Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model

Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model

Background and purpose: Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions...

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Bibliographic Details
Main Authors: Michael Lempart, Hunor Benedek, Christian Jamtheim Gustafsson, Mikael Nilsson, Niklas Eliasson, Sven Bäck, Per Munck af Rosenschöld, Lars E. Olsson
Format: Article
Language:English
Published: Elsevier 2021-07-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Deep learning
Dose prediction
Volumetric modulated arc therapy
Machine learning
Deliverable treatment plans
Radiotherapy
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631621000439

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http://www.sciencedirect.com/science/article/pii/S2405631621000439

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