Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions
in neuro-oncology for the detection, characterization and monitoring of brain tumors …

Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues
with anatomical properties. The existence of completely aligned and paired multi-modality …

Objectives This study proposes and evaluates a deep learning method that predicts
surrogate images for contrast-enhanced T1 from multiparametric magnetic resonance …

Objectives The purpose of this study was to implement a state-of-the-art convolutional neural
network used to synthesize artificial T1-weighted (T1w) full-dose images from corresponding …

Purpose Gadolinium‐based contrast agents (GBCAs) are widely administrated in MR
imaging for diagnostic studies and treatment planning. Although GBCAs are generally …

Recently, deep learning (DL)-based methods have been proposed for the computational
reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects …

Contrast agents are commonly used to highlight blood vessels, organs, and other structures
in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However …

Objectives The aim of this study was to evaluate a deep learning method designed to
increase the contrast-to-noise ratio in contrast-enhanced gradient echo T1-weighted brain …

Objectives Reducing gadolinium-based contrast agents to lower costs, the environmental
impact of gadolinium-containing wastewater, and patient exposure is still an unresolved …

Recently, deep learning has been demonstrated to be feasible in eliminating the use of
gadoliniumbased contrast agents (GBCAs) through synthesizing gadolinium-free contrast …