research
∙
02/06/2023
DDM^2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Magnetic resonance imaging (MRI) is a common and life-saving medical ima...
share
research
∙
06/30/2021
ResViT: Residual vision transformers for multi-modal medical image synthesis
Multi-modal imaging is a key healthcare technology in the diagnosis and ...
research
∙
05/15/2021
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Supervised deep learning has swiftly become a workhorse for accelerated ...
research
∙
03/13/2021
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Deep neural networks (DNNs) have recently found emerging use in accelera...
research
∙
11/29/2020
Semi-Supervised Learning of Mutually Accelerated Multi-Contrast MRI Synthesis without Fully-Sampled Ground-Truths
This study proposes a novel semi-supervised learning framework for mutua...
research
∙
11/27/2020
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Magnetic resonance imaging (MRI) offers the flexibility to image a given...
research
∙
09/25/2019
mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis
Multi-contrast MRI protocols increase the level of morphological informa...
research
∙
05/27/2018
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of in...
research
∙
02/05/2018