Deep learning for tomographic image reconstruction

G Wang, JC Ye, B De Man - Nature machine intelligence, 2020‏ - nature.com
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

Cardiac MRI: state of the art

PS Rajiah, CJ François, T Leiner - Radiology, 2023‏ - pubs.rsna.org
Cardiac MRI plays an important role in the evaluation of cardiovascular diseases (CVDs),
including ischemic heart disease, cardiomyopathy, valvular disease, congenital disease …

Robust compressed sensing mri with deep generative priors

A Jalal, M Arvinte, G Daras, E Price… - Advances in …, 2021‏ - proceedings.neurips.cc
Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

Shadowdiffusion: When degradation prior meets diffusion model for shadow removal

L Guo, C Wang, W Yang, S Huang… - Proceedings of the …, 2023‏ - openaccess.thecvf.com
Recent deep learning methods have achieved promising results in image shadow removal.
However, their restored images still suffer from unsatisfactory boundary artifacts, due to the …

An overview of deep learning in medical imaging focusing on MRI

AS Lundervold, A Lundervold - arxiv preprint arxiv:1811.10052, 2018‏ - arxiv.org
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Artificial intelligence in radiation oncology

E Huynh, A Hosny, C Guthier, DS Bitterman… - Nature Reviews …, 2020‏ - nature.com
Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is
practised. AI platforms excel in recognizing complex patterns in medical data and provide a …

[HTML][HTML] Swin transformer for fast MRI

J Huang, Y Fang, Y Wu, H Wu, Z Gao, Y Li, J Del Ser… - Neurocomputing, 2022‏ - Elsevier
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can
produce high-resolution and reproducible images. However, a long scanning time is …

fastMRI: An open dataset and benchmarks for accelerated MRI

J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang… - arxiv preprint arxiv …, 2018‏ - arxiv.org
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the
potential to reduce medical costs, minimize stress to patients and make MRI possible in …

fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning

F Knoll, J Zbontar, A Sriram, MJ Muckley… - Radiology: Artificial …, 2020‏ - pubs.rsna.org
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated
MR Image Reconstruction Using Machine Learning | Radiology: Artificial Intelligence RSNA …

Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline

L Henschel, S Conjeti, S Estrada, K Diers, B Fischl… - NeuroImage, 2020‏ - Elsevier
Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …