bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐FG‐207B‐09: Experimental Assessment of Noise and Spatial Resolution in Virtual Non‐Contrast Dual‐Energy CT Images Across Multiple Patient Sizes and CT Systems
Authors: Montoya J , Ferrero A , Yu L , Leng S , McCollough C 
Published: July 2016

Purpose:To investigate the noise and spatial resolution properties of virtual non‐contrast (VNC) dual‐energy CT images compared to true non‐contrast (TNC) images across multiple patient sizes and CT systems.Methods:Torso‐shaped water phantoms with lateral widths of 25, 30, 35, 40 and 45 cm and a high resolution bar pattern phantom (Catphan CTP528) were scanned using 2nd and 3rd generation dual‐source CT systems (Scanner A: Somatom Definition Flash, Scanner B: Somatom Force, Siemens Healthcare) in dual‐energy scan mode with the same radiation dose for a given phantom size. Tube potentials of 80/Sn140 and 100/Sn140 on Scanner A and 80/Sn150, 90/Sn150 and 100/Sn150 on Scanner B were evaluated to examine the impact of spectral separation. Images were reconstructed using a medium sharp quantitative kernel (Qr40), 1.0‐mm thickness, 1.0‐mm interval and 20 cm field of view. Mixed images served as TNC images. VNC images were created using commercial software (Virtual Unenhanced, Syngo VIA Version VA30, Siemens Healthcare). The noise power spectrum (NPS), area under the NPS, peak frequency of the NPS and image noise were measured for every phantom size and tube potential combination in TNC and VNC images. Results were compared within and between CT systems.Results:Minimal shift in NPS peak frequencies was observed in VNC images compared to TNC for NPS having pronounced peaks. Image noise and area under the NPS were higher in VNC images compared to TNC images across all tube potentials and for scanner A compared to scanner B. Limiting spatial resolution was deemed to be identical between VNC and TNC images.Conclusion:Quantitative assessment of image quality in VNC images demonstrated higher noise but equivalent spatial resolution compared to TNC images. Decreased noise was observed in the 3rd generation dual‐source CT system for tube potential pairs having greater spectral separation.Dr. McCollough receives research support from Siemens Healthcare
WE‐G‐BRC‐00: Experiences with TG100 in Clinical Use
Authors: Kim Grace Gwe‐Ya 
Published: July 2016

Failure Mode and Effects Analysis (FMEA) originated as an industrial engineering technique used for risk management and safety improvement of complex processes. In the context of radiotherapy, the AAPM Task Group 100 advocates FMEA as the framework of choice for establishing clinical quality management protocols. However, there is concern that widespread adoption of FMEA in radiation oncology will be hampered by the perception that implementation of the tool will have a steep learning curve, be extremely time consuming and labor intensive, and require additional resources. To overcome these preconceptions and facilitate the introduction of the tool into clinical practice, the medical physics community must be educated in the use of this tool and the ease in which it can be implemented. Organizations with experience in FMEA should share their knowledge with others in order to increase the implementation, effectiveness and productivity of the tool.This session will include a brief, general introduction to FMEA followed by a focus on practical aspects of implementing FMEA for specific clinical procedures including HDR brachytherapy, physics plan review and radiosurgery. A description of common equipment and devices used in these procedures and how to characterize new devices for safe use in patient treatments will be presented. This will be followed by a discussion of how to customize FMEA techniques and templates to one's own clinic. Finally, cases of common failure modes for specific procedures (described previously) will be shown and recommended intervention methodologies and outcomes reviewed.

Learning Objectives:
Understand the general concept of failure mode and effect analysis
Learn how to characterize new equipment for safety
Be able to identify potential failure modes for specific procedures and learn mitigation techniques
Be able to customize FMEA examples and templates for use in any clinic
WE‐G‐BRC‐02: Risk Assessment for HDR Brachytherapy
Authors: Mayadev J. 
Published: July 2016

Failure Mode and Effects Analysis (FMEA) originated as an industrial engineering technique used for risk management and safety improvement of complex processes. In the context of radiotherapy, the AAPM Task Group 100 advocates FMEA as the framework of choice for establishing clinical quality management protocols. However, there is concern that widespread adoption of FMEA in radiation oncology will be hampered by the perception that implementation of the tool will have a steep learning curve, be extremely time consuming and labor intensive, and require additional resources. To overcome these preconceptions and facilitate the introduction of the tool into clinical practice, the medical physics community must be educated in the use of this tool and the ease in which it can be implemented. Organizations with experience in FMEA should share their knowledge with others in order to increase the implementation, effectiveness and productivity of the tool.This session will include a brief, general introduction to FMEA followed by a focus on practical aspects of implementing FMEA for specific clinical procedures including HDR brachytherapy, physics plan review and radiosurgery. A description of common equipment and devices used in these procedures and how to characterize new devices for safe use in patient treatments will be presented. This will be followed by a discussion of how to customize FMEA techniques and templates to one's own clinic. Finally, cases of common failure modes for specific procedures (described previously) will be shown and recommended intervention methodologies and outcomes reviewed.

Learning Objectives:
Understand the general concept of failure mode and effect analysis
Learn how to characterize new equipment for safety
Be able to identify potential failure modes for specific procedures and learn mitigation techniques
Be able to customize FMEA examples and templates for use in any clinic
WE‐G‐BRC‐03: Risk Assessment for Physics Plan Review
Authors: Parker S. 
Published: July 2016

Failure Mode and Effects Analysis (FMEA) originated as an industrial engineering technique used for risk management and safety improvement of complex processes. In the context of radiotherapy, the AAPM Task Group 100 advocates FMEA as the framework of choice for establishing clinical quality management protocols. However, there is concern that widespread adoption of FMEA in radiation oncology will be hampered by the perception that implementation of the tool will have a steep learning curve, be extremely time consuming and labor intensive, and require additional resources. To overcome these preconceptions and facilitate the introduction of the tool into clinical practice, the medical physics community must be educated in the use of this tool and the ease in which it can be implemented. Organizations with experience in FMEA should share their knowledge with others in order to increase the implementation, effectiveness and productivity of the tool.This session will include a brief, general introduction to FMEA followed by a focus on practical aspects of implementing FMEA for specific clinical procedures including HDR brachytherapy, physics plan review and radiosurgery. A description of common equipment and devices used in these procedures and how to characterize new devices for safe use in patient treatments will be presented. This will be followed by a discussion of how to customize FMEA techniques and templates to one's own clinic. Finally, cases of common failure modes for specific procedures (described previously) will be shown and recommended intervention methodologies and outcomes reviewed.

Learning Objectives:
Understand the general concept of failure mode and effect analysis
Learn how to characterize new equipment for safety
Be able to identify potential failure modes for specific procedures and learn mitigation techniques
Be able to customize FMEA examples and templates for use in any clinic
WE‐G‐209‐00: Identifying Image Artifacts, Their Causes, and How to Fix Them
Authors: Pooley Robert 
Published: July 2016

Digital radiography, CT, PET, and MR are complicated imaging modalities which are composed of many hardware and software components. These components work together in a highly coordinated chain of events with the intent to produce high quality images. Acquisition, processing and reconstruction of data must occur in a precise way for optimum image quality to be achieved. Any error or unexpected event in the entire process can produce unwanted pixel intensities in the final images which may contribute to visible image artifacts. The diagnostic imaging physicist is uniquely qualified to investigate and contribute to resolution of image artifacts.This course will teach the participant to identify common artifacts found clinically in digital radiography, CT, PET, and MR, to determine the causes of artifacts, and to make recommendations for how to resolve artifacts.

Learning Objectives:
Identify common artifacts found clinically in digital radiography, CT, PET and MR.
Determine causes of various clinical artifacts from digital radiography, CT, PET and MR.
Describe how to resolve various clinical artifacts from digital radiography, CT, PET and MR.
WE‐G‐209‐02: CT
Authors: Kofler J. 
Published: July 2016

Digital radiography, CT, PET, and MR are complicated imaging modalities which are composed of many hardware and software components. These components work together in a highly coordinated chain of events with the intent to produce high quality images. Acquisition, processing and reconstruction of data must occur in a precise way for optimum image quality to be achieved. Any error or unexpected event in the entire process can produce unwanted pixel intensities in the final images which may contribute to visible image artifacts. The diagnostic imaging physicist is uniquely qualified to investigate and contribute to resolution of image artifacts.This course will teach the participant to identify common artifacts found clinically in digital radiography, CT, PET, and MR, to determine the causes of artifacts, and to make recommendations for how to resolve artifacts.

Learning Objectives:
Identify common artifacts found clinically in digital radiography, CT, PET and MR.
Determine causes of various clinical artifacts from digital radiography, CT, PET and MR.
Describe how to resolve various clinical artifacts from digital radiography, CT, PET and MR.
WE‐G‐209‐03: PET
Authors: Kemp B. 
Published: July 2016

Digital radiography, CT, PET, and MR are complicated imaging modalities which are composed of many hardware and software components. These components work together in a highly coordinated chain of events with the intent to produce high quality images. Acquisition, processing and reconstruction of data must occur in a precise way for optimum image quality to be achieved. Any error or unexpected event in the entire process can produce unwanted pixel intensities in the final images which may contribute to visible image artifacts. The diagnostic imaging physicist is uniquely qualified to investigate and contribute to resolution of image artifacts.This course will teach the participant to identify common artifacts found clinically in digital radiography, CT, PET, and MR, to determine the causes of artifacts, and to make recommendations for how to resolve artifacts.

Learning Objectives:
Identify common artifacts found clinically in digital radiography, CT, PET and MR.
Determine causes of various clinical artifacts from digital radiography, CT, PET and MR.
Describe how to resolve various clinical artifacts from digital radiography, CT, PET and MR.
WE‐G‐209‐04: MRI
Authors: Pooley R. 
Published: July 2016

Digital radiography, CT, PET, and MR are complicated imaging modalities which are composed of many hardware and software components. These components work together in a highly coordinated chain of events with the intent to produce high quality images. Acquisition, processing and reconstruction of data must occur in a precise way for optimum image quality to be achieved. Any error or unexpected event in the entire process can produce unwanted pixel intensities in the final images which may contribute to visible image artifacts. The diagnostic imaging physicist is uniquely qualified to investigate and contribute to resolution of image artifacts.This course will teach the participant to identify common artifacts found clinically in digital radiography, CT, PET, and MR, to determine the causes of artifacts, and to make recommendations for how to resolve artifacts.

Learning Objectives:
Identify common artifacts found clinically in digital radiography, CT, PET and MR.
Determine causes of various clinical artifacts from digital radiography, CT, PET and MR.
Describe how to resolve various clinical artifacts from digital radiography, CT, PET and MR.
WE‐H‐BRC‐03: Failure Mode and Effects Analysis in the First Clinical Implementation of a Novel Stereotactic Breast Radiotherapy Device: GammaPod™
Authors: Mossahebi S , Feigenberg S , Nichols E , Niu Y , Becker S , Prado K , Yi B , Yu C , Mutaf Y 
Published: July 2016

Purpose:GammaPod™, the first stereotactic radiotherapy device for early stage breast cancer treatment, has been recently installed and commissioned at our institution. A multidisciplinary working group applied the failure mode and effects analysis (FMEA) approach to perform a risk analysis.Methods:FMEA was applied to the GammaPod™ treatment process by: 1) generating process maps for each stage of treatment; 2) identifying potential failure modes and outlining their causes and effects; 3) scoring the potential failure modes using the risk priority number (RPN) system based on the product of severity, frequency of occurrence, and detectability (ranging 1–10). An RPN of higher than 150 was set as the threshold for minimal concern of risk. For these high‐risk failure modes, potential quality assurance procedures and risk control techniques have been proposed. A new set of severity, occurrence, and detectability values were re‐assessed in presence of the suggested mitigation strategies.Results:In the single‐day image‐and‐treat workflow, 19, 22, and 27 sub‐processes were identified for the stages of simulation, treatment planning, and delivery processes, respectively. During the simulation stage, 38 potential failure modes were found and scored, in terms of RPN, in the range of 9ߝ392. 34 potential failure modes were analyzed in treatment planning with a score range of 16ߝ200. For the treatment delivery stage, 47 potential failure modes were found with an RPN score range of 16ߝ392. The most critical failure modes consisted of breast‐cup pressure loss and incorrect target localization due to patient upper‐body alignment inaccuracies. The final RPN score of these failure modes based on recommended actions were assessed to be below 150.Conclusion:FMEA risk analysis technique was applied to the treatment process of GammaPod™, a new stereotactic radiotherapy technology. Application of systematic risk analysis methods is projected to lead to improved quality of GammaPod™ treatments.Ying Niu and Cedric Yu are affiliated with Xcision Medical Systems.
WE‐H‐BRC‐08: Examining Credentialing Criteria and Poor Performance Indicators for IROC Houston's Anthropomorphic Head and Neck Phantom
Authors: Carson M , Molineu A , Taylor P , Followill D , Kry S 
Published: July 2016

Purpose:To analyze the most recent results of IROC Houston's anthropomorphic H&N phantom to determine the nature of failing irradiations and the feasibility of altering pass/fail credentialing criteria.Methods:IROC Houston's H&N phantom, used for IMRT credentialing for NCI‐sponsored clinical trials, requires that an institution's treatment plan must agree with measurement within 7% (TLD doses) and ≥85% pixels must pass 7%/4 mm gamma analysis. 156 phantom irradiations (November 2014 – October 2015) were re‐evaluated using tighter criteria: 1) 5% TLD and 5%/4 mm, 2) 5% TLD and 5%/3 mm, 3) 4% TLD and 4%/4 mm, and 4) 3% TLD and 3%/3 mm. Failure/poor performance rates were evaluated with respect to individual film and TLD performance by location in the phantom. Overall poor phantom results were characterized qualitatively as systematic (dosimetric) errors, setup errors/positional shifts, global but non‐systematic errors, and errors affecting only a local region.Results:The pass rate for these phantoms using current criteria is 90%. Substituting criteria 1ߝ4 reduces the overall pass rate to 77%, 70%, 63%, and 37%, respectively. Statistical analyses indicated the probability of noise‐induced TLD failure at the 5% criterion was <0.5%. Using criteria 1, TLD results were most often the cause of failure (86% failed TLD while 61% failed film), with most failures identified in the primary PTV (77% cases). Other criteria posed similar results. Irradiations that failed from film only were overwhelmingly associated with phantom shifts/setup errors (≥80% cases). Results failing criteria 1 were primarily diagnosed as systematic: 58% of cases. 11% were setup/positioning errors, 8% were global non‐systematic errors, and 22% were local errors.Conclusion:This study demonstrates that 5% TLD and 5%/4 mm gamma criteria may be both practically and theoretically achievable. Further work is necessary to diagnose and resolve dosimetric inaccuracy in these trials, particularly for systematic dose errors.This work is funded by NCI Grant CA180803.
WE‐H‐BRC‐09: Simulated Errors in Mock Radiotherapy Plans to Quantify the Effectiveness of the Physics Plan Review
Authors: Gopan O , Kalet A , Smith W , Hendrickson K , Kim M , Young L , Nyflot M , Chvetsov A , Phillips M , Ford E 
Published: July 2016

Purpose:A standard tool for ensuring the quality of radiation therapy treatments is the initial physics plan review. However, little is known about its performance in practice. The goal of this study is to measure the effectiveness of physics plan review by introducing simulated errors into “mock” treatment plans and measuring the performance of plan review by physicists.Methods:We generated six mock treatment plans containing multiple errors. These errors were based on incident learning system data both within the department and internationally (SAFRON). These errors were scored for severity and frequency. Those with the highest scores were included in the simulations (13 errors total). Observer bias was minimized using a multiple co‐correlated distractor approach. Eight physicists reviewed these plans for errors, with each physicist reviewing, on average, 3/6 plans. The confidence interval for the proportion of errors detected was computed using the Wilson score interval.Results:Simulated errors were detected in 65% of reviews [51–75%] (95% confidence interval [CI] in brackets). The following error scenarios had the highest detection rates: incorrect isocenter in DRRs/CBCT (91% [73–98%]) and a planned dose different from the prescribed dose (100% [61–100%]). Errors with low detection rates involved incorrect field parameters in record and verify system (38%, [18–61%]) and incorrect isocenter localization in planning system (29% [8–64%]). Though pre‐treatment QA failure was reliably identified (100%), less than 20% of participants reported the error that caused the failure.Conclusion:This is one of the first quantitative studies of error detection. Although physics plan review is a key safety measure and can identify some errors with high fidelity, others errors are more challenging to detect. This data will guide future work on standardization and automation. Creating new checks or improving existing ones (i.e., via automation) will help in detecting those errors with low detection rates.
WE‐H‐BRA‐02: Radiobiological Modeling of Tumor Control Probability (TCP) and Radiation‐Induced Pneumonitis (RP) for Lung Cancer Patients Treated with Monte Carlo‐Based Lung SBRT
Authors: Pokhrel D , Sood S , Shen X , Badkul R , McClinton C , Jiang H , Mitchell M , Lominska C , Wang F 
Published: July 2016

Purpose:To present radiobiological modeling of TCP using tumor size‐adjusted BED(s‐BED)and PTV(D99) to lung SBRT patients treated with X‐ray Voxel Monte Carlo(XVMC) algorithm, apply parameterized Lyman‐NTCP model to predict grade‐2 RP and subsequently, compare with clinical outcomes/observations.Methods:Dosimetric parameters and clinical follow‐up for XVMC‐based lung‐SBRT patients were retrospectively evaluated. Patients were treated at Novalis‐TX with hybrid(2 non‐coplanar partial‐arcs plus 3–6 static‐beams)plan using HD‐MLC/6MV‐SRS‐beam.For TCP,s‐BED modelling was utilized: TCP=EXP[sBED‐TCD50]/k/(1.0+EXP[sBED‐TCD50]/k), where k=31Gy corresponding to TCD50=0Gy and s‐BED was defined as BED10 minus 10 times the tumor diameter(in centimeters)by Ohri et al.(IJROBP,2012). For 2‐yr local‐control, we used more‐realistic MC‐computed PTVD99 as a predictive parameter, s‐BED(D99).Due to relatively shorter median follow‐up interval(12‐months),Kaplan‐Meier curves were generated to estimate 2‐yr observed local‐control and compared to predicted‐rate by TCP modeling. For NTCP, we employed parameterized Lyman‐NTCP model utilizing normal‐lung DVH and α/β=3Gy fitted to predict grade‐2 RP after lung‐SBRT.Results:Total 108 patients (137 tumors) treated for 35–70Gy in 3–5 fractions, either primary‐lung(n=74)or metastatic‐lung(n=53)tumors were included.F or the given prescription dose with MC‐computed MUs, 2‐yr local‐control rates with s‐BED(D99) was 87±8%. Kaplan‐Meier generated observed local‐control rate at 2‐yr was 87.5%,suggesting that PTV(D99) could be a potential predictor (p‐value=0.38). Observed vs predicted TCP for primary‐lung tumors and metastatic tumors were 97% vs 88±7% and 94% vs 86±9%.NTCP model predicted well for symptomatic‐RP with predicted vs observed (3±5% vs 2%). Radiographic and clinically significant RP was observed in 13% and 2% of patients. Higher rates of radiographic change were observed in patients who received >50Gy compared to ≤50Gy(24% vs 10%).Conclusion:Utilizing MC‐computed PTVD99, our TCP results were well correlated with clinical outcome. The predicted grade‐2 RP rate was comparable to clinical observations. Clinical application of these radiobiological models may potentially allow for target dose escalation and/or lung‐toxicity reduction. Further validation of these radiobiological models with longer follow up interval for large cohorts of lung‐SBRT patients is anticipated.