bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH‐A‐210A‐01: Management of Interventional Equipment and Suite: Cradle to Grave
Authors: Strauss K 
Published: April 2010

The life of any fluoroscopic imaging device begins with someone's perceived need for it and ends when it is removed from the clinical environment. In between these “cradle and grave” milestones, the machine must meet clinical objectives, provide good image quality, and reduce radiation dose to both patients and personnel. An organized program that achieves cost effective quality improvement is required to achieve these objectives. The first step identifies the clinical requirements of the imager. This identification assists obtaining adequate project funding. One acquires the imager by identifying the vendor of choice, negotiating the purchase, and issuing a purchase contract. One concurrently must plan an effective and efficient supporting facility followed by monitoring of the progress of renovation/construction. After the unit is installed, the owner acceptance tests the imager followed by staff training. A comprehensive program of routine testing, maintenance, repair, and record keeping to assure equipment performance begins after commissioning of the unit and continues throughout its life.Learning Objectives1. Understand how to help physicians identify equipment attributes that address clinical needs.2. Understand important steps in selecting, negotiating, and contracting the purchase of imaging equipment.3. Understand basic elements of facility design necessary to support selected imaging equipment.4. Understand the objectives of acceptance testing of state‐of‐the‐art imaging equipment.5. Understand the basic elements of a program to assure equipment performance after its commissioning.
TH‐A‐213A‐01: Multi‐Modality Image‐Guided Near Infrared Spectroscopy: Optimization and Clinical Applications
Authors: Srinivasan S , Pogue BW , Carpenter C , Davis SC , Paulsen KD 
Published: April 2010

Image‐guided near infrared spectroscopy (IG‐NIRS) provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical near infrared technologies, giving information directly relating to the vascular and metabolic status of tissue in‐vivo. The resultant estimates of total hemoglobin, oxygen saturation, water, lipids and scatter provide a window towards understanding the mechanisms of cancer in terms of angiogenesis, hypoxia, changes in the interstitium and cell organelle structural changes. This type of spectroscopy has been applied for breast cancer diagnosis and treatment monitoring, as well as image‐guided fluorescence in small‐animals.Optimization of these systems is essential to provide quantitative and accurate spectroscopy. This optimization encompasses system design for simultaneous multi‐modality image acquisition, methods for intelligently combining spatial anatomical structure from MRI/CT into optical recovery, image segmentation, visualization and interpretation of novel combined optical and MRI/CT parameters.This talk will provide an over‐view of these aspects of multi‐modality imaging as well as results from in‐vivo clinical applications.Learning Objectives:1. Understanding the rationale for multi‐modality IG‐NIRS systems2. Understanding the type of information and contrast available through these systems3. Understanding the challenges towards clinical use of these systems.
TH‐A‐213A‐02: Noninvasive Monitoring of Brain Tumor Therapy
Authors: Bullitt E 
Published: April 2010

Monitoring the efficacy of brain tumor therapy is a clinical dilemma. Results of biopsy provide the most reliable indicator but biopsy is too invasive for frequent use. The clinical standard of practice is to perform serial magnetic resonance scans and to assess changes to tumor “size” as seen on gadolinium enhanced images, with “size” measured in one, two, or three dimensions. This review discusses the advantages and disadvantages of the three measurement methods as well as some of the effects of voxel size on the calculation. Problems inherent to the use of gadolinium enhanced images include the effect of steroids and the confounding appearance of radiation necrosis. The limitations of the approach have led multiple groups to explore alternative methods of assessing tumor activity.Learning Objectives:1. Understand the clinical standard of practice of monitoring brain tumor therapy from T1 gadolinium enhanced images and its strengths/weaknesses.2. Understand some of the different experimental approaches, often involving different image types, to assessing tumor activity.
TH‐B‐BRC‐01: Tools for IMRT Commissioning: Static and Rotating Gantries
Authors: Ezzell G , Yin F , Xiao Y 
Published: April 2010

Commissioning an IMRT system refers to the initial verification by phantom studies that treatments can be planned, prepared, and delivered with sufficient accuracy. Our profession has struggled since the inception of IMRT to define what measurements to make and what degree of accuracy is sufficient. Two AAPM task groups have been dealing with complementary issues. AAPM TG 120 is finalizing a report on dose measurement techniques for IMRT validation. AAPM TG 119 is finalizing a report that recommends a specific set of tests for IMRT commissioning and presents the results of those tests from several institutions in order to illustrate the accuracy that is achievable. The report presents a methodology for other facilities to perform the tests and determine if their achieved accuracy is comparable to this reference baseline.The TG119 tests pose a range of optimization problems requiring simple to complex modulation patterns. The tests include mock prostate, head and neck, and para‐spinal geometries. Members of the group have planned and delivered the treatments using their local planning and delivery systems, and then assessed the resulting doses using broadly available dosimetry tools following a specified protocol. Measurements included ion chamber “point” doses and film dosimetry on selected planes for all fields irradiating the phantom. Institutions also evaluated dose distributions produced by individual fields using detector arrays, film, or EPID. Planar dosimetry results were analyzed with gamma criteria of 3% dose/ 3mm distance to agreement. The mean values and standard deviations of the results were used to develop confidence limits for the test results using the concept [Confidence Limit = |Mean| + 1.96 σ]. Other facilities can use the test protocol and results as a basis for comparison to this group.Learning objectives for this presentation:1. Describe the TG119 test suite, analysis protocol, and initial results.2. Demonstrate how it can be applied by other facilities, including delivery with rotating gantries.3. Provide examples of IMRT QA failures that can be identified and corrected using these processes.
TH‐C‐211A‐01: Designing Effective and Efficient Medical Imaging Physics Learning Activities
Authors: Sprawls P 
Published: April 2010

The clinically effective and safe utilization of the medical imaging modalities requires a staff of radiologists, technologists, and medical physicists with the education and training to select appropriate technology, evaluate equipment performance, optimize procedures, and manage patient safety. Educational activities (classes, conferences, self‐study, interactive experiences, etc.) to develop these competences are characterized by their effectiveness and their efficiency.The effectiveness of a learning activity determines outcome and the ability to perform functions as defined by the learning objectives. Effective learning is generally enhanced by providing an enriched learning environment that engages the learner with the imaging technology and the associated clinical activities. Elements that contribute to an enriched learning environment include clinical images, graphics for visualizing the invisible, interaction with feedback directly or through simulation, appropriate organization and structure of the engagement (concept development before quantization), and the guidance by a medical physicist as the learning facilitator. The elements and activities of the learning process should be organized to activate specific mental functions that collectively contribute to effective learning.In most cases the development and conduction of a highly effective and enriched learning activity are compromised by issues of efficiency. These include availability of: qualified medical physics faculty at the place of need, limitations on time to develop educational materials, logistical constraints imposed by scheduling and conflicting clinical activities, and adequate access to the imaging technology for educational purposes.In this symposium we will work through the general process of developing an educational activity to produce specific outcomes taking into account the issues of effectiveness and efficiency. A review of certain mental functions will provide the background for designing and organizing enriched learning environments. Applications of technology will be integrated into the design and demonstrated to enhance human performance of both the learners (students, residents) and the medical physics learning facilitators (teachers, mentors).Learning Objectives:1. Establish appropriate learning objectives based on the needs associated with the learning activity.2. Understand the various functions of the human brain that need to be considered in the development of learning activities.3. Describe the characteristics and give examples of an enriched learning environment.4. Design a learning environment that maximizes the effectiveness of the planned learning activity.5. Include in the design appropriate utilization of technology to increase the efficiency of learning activities.6. Apply the design principles developed in this symposium to your specific educational programs.
TH‐C‐211A‐02: Writing and Reviewing Medical Physics Papers
Authors: Hendee W , Karellas A , Rogers D , Slattery P 
Published: April 2010

There is an art to writing a scientific paper so that it communicates accurately, succinctly, and comprehensively. Developing this art comes with experience, and sharing that experience with younger physicists is an obligation of senior scientists, especially those with editorial responsibilities for the journal. In this workshop, the preparation of a scientific manuscript will be dissected so participants can appreciate how each part is developed and then assembled into a complete paper. Then the review process for the paper will be discussed, including how to examine a paper and write an insightful and constructive review. Finally, we will consider the challenge of accommodating the concerns and recommendations of a reviewer in preparing a revision of the paper.A second feature of the workshop will be a discussion of the process of electronic submission of a paper for consideration by Medical Physics. The web‐based PeerX‐Press engine for manuscript submission and management will be examined, with attention to special features such as EPAPS and line referencing. Finally, new features of Medical Physics will be explained, such as Vision 20/20 manuscripts, Physics Letters and the standardized formatting of book reviews.Learning Objectives:1. Improve the participants' abilities to write a scientific manuscript.2. Understand the review process for Medical Physics manuscripts and how to participate in and benefit from it.3. Appreciate the many features of the PeerX‐Press electronic management process for Medical Physics manuscripts.4. Develop a knowledge of new features of Medical Physics.
TH‐C‐BRD‐06: Analysis of the Mechanical Accuracy of Volumetric Modulated Arc Therapy (VMAT) Deliveries and Corresponding Dosimetric Effects
Authors: Matuszak M , DeCesare S , Gersten D , Yan D 
Published: April 2010

Purpose: Due to the continuous nature of VMAT deliveries, monitoring mechanical parameters and their effect on dosimetry is not straightforward. The purpose of this work is to present and evaluate a tool that compares real‐time VMAT delivery parameters with expected values, and utilize the tool to evaluate the mechanical accuracy of VMAT plans and judge the effect of mechanical errors on dosimetric QA. Method and Materials: An application was designed to record real‐time VMAT delivery information such as gantry position, leaf positions, and dose rate. An analysis utility was created to compare the delivery data to the DICOM‐RT plan data. Several example and clinical plans with and without simulated errors were delivered to a cylindrical QA phantom and analyzed. Results: For test plans where a 2cm MLC gap oscillated during arc delivery at various speeds, mechanical accuracy was good. The leaf gap absolute error was 0.24+/−0.26mm at maximum speed. For four clinical plans, the average mean gantry/leaf errors were 0.18 °/− 0.01mm and the average standard deviations were 0.35°/0.52mm. The analysis program correctly reported simulated errors in both test and clinical plans, including a 2mm shift of one leaf, a missing control point, and gantry lag. Dosimetric results were not significantly affected by the simulated errors, but the phantom was sensitive enough to detect them. Conclusion: Software tools that support monitoring and subsequent analysis of treatment machine parameters during VMAT compared to expected values have been developed. Clinical plans demonstrated good mechanical accuracy, and simulated errors were detected by the analysis software although dosimetric QA was relatively insensitive to them. These tools can complement VMAT dosimetric QA by helping determine the cause of dosimetric discrepancies, and routine use may also identify potential problems before they lead to dosimetric or other issues.Conflict of Interest: Supported in part by Elekta
TH‐C‐BRB‐01: Society‐Based Practice Quality Improvement Projects for Maintenance of Certification
Authors: Taylor M 
Published: April 2010

Practice Quality Improvement (PQI) is the final component of Maintenance of Certification (MOC) as required by the American Board of Radiology (ABR) and the American Board of Medical Specialties (ABMS). The first three components of MOC: professional standing, lifelong learning and self‐assessment, and the cognitive exam are less ambiguous than the fourth component, PQI, which is meant to provide a pathway for diplomates to critically self‐assess and improve clinical aspects of practice. The PQI process consists of six general steps: select a project and develop metrics, collect baseline data, review and analyze baseline data, implement an improvement plan, re‐measure baseline data, and report participation to the ABR. There are two types of projects that meet the ABR/ABMS requirements: Type I, which are individually‐based projects, developed and attested to by the dipomate; and Type II, which are an ABR endorsed, society‐provided template. AAPM Task Group 127 (MOC) has developed a Type II template that has been submitted to the ABR for endorsement.Learning Objectives:1. Understand the steps required to successfully meet the PQI requirements of MOC.2. Understand the differences between the two types of PQI projects and their essential components.3. Review and understand the ABR submitted Type II template developed by TG‐127.
TH‐C‐303A‐02: Clinical Data Evaluation of Fiducial‐Free Spine Tracking for CyberKnife Radiosurgery
Authors: Fu D , Zhang H , Wang H , Wang B , Kuduvalli G , Maurer CR 
Published: April 2010

Purpose: To evaluate the accuracy of the Xsight® Spine Tracking (XST) System in the CyberKnife® Robotic Radiosurgery System (Accuray Incorporated, Sunnyvale, CA) using retrospective analysis of clinical data. Method and Materials: The XST System performs patient alignment and frequent intra‐fractional tracking for spine radiosurgery. The 3 translations and 3 rotations of the 3D transformation are computed and used for treatment couch correction in patient setup and radiation beam position compensation during treatment delivery. The XST System eliminates the need for fiducials by using 2D–3D spine registration of two orthogonal X‐ray images and the planning CT image. Analysis was performed using image data acquired for 26 patients previously treated using fiducial tracking. The data consists of a CT image for each patient plus 4,480 X‐ray image pairs acquired during treatment. The cases cover the entire spinal column (3 cervical, 13 thoracic, 7 lumbar and 3 sacrum). Each patient had 4–6 metal fiducials implanted in vertebrae adjacent to the spine lesion being treated. Fiducial and XST tracking were performed for all X‐ray image pairs. The XST transformation errors were calculated by using the fiducial tracking results as the reference gold standard. Results: The error of each translation component is <0.5 mm in 20/26 patients, <1 mm in 25/26 and <1.5 mm in 26/26. The mean 3D translation error is 0.6 mm. The error of each rotation component is <0.5° in 9/26, <1° in 23/26, <1.5° in 25/26 and <2° in 26/26. The mean total rotation error is 0.8°. For 1 patient with >1 mm translation error and 3 patients with >1° rotation error, the fiducials are far from the center of region of interest, which degrades the fiducial tracking accuracy. Conclusion: The XST System robustly tracks all spine regions and accurately computes both translations and rotations.Research sponsored by Accuray Incorporated.
TH‐C‐304A‐01: Utilizing Shape Models Composed of Geometric Primitives for Organ Segmentation
Authors: Gering D , Lu W , Ruchala K , Olivera G 
Published: April 2010

Purpose: Image segmentation technology can automate the task of contouring organs for radiation treatment planning. A segmentation method using a five‐layer hierarchy, proposed in AAPM 2008, propagates information from the voxel layer up to voxel neighborhoods, then tissues, then organs, and finally organ systems. An improvement to the organ layer is proposed that uses shape models composed of geometric primitives. Method and Materials: The organ layer generally follows a sequence of six steps for each muscle and organ: (1.) Generate an ROI that limits the search space. (2.) Generate a field of candidate tissues within the ROI. (3.) On a slice‐by‐slice basis, recognize the object within the field that best matches expectations. (4.) Fit a geometric shape model to the cross‐section of the object on each slice. (5.) Smooth the shape parameters over all slices. (6.) Refine the object boundary by reconciling shape and image data. Consider how to define the shape model used by the fourth step. Use an ellipse for the bladder, rectum, and penile bulb. For the prostate, use a large circle with its bottom quarter cut off, and two small side‐by‐side circles with radii half that of the large circle lowered into position. For seminal vesicles, use a horizontal ellipse for the caudal‐most centimeter, which then splits into two ellipses that are permitted to tilt and slide laterally and posteriorly. Results: The segmentation method using geometric shape models has been trained and tested on 50 prostatic datasets. Processing time for volumes with roughly 90 slices, and 256×256 pixels per slice, is 40 seconds on a standard PC, without any human interaction. The segmentation results were verified by trained experts and redeemed to be acceptable. Conclusion: Shape models composed from geometric primitives are a useful method of regularizing the segmentation of muscles and organs.Sponsored by TomoTherapy.
TH‐D‐BRD‐01: New Developments in The Computational Environment for Radiotherapy Research (CERR) Software System
Authors: Apte A , Khullar D , Wu Y , Deasy J 
Published: April 2010

Purpose: CERR continues to be used across the world for radiotherapy research, and was downloaded over 1,000 times in the last year. We present recent improvements and changes to the system in response to user requests and research needs. Methods: New developments in the last year include: (1) integration of more powerful image registration tools (a multi‐scale demons algorithm), (2) the development of an extensive plan robustness analysis module (used to statistically simulate the effect of random, systematic, and contouring variations over a course of treatment), (3) generalizations to the java‐based DICOM input and output, (4) documentation now available via an extensive wiki page, and (5) a new tool that indicates the location of cold spots in a target volume by plotting the distance to the edge of a target volume for each voxel below a user‐selected dose threshold. Results: The image registration module has been stabilized and is being used extensively. The plan robustness module provides the statistical effect on DVH curves of presumed uncertainties. These ‘dose‐distance histograms’ give the user a simple graphical method for understanding the location of cold spots in target volumes. Documentation is now much more extensive. Conclusion: The updates to CERR will enhance user experience in using image registration and plan QA to validate treatment plan.Partially supported by NIH grants R01 CA11820 and R01 CA85181.
TH‐D‐BRD‐02: Convolution‐Superposition Dose Calculations with GPUs
Authors: Hissoiny S , Ozell B , Després P 
Published: April 2010

Purpose: To study the impact in terms of execution time and accuracy of using graphics hardware for calculating the dose in a treatment planning system. The architecture of Graphics Processing Units (GPU) is well suited for numerical tasks that are intrinsically parallel, such as dose calculations. Method and Materials: This work was made within the framework of PlanUNC, or PLUNC, a treatment planning system developed and maintained by the Department of Radiation Oncology of the University of North Carolina at Chapel Hill for research and development purposes. The objective was to transparently integrate a GPU dose calculation engine to PLUNC. The CUDA platform from NVIDIA was used for the GPU implementation. A convolution/superposition (CS) dose calculation algorithm was ported by developing programs (called kernels) that are executed on the GPU. Firstly, the CS engine of PLUNC was directly ported to the GPU, with the original code preserved as much as possible. Secondly, parts of the original algorithm were redesigned to better exploit the massively parallel architecture of GPUs. The numerical experiments were conducted with a NVIDIA GeForce GTX280 and an Intel Q6600 CPU. Results: Acceleration factors of 10× to 20× were achieved with the GPU implementation relative to the CPU version with the direct port of the CS algorithm. The numerical accuracy of the results was preserved with the GPU implementation. A 40× acceleration factor was obtained for the TERMA calculation subroutine, which was rewritten with the GPU architecture in mind. These acceleration factors were sufficient to significantly improve the responsiveness of the PLUNC graphical interface. Conclusion: This work demonstrates the potential of graphics hardware for dose calculation in treatment planning systems. This could in turn have a significant impact on optimization strategies for complex delivery techniques such as IMRT.Research sponsored by Varian Medical Systems, Inc.