We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to operate a treatment planning system (TPS) to generate high-quality radiation therapy (RT) treatment pla...
PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reduc...
PURPOSE: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the neces...
This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodo...
PURPOSE: We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical ra...
BACKGROUND: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images gene...
The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realization of couch tracking in development. Multileaf collimators (MLCs) are a standard equipment for most c...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Mar 20, 2021
PURPOSE: To develop a deep learning model capable of producing clinically acceptable dose distributions for left-sided breast cancers for 3D-CRT while exploring the use of two-dimensional versus three-dimensional anatomical data.
OBJECTIVE: To estimate the epistemic (or fuzzy) uncertainty, arising due to limited data samples in the measurement of the output factors (OFs) of the small fields using Fuzzy Set Theory (FST).
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