MR image reconstruction using cosupport constraints and group sparsity regularisation

Han, Yu; Du, Heng; Gao, Xiangzhen; Mei, Wenbo

doi:10.1049/iet-ipr.2016.0615

Cited by 17 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, there are numerous voxels near the course dividers that are bereft of flag, either from fat concealment or from blood stream pay. Such voxels don't give data about the predisposition field [18].…”

Section: Improve Quality Of Mr Images By Energy Optimizationmentioning

confidence: 99%

Correction of MRI Image Intenseness by Minimization of Energy Approach

Ali¹,

Kumar²

2017

IJSIP

View full text Add to dashboard Cite

show abstract

Section: Improve Quality Of Mr Images By Energy Optimizationmentioning

confidence: 99%

Correction of MRI Image Intenseness by Minimization of Energy Approach

Ali¹,

Kumar²

2017

IJSIP

View full text Add to dashboard Cite

show abstract

“…The available prior used in classical CS-MRI can be the sparsity in specific transform domains (e.g., gradient and wavelet) [2,5,6], as well as a more fixable sparse representation obtained from data via dictionary learning [7][8][9][10]. In addition, the structural prior information is drawing increased attention, because it can be acquired from a known high-resolution reference image [11][12][13] and introduces support information [14,15] or structural sparsity (e.g., group sparsity and block sparsity) [16][17][18] into the reconstruction model based on the union of subspaces theory [19,20].…”

Section: Introductionmentioning

confidence: 99%

Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training

Zhao

Zhang

Gan

2020

Sensors

View full text Add to dashboard Cite

Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data.

show abstract

“…Classic CS-MRI methods aim to accurately reconstruct MR images from highly under-sampled k-space data. Over the last ten years, plenty of classic CS-MRI methods [2][3][4][5] have been proposed to achieve accurate CS-MRI reconstruction. The issues encountered by the classic CS-MRI methods are that: (a) at present, it is difficult to find a suitable sparsifying transform or decomposition tool to accurately capture complex image details; and (b) the classic CS-MRI methods usually require multiple iterative calculations, which may result in relatively long reconstruction time and thus make the reconstruction not-realtime.…”

Section: Introductionmentioning

confidence: 99%

Spatial orthogonal attention generative adversarial network for MRI reconstruction

Zhou

Mei

et al. 2020

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose: Recent studies have witnessed that self-attention modules can better solve the vision understanding problems by capturing long-range dependencies. However, there are very few works designing a lightweight self-attention module to improve the quality of MRI reconstruction. Furthermore, it can be observed that several important self-attention modules (e.g., the non-local block) cause high computational complexity and need a huge number of GPU memory when the size of the input feature is large. The purpose of this study is to design a lightweight yet effective spatial orthogonal attention module (SOAM) to capture long-range dependencies, and develop a novel spatial orthogonal attention generative adversarial network, termed as SOGAN, to achieve more accurate MRI reconstruction. Methods: We first develop a lightweight SOAM, which can generate two small attention maps to effectively aggregate the long-range contextual information in vertical and horizontal directions, respectively. Then, we embed the proposed SOAMs into the concatenated convolutional autoencoders to form the generator of the proposed SOGAN. Results: The experimental results demonstrate that the proposed SOAMs improve the quality of the reconstructed MR images effectively by capturing long-range dependencies. Besides, compared with state-of-the-art deep learning-based CS-MRI methods, the proposed SOGAN reconstructs MR images more accurately, but with fewer model parameters. Conclusions: The proposed SOAM is a lightweight yet effective self-attention module to capture long-range dependencies, thus, can improve the quality of MRI reconstruction to a large extent. Besides, with the help of SOAMs, the proposed SOGAN outperforms the state-of-the-art deep learning-based CS-MRI methods.

show abstract

MR image reconstruction using cosupport constraints and group sparsity regularisation

Cited by 17 publications

References 51 publications

Correction of MRI Image Intenseness by Minimization of Energy Approach

Correction of MRI Image Intenseness by Minimization of Energy Approach

Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training

Spatial orthogonal attention generative adversarial network for MRI reconstruction

Contact Info

Product

Resources

About