Journal of applied clinical medical physics
Jun 7, 2022
PURPOSE: Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient ...
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)
May 21, 2022
PURPOSE: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed ...
BACKGROUND: Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning, however, poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam a...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Mar 26, 2022
BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to de...
Journal of applied clinical medical physics
Mar 9, 2022
PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explo...
PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 18, 2022
BACKGROUND AND PURPOSE: To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a ...
BACKGROUND: This paper describes the development of a predicted electronic portal imaging device (EPID) transmission image (TI) using Monte Carlo (MC) and deep learning (DL). The measured and predicted TI were compared for two-dimensional in vivo rad...
PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy.
BACKGROUND: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of t...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.