3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy.

Background And Purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.

Viscosimetric squeeze flow of suspensions.

The rheology of particle suspensions has been extensively explored in the case of a simple shear flow, but less in other flow configurations which are also important in practice. Here we investigate the behavior of a suspension in a squeeze flow, which we revisit using local pressure measurements to deduce the effective viscosity. The flow is generated by approaching a moving disk to a fixed wall at constant velocity in the low Reynolds number limit.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability.

Mortalin/Hspa9 involvement and therapeutic perspective in Parkinson's disease.

By controlling the proper folding of proteins imported into mitochondria and ensuring crosstalk between the reticulum and mitochondria to modulate intracellular calcium fluxes, Mortalin is a chaperone protein that plays crucial roles in neuronal homeostasis and activity. However, its expression and stability are strongly modified in response to cellular stresses, in particular upon altered oxidative conditions during neurodegeneration. Here, we report and discuss the abundant literature that has highlighted its contribution to the pathophysiology of Parkinson's disease, as well as its therapeutic and prognostic potential in this still incurable pathology.