A review on deep learning MRI reconstruction without fully sampled k-space
Background Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method
in clinical medicine, but it has always suffered from the problem of long acquisition time …
in clinical medicine, but it has always suffered from the problem of long acquisition time …
Compressed sensing MRI: a review from signal processing perspective
JC Ye - BMC Biomedical Engineering, 2019 - Springer
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
MoDL: Model-based deep learning architecture for inverse problems
We introduce a model-based image reconstruction framework with a convolution neural
network (CNN)-based regularization prior. The proposed formulation provides a systematic …
network (CNN)-based regularization prior. The proposed formulation provides a systematic …
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
-Space Deep Learning for Accelerated MRI
The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-
the-art compressed sensing approaches that directly interpolates the missing k-space data …
the-art compressed sensing approaches that directly interpolates the missing k-space data …
Deep convolutional framelets: A general deep learning framework for inverse problems
Recently, deep learning approaches with various network architectures have achieved
significant performance improvement over existing iterative reconstruction methods in …
significant performance improvement over existing iterative reconstruction methods in …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix
Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two
distinct reconstruction problems. Inspired by recent k-space interpolation methods, an …
distinct reconstruction problems. Inspired by recent k-space interpolation methods, an …
Multi‐shot sensitivity‐encoded diffusion data recovery using structured low‐rank matrix completion (MUSSELS)
Purpose To introduce a novel method for the recovery of multi‐shot diffusion weighted (MS‐
DW) images from echo‐planar imaging (EPI) acquisitions. Methods Current EPI‐based MS …
DW) images from echo‐planar imaging (EPI) acquisitions. Methods Current EPI‐based MS …
Acceleration of MR parameter mapping using annihilating filter‐based low rank hankel matrix (ALOHA)
Purpose MR parameter mapping is one of clinically valuable MR imaging techniques.
However, increased scan time makes it difficult for routine clinical use. This article aims at …
However, increased scan time makes it difficult for routine clinical use. This article aims at …