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 …

In recent years, generative adversarial networks (GANs) have gained tremendous popularity
for various imaging related tasks such as artificial image generation to support AI training …

Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …

Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …

Background Missing MRI sequences represent an obstacle in the development and use of
deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing …

MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice.
Diffusion model has emerged as an effective technique for image synthesis by modelling …

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …

Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …