Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps.

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are fed data with known characteristics.

A convolutional neural network model for EPID-based non-transit dosimetry.

Purpose: To develop an alternative computational approach for EPID-based non-transit dosimetry using a convolutional neural network model.

Method: A U-net followed by a non-trainable layer named True Dose Modulation recovering the spatialized information was developed. The model was trained on 186 Intensity-Modulated Radiation Therapy Step & Shot beams from 36 treatment plans of different tumor locations to convert grayscale portal images into planar absolute dose distributions.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks.

Background: There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma.

Methods: This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020.

Technical note: GAMMORA, a free, open-source, and validated GATE-based model for Monte-Carlo simulations of the Varian TrueBeam.

Purpose: Monte Carlo (MC) is the reference computation method for medical physics. In radiotherapy, MC computations are necessary for some issues (such as assessing figures of merit, double checks, and dose conversions). A tool based on GATE is proposed to easily create full MC simulations of the Varian TrueBeam STx.

A study of the interplay effect in radiation therapy using a Monte-Carlo model.

Purpose: In modulated radiotherapy, breathing motion can lead to Interplay (IE) and Blurring (BE) effects that can modify the delivered dose. The aim of this work is to present the implementation, the validation and the use of an open-source Monte-Carlo (MC) model that computes the delivered dose including these motion effects.

Methods: The MC model of the Varian TrueBeam was implemented using GATE.