Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy.

Background: Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely ATP (Adapt to Position) and ATS (Adapt to Shape), where the latter requires daily re-contouring to obtain a dose plan tailored to the daily anatomy. These steps are inherently resource-intensive, and quickly predicting the dose distribution and the dosimetric evaluation criteria while the patient is on the table could facilitate a fast selection of adaptation method and decrease the treatment times.

Purpose: In this work, we aimed to develop a deep-learning-based dose-prediction pipeline for prostate MR-Linac treatments.

Comparing multi-image and image augmentation strategies for deep learning-based prostate segmentation.

During MR-Linac-based adaptive radiotherapy, multiple images are acquired per patient. These can be applied in training deep learning networks to reduce annotation efforts. This study examined the advantage of using multiple versus single images for prostate treatment segmentation.

Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.

Background And Purpose: Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training.

Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy.

Background And Purpose: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.

Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma.

Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue.

Purpose: To assess whether radiotherapy induces changes in vascular permeability (K) and the fractional volume of the extravascular extracellular space (V) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given.

Material And Methods: Seventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out.