bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐EF‐207‐03: Design and Optimization of a CBCT Head Scanner for Detection of Acute Intracranial Hemorrhage
Authors: Xu J , Sisniega A , Zbijewski W , Dang H , Stayman J , Wang X , Foos DH , Aygun N , Koliatsos V , Siewerdsen JH 
Published: June 2015

Purpose:To design a dedicated x‐ray cone‐beam CT (CBCT) system suitable to deployment at the point‐of‐care and offering reliable detection of acute intracranial hemorrhage (ICH), traumatic brain injury (TBI), stroke, and other head and neck injuries.Methods:A comprehensive task‐based image quality model was developed to guide system design and optimization of a prototype head scanner suitable to imaging of acute TBI and ICH. Previously reported models were expanded to include the effects of x‐ray scatter correction necessary for detection of low contrast ICH and the contribution of bit depth (digitization noise) to imaging performance. Task‐based detectablity index provided the objective function for optimization of system geometry, x‐ray source, detector type, anti‐scatter grid, and technique at 10–25 mGy dose. Optimal characteristics were experimentally validated using a custom head phantom with 50 HU contrast ICH inserts imaged on a CBCT imaging bench allowing variation of system geometry, focal spot size, detector, grid selection, and x‐ray technique.Results:The model guided selection of system geometry with a nominal source‐detector distance 1100 mm and optimal magnification of 1.50. Focal spot size ∼0.6 mm was sufficient for spatial resolution requirements in ICH detection. Imaging at 90 kVp yielded the best tradeoff between noise and contrast. The model provided quantitation of tradeoffs between flat‐panel and CMOS detectors with respect to electronic noise, field of view, and readout speed required for imaging of ICH. An anti‐scatter grid was shown to provide modest benefit in conjunction with post‐acquisition scatter correction. Images of the head phantom demonstrate visualization of millimeter‐scale simulated ICH.Conclusions:Performance consistent with acute TBI and ICH detection is feasible with model‐based system design and robust artifact correction in a dedicated head CBCT system. Further improvements can be achieved with incorporation of model‐based iterative reconstruction techniques also within the scope of the task‐based optimization framework.David Foos and Xiaohui Wang are employees of Carestream Health
WE‐E‐201‐00: Practical Statistics for Medical Physicists
Authors: Sensakovic William 
Published: June 2015

Physicists are often expected to have a solid grounding in experimental design and statistical analysis, sometimes filling in when biostatisticians or other experts are not available for consultation. Unfortunately, graduate education on these topics is seldom emphasized and few opportunities for continuing education exist. Clinical physicists incorporate new technology and methods into their practice based on published literature. A poor understanding of experimental design and analysis could Result in inappropriate use of new techniques. Clinical physicists also improve current practice through quality initiatives that require sound experimental design and analysis. Academic physicists with a poor understanding of design and analysis may produce ambiguous (or misleading) results. This can Result in unnecessary rewrites, publication rejection, and experimental redesign (wasting time, money, and effort).This symposium will provide a practical review of error and uncertainty, common study designs, and statistical tests. Instruction will primarily focus on practical implementation through examples and answer questions such as: where would you typically apply the test/design and where is the test/design typically misapplied (i.e., common pitfalls)? An analysis of error and uncertainty will also be explored using biological studies and associated modeling as a specific use case.Learning Objectives:

Understand common experimental testing and clinical trial designs, what questions they can answer, and how to interpret the results
Determine where specific statistical tests are appropriate and identify common pitfalls
Understand the how uncertainty and error are addressed in biological testing and associated biological modeling.
WE‐E‐201‐01: Use and Abuse of Common Statistics in Radiological Physics
Authors: Labby Z. 
Published: June 2015

Physicists are often expected to have a solid grounding in experimental design and statistical analysis, sometimes filling in when biostatisticians or other experts are not available for consultation. Unfortunately, graduate education on these topics is seldom emphasized and few opportunities for continuing education exist. Clinical physicists incorporate new technology and methods into their practice based on published literature. A poor understanding of experimental design and analysis could Result in inappropriate use of new techniques. Clinical physicists also improve current practice through quality initiatives that require sound experimental design and analysis. Academic physicists with a poor understanding of design and analysis may produce ambiguous (or misleading) results. This can Result in unnecessary rewrites, publication rejection, and experimental redesign (wasting time, money, and effort).This symposium will provide a practical review of error and uncertainty, common study designs, and statistical tests. Instruction will primarily focus on practical implementation through examples and answer questions such as: where would you typically apply the test/design and where is the test/design typically misapplied (i.e., common pitfalls)? An analysis of error and uncertainty will also be explored using biological studies and associated modeling as a specific use case.Learning Objectives:

Understand common experimental testing and clinical trial designs, what questions they can answer, and how to interpret the results
Determine where specific statistical tests are appropriate and identify common pitfalls
Understand the how uncertainty and error are addressed in biological testing and associated biological modeling.
WE‐EF‐210‐08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch‐Based Anatomical Feature
Authors: Yang X , Rossi P , Jani A , Ogunleye T , Curran W , Liu T 
Published: June 2015

Purpose:Transrectal ultrasound (TRUS) is the standard imaging modality for the image‐guided prostate‐cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real‐time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal‐to‐noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge.Methods:The proposed segmentation method consists of two major stages: the training stage and the segmentation stage. During the training stage, patch‐based anatomical features are extracted from the registered training images with patient‐specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well‐trained KSVM and the output of this trained KSVM is the segmented prostate of this patient.Results:This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound‐guided prostate intervention.Conclusion:We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful tool for image‐guided interventions in prostate‐cancer diagnosis and treatment.This research is supported in part by DOD PCRP Award W81XWH‐13‐1‐0269, and National Cancer Institute (NCI) Grant CA114313.
WE‐F‐213‐00: Building, Maintaining and Improving Physics Residency Programs
Authors: Klein Eric 
Published: June 2015

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.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. Experiences on the first year of Medphys Match will be shared as well as strategies for placement following graduation.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.
WE‐F‐304‐00: Outcomes of Hypofractionated Treatments ‐ Results of the WGSBRT
Authors: Grimm Jimm 
Published: June 2015

Stereotactic Body Radiation Therapy (SBRT) was introduced clinically more than twenty years ago, and many subsequent publications have reported safety and efficacy data. The AAPM Working Group on Biological Effects of Hypofractionated Radiotherapy/SBRT (WGSBRT) extracted published treatment outcomes data from extensive literature searches to summarize and construct tumor control probability (TCP) and normal tissue complication probability (NTCP) models for six anatomical regions: Cranial, Head and Neck, Thoracic, Abdominal, Pelvic, and Spinal. In this session, we present the WGSBRT's work for cranial sites, and recurrent head and neck cancer. From literature‐based data and associated models, guidelines to aid with safe and effective hypofractionated radiotherapy treatment are being determined. Further, the ability of existing and proposed radiobiological models to fit these data is considered as to the ability to distinguish between the linear‐quadratic and alternative radiobiological models such as secondary cell death from vascular damage, immunogenic, or bystander effects. Where appropriate, specific model parameters are estimated. As described in “The lessons of QUANTEC,” (1), lack of adequate reporting standards continues to limit the amount of useful quantitative information that can be extracted from peer‐reviewed publications. Recommendations regarding reporting standards are considered, to enable such reviews to achieve more complete characterization of clinical outcomes.1 Jackson A, Marks LB, Bentzen SM, Eisbruch A, Yorke ED, Ten Haken RK, Constine LS, Deasy JO. The lessons of QUANTEC: recommendations for reporting and gathering data on dose‐volume dependencies of treatment outcome. Int J Radiat Oncol Biol Phys. 2010 Mar 1;76(3 Suppl):S155–60.Learning Objectives:

Describe the techniques, types of cancer and dose schedules used in treating recurrent H&N cancers with SBRT
List the radiobiological models that compete with the linear‐quadratic model in explaining the results of hypofractionated RT
Describe the dose/volume metrics that are considered safe in SBRT treatment of tumors near the optic structures.
Discuss the efficacy of hypofractionation and dosing schedules used in treating vestibular schwannomas
Identify some difficulties in modeling TCP and NTCP for cranial tumors treated with hypofractionation.
One moderator, Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and can analyze some of this data.No others have relevant conflicts of interest.
WE‐F‐304‐01: Overview of the Working Group On Stereotactic Body Radiation Therapy (WGSBRT)
Authors: Yorke E. 
Published: June 2015

Stereotactic Body Radiation Therapy (SBRT) was introduced clinically more than twenty years ago, and many subsequent publications have reported safety and efficacy data. The AAPM Working Group on Biological Effects of Hypofractionated Radiotherapy/SBRT (WGSBRT) extracted published treatment outcomes data from extensive literature searches to summarize and construct tumor control probability (TCP) and normal tissue complication probability (NTCP) models for six anatomical regions: Cranial, Head and Neck, Thoracic, Abdominal, Pelvic, and Spinal. In this session, we present the WGSBRT's work for cranial sites, and recurrent head and neck cancer. From literature‐based data and associated models, guidelines to aid with safe and effective hypofractionated radiotherapy treatment are being determined. Further, the ability of existing and proposed radiobiological models to fit these data is considered as to the ability to distinguish between the linear‐quadratic and alternative radiobiological models such as secondary cell death from vascular damage, immunogenic, or bystander effects. Where appropriate, specific model parameters are estimated. As described in “The lessons of QUANTEC,” (1), lack of adequate reporting standards continues to limit the amount of useful quantitative information that can be extracted from peer‐reviewed publications. Recommendations regarding reporting standards are considered, to enable such reviews to achieve more complete characterization of clinical outcomes.1 Jackson A, Marks LB, Bentzen SM, Eisbruch A, Yorke ED, Ten Haken RK, Constine LS, Deasy JO. The lessons of QUANTEC: recommendations for reporting and gathering data on dose‐volume dependencies of treatment outcome. Int J Radiat Oncol Biol Phys. 2010 Mar 1;76(3 Suppl):S155–60.Learning Objectives:

Describe the techniques, types of cancer and dose schedules used in treating recurrent H&N cancers with SBRT
List the radiobiological models that compete with the linear‐quadratic model in explaining the results of hypofractionated RT
Describe the dose/volume metrics that are considered safe in SBRT treatment of tumors near the optic structures.
Discuss the efficacy of hypofractionation and dosing schedules used in treating vestibular schwannomas
Identify some difficulties in modeling TCP and NTCP for cranial tumors treated with hypofractionation.
One moderator, Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and can analyze some of this data.No others have relevant conflicts of interest.
WE‐F‐304‐03: Optic Nerve/Chiasm Hypofractionated SRS/SRT Dose Tolerance
Authors: Milano M. 
Published: June 2015

Stereotactic Body Radiation Therapy (SBRT) was introduced clinically more than twenty years ago, and many subsequent publications have reported safety and efficacy data. The AAPM Working Group on Biological Effects of Hypofractionated Radiotherapy/SBRT (WGSBRT) extracted published treatment outcomes data from extensive literature searches to summarize and construct tumor control probability (TCP) and normal tissue complication probability (NTCP) models for six anatomical regions: Cranial, Head and Neck, Thoracic, Abdominal, Pelvic, and Spinal. In this session, we present the WGSBRT's work for cranial sites, and recurrent head and neck cancer. From literature‐based data and associated models, guidelines to aid with safe and effective hypofractionated radiotherapy treatment are being determined. Further, the ability of existing and proposed radiobiological models to fit these data is considered as to the ability to distinguish between the linear‐quadratic and alternative radiobiological models such as secondary cell death from vascular damage, immunogenic, or bystander effects. Where appropriate, specific model parameters are estimated. As described in “The lessons of QUANTEC,” (1), lack of adequate reporting standards continues to limit the amount of useful quantitative information that can be extracted from peer‐reviewed publications. Recommendations regarding reporting standards are considered, to enable such reviews to achieve more complete characterization of clinical outcomes.1 Jackson A, Marks LB, Bentzen SM, Eisbruch A, Yorke ED, Ten Haken RK, Constine LS, Deasy JO. The lessons of QUANTEC: recommendations for reporting and gathering data on dose‐volume dependencies of treatment outcome. Int J Radiat Oncol Biol Phys. 2010 Mar 1;76(3 Suppl):S155–60.Learning Objectives:

Describe the techniques, types of cancer and dose schedules used in treating recurrent H&N cancers with SBRT
List the radiobiological models that compete with the linear‐quadratic model in explaining the results of hypofractionated RT
Describe the dose/volume metrics that are considered safe in SBRT treatment of tumors near the optic structures.
Discuss the efficacy of hypofractionation and dosing schedules used in treating vestibular schwannomas
Identify some difficulties in modeling TCP and NTCP for cranial tumors treated with hypofractionation.
One moderator, Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and can analyze some of this data.No others have relevant conflicts of interest.
WE‐F‐304‐04: Radiosurgery for Vestibular Schwannomas: Tumor Control Probability Analyses and Recommended Reporting Standards
Authors: Soltys S. 
Published: June 2015

Stereotactic Body Radiation Therapy (SBRT) was introduced clinically more than twenty years ago, and many subsequent publications have reported safety and efficacy data. The AAPM Working Group on Biological Effects of Hypofractionated Radiotherapy/SBRT (WGSBRT) extracted published treatment outcomes data from extensive literature searches to summarize and construct tumor control probability (TCP) and normal tissue complication probability (NTCP) models for six anatomical regions: Cranial, Head and Neck, Thoracic, Abdominal, Pelvic, and Spinal. In this session, we present the WGSBRT's work for cranial sites, and recurrent head and neck cancer. From literature‐based data and associated models, guidelines to aid with safe and effective hypofractionated radiotherapy treatment are being determined. Further, the ability of existing and proposed radiobiological models to fit these data is considered as to the ability to distinguish between the linear‐quadratic and alternative radiobiological models such as secondary cell death from vascular damage, immunogenic, or bystander effects. Where appropriate, specific model parameters are estimated. As described in “The lessons of QUANTEC,” (1), lack of adequate reporting standards continues to limit the amount of useful quantitative information that can be extracted from peer‐reviewed publications. Recommendations regarding reporting standards are considered, to enable such reviews to achieve more complete characterization of clinical outcomes.1 Jackson A, Marks LB, Bentzen SM, Eisbruch A, Yorke ED, Ten Haken RK, Constine LS, Deasy JO. The lessons of QUANTEC: recommendations for reporting and gathering data on dose‐volume dependencies of treatment outcome. Int J Radiat Oncol Biol Phys. 2010 Mar 1;76(3 Suppl):S155–60.Learning Objectives:

Describe the techniques, types of cancer and dose schedules used in treating recurrent H&N cancers with SBRT
List the radiobiological models that compete with the linear‐quadratic model in explaining the results of hypofractionated RT
Describe the dose/volume metrics that are considered safe in SBRT treatment of tumors near the optic structures.
Discuss the efficacy of hypofractionation and dosing schedules used in treating vestibular schwannomas
Identify some difficulties in modeling TCP and NTCP for cranial tumors treated with hypofractionation.
One moderator, Dr. Grimm, designed and holds intellectual property rights to the DVH Evaluator software tool which is an FDA‐cleared product in commercial use, and can analyze some of this data.No others have relevant conflicts of interest.
WE‐F‐BRB‐00: New Developments in Knowledge‐Based Treatment Planning and Automation
Authors: McNutt Todd 
Published: June 2015

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, it is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3‐dimensional dose distribution and its relation to the critical anatomy.This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients.Learning Objectives:

Understand the role of standard terminologies, ontologies and data organization in oncology
Understand methods to capture clinical toxicity and outcomes in a clinical setting
Understand opportunities to learn from clinical data and its application to treatment planning
Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented