19 research outputs found

    Insight gained from responses to surveys on reference dosimetry practices

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    PURPOSE: To present the results and discuss potential insights gained through surveys on reference dosimetry practices. METHODS: Two surveys were sent to medical physicists to learn about the current state of reference dosimetry practices at radiation oncology clinics worldwide. A short survey designed to maximize response rate was made publicly available and distributed via the AAPM website and a medical physics list server. Another, much more involved survey, was sent to a smaller group of physicists to gain insight on detailed dosimetry practices. The questions were diverse, covering reference dosimetry practices on topics like measurements required for beam quality specification, the actual measurement of absorbed dose and ancillary equipment required like electrometers and environment monitoring measurements. RESULTS: There were 190 respondents to the short survey and seven respondents to the detailed survey. The diversity of responses indicates nonuniformity in reference dosimetry practices and differences in interpretation of reference dosimetry protocols. CONCLUSIONS: The results of these surveys offer insight on clinical reference dosimetry practices and will be useful in identifying current and future needs for reference dosimetry

    Application of systems and control theory-based hazard analysis to radiation oncology

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    Purpose: Both humans and software are notoriously challenging to account for in traditional hazard analysis models. The purpose of this work is to investigate and demonstrate the application of a new, extended accident causality model, called systems theoretic accident model and processes (STAMP), to radiation oncology. Specifically, a hazard analysis technique based on STAMP, system-theoretic process analysis (STPA), is used to perform a hazard analysis. Methods: The STPA procedure starts with the definition of high-level accidents for radiation oncology at the medical center and the hazards leading to those accidents. From there, the hierarchical safety control structure of the radiation oncology clinic is modeled, i.e., the controls that are used to prevent accidents and provide effective treatment. Using STPA, unsafe control actions (behaviors) are identified that can lead to the hazards as well as causal scenarios that can lead to the identified unsafe control. This information can be used to eliminate or mitigate potential hazards. The STPA procedure is demonstrated on a new online adaptive cranial radiosurgery procedure that omits the CT simulation step and uses CBCT for localization, planning, and surface imaging system during treatment. Results: The STPA procedure generated a comprehensive set of causal scenarios that are traced back to system hazards and accidents. Ten control loops were created for the new SRS procedure, which covered the areas of hospital and department management, treatment design and delivery, and vendor service. Eighty three unsafe control actions were identified as well as 472 causal scenarios that could lead to those unsafe control actions. Conclusions: STPA provides a method for understanding the role of management decisions and hospital operations on system safety and generating process design requirements to prevent hazards and accidents. The interaction of people, hardware, and software is highlighted. The method of STPA produces results that can be used to improve safety and prevent accidents and warrants further investigation.Varian Medical System

    Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach

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    Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI/MRA were included. A hybrid semi-automated and manual approach was used to label MRI/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI/MRA. Clinical validation of their utility in radiosurgery planning is warranted
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