Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
NeSVoR: implicit neural representation for slice-to-volume reconstruction in MRI
Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has
shown promise in imaging of moving subjects, eg, fetal MRI. However, existing slice-to …
shown promise in imaging of moving subjects, eg, fetal MRI. However, existing slice-to …
Artificial intelligence applied to fetal MRI: A scoping review of current research
R Meshaka, T Gaunt… - The British Journal of …, 2023 - academic.oup.com
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks
normally requiring human intelligence. A subset of AI, known as machine learning (ML) …
normally requiring human intelligence. A subset of AI, known as machine learning (ML) …
SVoRT: Iterative transformer for slice-to-volume registration in fetal brain MRI
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the
presence of almost unpredictable and often severe subject motion, is a challenging task that …
presence of almost unpredictable and often severe subject motion, is a challenging task that …
Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI
Abstract Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer
significant clinical benefits by combining the high sensitivity of 7T SWI for neurological …
significant clinical benefits by combining the high sensitivity of 7T SWI for neurological …
Fetal MRI Reconstruction by Global Diffusion and Consistent Implicit Representation
Although the utilization of multi-stacks can solve fetal MRI motion correction and artifact
removal problems, there are still problems of regional intensity heterogeneity, and global …
removal problems, there are still problems of regional intensity heterogeneity, and global …
Stop moving: MR motion correction as an opportunity for artificial intelligence
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …
seriously deteriorate the image quality. Various prospective and retrospective methods have …
Semi‐supervised super‐resolution of diffusion‐weighted images based on multiple references
H Guo, L Wang, Y Gu, J Zhang, Y Zhu - NMR in Biomedicine, 2023 - Wiley Online Library
Spatial resolution of diffusion tensor images is usually compromised to accelerate the
acquisitions, and the state‐of‐the‐art (SOTA) image super‐resolution (SR) reconstruction …
acquisitions, and the state‐of‐the‐art (SOTA) image super‐resolution (SR) reconstruction …
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
This chapter provides an overview of deep learning techniques for improving the spatial
resolution of MRI, ranging from convolutional neural networks, generative adversarial …
resolution of MRI, ranging from convolutional neural networks, generative adversarial …
A Robust and Efficient Framework for Slice-to-Volume Reconstruction: Application to Fetal MRI
J Xu - 2023 - dspace.mit.edu
Volumetric reconstruction in presence of motion is a challenging problem in medical
imaging. When imaging moving targets, many modalities are limited to fast 2D imaging …
imaging. When imaging moving targets, many modalities are limited to fast 2D imaging …