management tools was analyzed through a reassessment of the O and D made by consensus.Results:149 FM were identified, 43 of which had RPN ≥ 100 and 30 had S ≥ 7. After introduction of the tools of quality management, only 3 FM had RPN ≥ 100 and 22 FM had RPN ≥ 50. These 22 FM were thoroughly analyzed and new tools for quality management were proposed. The most common cause of highest RPN FM was associated with the heavy patient workload and the continuous and accurate applicator‐patient skin contact during the treatment. To overcome this second item, a regular quality control and setup review by a second individual before each treatment session was proposed.Conclusion:FMEA revealed some of the FM potentials that were not predicted during the initial implementation of the quality management tools. This exercise was useful in identifying the need of periodic update of the FMEA process as new potential failures can be identified.
WE‐H‐BRC‐02: Failure Mode and Effect Analysis of Liver Stereotactic Body Radiotherapy
Authors: Rusu I , Thomas T , Roeske J , Price J , Perino C , Mescioglu I , Surucu M 
Published: July 2016

Purpose:To identify areas of improvement in our liver stereotactic body radiation therapy (SBRT) program, using failure mode and effect analysis (FMEA).Methods:A multidisciplinary group consisting of one physician, three physicists, one dosimetrist and two therapists was formed. A process map covering 10 major stages of the liver SBRT program from the initial diagnosis to post treatment follow‐up was generated. A total of 102 failure modes, together with their causes and effects, were identified. The occurrence (O), severity (S) and lack of detectability (D) were independently scored. The ranking was done using the risk probability number (RPN) defined as the product of average O, S and D numbers for each mode. The scores were normalized to remove inter‐observer variability, while preserving individual ranking order. Further, a correlation analysis on the overall agreement on rank order of all failure modes resulted in positive values for successive pairs of evaluators. The failure modes with the highest RPN value were considered for further investigation.Results:The average normalized RPN values for all modes were 39 with a range of 9 to 103. The FMEA analysis resulted in the identification of the top 10 critical failures modes as: Incorrect CT‐MR registration, MR scan not performed in treatment position, patient movement between CBCT acquisition and treatment, daily IGRT QA not verified, incorrect or incomplete ITV delineation, OAR contours not verified, inaccurate normal liver effective dose (Veff) calculation, failure of bolus tracking for 4D CT scan, setup instructions not followed for treatment and plan evaluation metrics missed.Conclusion:The application of FMEA to our liver SBRT program led to the identification and possible improvement of areas affecting patient safety.
WE‐H‐BRC‐04: Implement Lean Methodology to Make Our Current Process of CT Simulation to Treatment More Efficient
Authors: Boddu S , Morrow A , Krishnamurthy N , McVicker A , Deb N , Rangaraj D 
Published: July 2016

Purpose:Our goal is to implement lean methodology to make our current process of CT simulation to treatment more efficient.Methods:In this study, we implemented lean methodology and tools and employed flowchart in excel for process‐mapping. We formed a group of physicians, physicists, dosimetrists, therapists and a clinical physics assistant and huddled bi‐weekly to map current value streams. We performed GEMBA walks and observed current processes from scheduling patient CT Simulations to treatment plan approval. From this, the entire workflow was categorized into processes, sub‐processes, and tasks. For each process we gathered data on touch time, first time quality, undesirable effects (UDEs), and wait‐times from relevant members of each task. UDEs were binned per frequency of their occurrence. We huddled to map future state and to find solutions to high frequency UDEs. We implemented visual controls, hard stops, and documented issues found during chart checks prior to treatment plan approval.Results:We have identified approximately 64 UDEs in our current workflow that could cause delays, re‐work, compromise the quality and safety of patient treatments, or cause wait times between 1 – 6 days. While some UDEs are unavoidable, such as re‐planning due to patient weight loss, eliminating avoidable UDEs is our goal. In 2015, we found 399 issues with patient treatment plans, of which 261, 95 and 43 were low, medium and high severity, respectively. We also mapped patient‐specific QA processes for IMRT/Rapid Arc and SRS/SBRT, involving 10 and 18 steps, respectively. From these, 13 UDEs were found and 5 were addressed that solved 20% of issues.Conclusion:We have successfully implemented lean methodology and tools. We are further mapping treatment site specific workflows to identify bottlenecks, potential breakdowns and personnel allocation and employ tools like failure mode effects analysis to mitigate risk factors to make this process efficient.
WE‐H‐BRC‐07: Validation of a Commercial Atlas Based Auto‐Segmentation Package For Automated Contour Quality Control
Authors: Berry S , Jedruszczuk K , Veeraraghavan H , Apte A , Saleh Z , Hunt M 
Published: July 2016

Purpose:To investigate whether a commercial atlas based auto‐segmentation (ABAS) package can be used for automated review and quality control (QC) of physician segmentations, and if so, to determine action levels for future clinical implementation.Methods:42 head and neck cancer (HNC) patient plan CT scans with manually defined expert physician organ at risk (OAR) segmentations were retrospectively identified. Five representative patients were selected as ABAS models and were subsequently applied to each of the remaining 37 studies, resulting in 5 auto‐segmentations (AS) per patient. The similarity metric (SM) generated by the ABAS package to describe the anatomical similarity of the CT scan to be segmented against that of the atlas CT was recorded for each AS. A simultaneous truth and performance level (STAPLE) consensus contour was created from the 5 AS on each patient. Each individual AS and the STAPLE were compared to the existing expert manual segmentation using the Hausdorff Distance (HD) and Dice similarity coefficient (DSC).Results:Software‐based SM is not indicative of AS accuracy. STAPLE significantly improved AS quality over the results averaged from individual ABAS for 7 of 8 OARs. DSC values (mean ± 1 standard deviation) for STAPLE compared to the expert segmentations are comparable to the literature for the parotids (0.74 ± 0.10), mandible (0.80 ± 0.07), and submandibular glands (0.68 ± 0.10) but is poor at delineating the brachial plexus (0.32 ± 0.08) and larynx (0.61 ± 0.15).Conclusion:Automated QC of manually delineated HNC OARs can be achieved with a STAPLE consensus contour constructed from 5 ABAS. Parotid, mandible, and submandibular gland structures can be flagged for manual review and user intervention if the DSC and HD exceed the action levels defined during the commissioning process. Further investigation is necessary to improve the AS results for the brachial plexus and larynx.Sean Berry, Harini Veeraraghavan, and Margie Hunt hold grants from Varian Medical Systems unrelated to this project
WE‐H‐BRB‐03: Learning Health Systems for Radiation Oncology: Needs and Challenges for Future Success
Authors: McNutt T. 
Published: July 2016

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future SuccessThe overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives.This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis.

Learning Objectives:
To discuss current and future sources of big data for use in radiation oncology research
To optimize our current data collection by adopting new strategies from outside radiation oncology
To determine what new knowledge big data can provide for clinical decision support for personalized medicineL. Xing, NIH/NCI Google Inc.