Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Medical image registration is a vital component of many medical procedures, such as image-
guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Purpose To investigate a novel deep-learning network that synthesizes virtual contrast-
enhanced T1-weighted (vceT1w) magnetic resonance images (MRI) from multimodality …

Compared to computed tomography (CT), magnetic resonance imaging (MRI) is more
sensitive to acute ischemic stroke lesion. However, MRI is time-consuming, expensive, and …

Magnetic resonance imaging (MRI) is becoming increasingly important in precision
radiotherapy owing to its excellent soft‐tissue contrast and versatile scan options. Many …

Artificial intelligence (AI) is developing rapidly and has found widespread applications in
medicine, especially radiotherapy. This paper provides a brief overview of AI applications in …

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