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This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6., (© 2023 American Association of Physicists in Medicine.)

Magnetic resonance-guided focused ultrasound (MRgFUS) is a completely non-invasive technology that has been approved by FDA to treat several diseases. This report, prepared by the American Association of Physicist in Medicine (AAPM) Task Group 241, provides background on MRgFUS technology with a focus on clinical body MRgFUS systems. The report addresses the issues of interest to the medical physics community, specific to the body MRgFUS system configuration, and provides recommendations on how to successfully implement and maintain a clinical MRgFUS program. The following sections describe the key features of typical MRgFUS systems and clinical workflow and provide key points and best practices for the medical physicist. Commonly used terms, metrics and physics are defined and sources of uncertainty that affect MRgFUS procedures are described. Finally, safety and quality assurance procedures are explained, the recommended role of the medical physicist in MRgFUS procedures is described, and regulatory requirements for planning clinical trials are detailed. Although this report is limited in scope to clinical body MRgFUS systems that are approved or currently undergoing clinical trials in the United States, much of the material presented is also applicable to systems designed for other applications., (© 2021 American Association of Physicists in Medicine.)

Purpose: The American Association of Physicists in Medicine (AAPM) previously developed a research database consisting of the National Institutes of Health (NIH) grants that were awarded to its members. The purpose of this report is to classify these NIH grants into various medical physics subdisciplines and analyze the scope of AAPM member research., Methods: For this report, an algorithm classified grant topics into medical physics research subdisciplines (grants from 2002 to 2019 were analyzed). This algorithm utilized a search for common words and phrases within grant titles, keywords, abstracts, and activity codes to perform the classification. AAPM member grants were compared with non-AAPM member grants in various relevant subcategories to assess what percentage of these grants was held by AAPM members., Results: The percentage of AAPM member grants that included words relating to both imaging and therapy (image-guided therapy grants) increased from 13% (27/207) in 2002 to 27% (79/293) in 2019. The percentage of AAPM member grants utilizing words relating to artificial intelligence increased from 8% in 2002 to 20% in 2019. From 2002 to 2019, AAPM member grants referenced cancer more than all other diseases combined. The majority of AAPM member grants included words relating to clinical research (81% of grants in 2002 and 99% in 2019). When comparing AAPM member with non-AAPM member grants it was found that in 2019 AAPM members held a substantial fraction of all NIH grants that referenced stereotactic radiation therapies (41%), radionuclide therapies (10%), brachytherapies (35%), intensity-modulated radiation therapies (45%), and external beam particle therapies (55%). From 2002 to 2019, the percentage of AAPM membership holding NIH grants decreased for males (3.2% down to 2.3%) and increased for females (0.8% up to 1.3%) CONCLUSIONS: The majority of grants awarded to AAPM members focus on clinical research, which underlies the translational aspect of medical physics and suggests medical physicists are uniquely positioned to help translate new technologies such as artificial intelligence into the clinic. Since 2002, NIH grants awarded to AAPM members have increasingly referenced some form of image-guided therapy, suggesting opportunities for continued innovation of imaging technologies. A substantial fraction of all radiotherapy-related research grants were awarded to AAPM members, emphasizing the important role physicists have in developing radiotherapy-related treatments., (© 2021 American Association of Physicists in Medicine.)

Purpose: This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images., Methods: Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures., Results: The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72., Conclusion: The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource., (© 2018 American Association of Physicists in Medicine.)