As new radiotherapy treatment systems using MRI (rather than traditional CT) are being
developed, the accurate calculation of dose maps from MR imaging has become an …

We developed a machine-learning-based method generate good quality low dose CT using
a residual block concept and a self-attention strategy with a cycle-consistent adversarial …

We propose a learning method to generate synthetic CT (sCT) image for MRI-only radiation
treatment planning. The proposed method integrated a dense-block concept into a cycle …

Background: Synthetic computed tomography (sCT) has been proposed and increasingly
clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep …

The purpose of this work is to develop a reliable deep-learning-based method that is
capable of synthesizing needed CT from MRI for radiotherapy treatment planning …

Background and purpose MRI-only planning relies on dosimetrically accurate synthetic-CT
(sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of …

Purpose MRI is increasingly utilized for image‐guided radiotherapy due to its outstanding
soft‐tissue contrast and lack of ionizing radiation. However, geometric distortions caused by …

Background and purpose: The reliability of deep-learning-based methods for synthetic CT
(SCT) generation depends on magnetic resonance (MR)-CT registration errors and the …

Traditional magnetic resonance imaging (MRI) acquires three contrasts of T 1, T 2, and
proton density (PD), but only one contrast can be highlighted in an imaging process, which …

Fast and low-dose reconstructions of medical images are highly desired in clinical routines.
We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework …