PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) has made great progress in speeding up MRI imaging. The existing non-local self-similarity (NSS) prior based CS-MRI models mainly take similar image patches as the processing objects, this patch-level non-local sparse representation method can n...

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Main Authors: Hao Hou, Yuchen Shao, Yang Geng, Yingkun Hou, Peng Ding, Benzheng Wei
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10109723/
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author Hao Hou
Yuchen Shao
Yang Geng
Yingkun Hou
Peng Ding
Benzheng Wei
author_facet Hao Hou
Yuchen Shao
Yang Geng
Yingkun Hou
Peng Ding
Benzheng Wei
author_sort Hao Hou
collection DOAJ
description Compressed sensing magnetic resonance imaging (CS-MRI) has made great progress in speeding up MRI imaging. The existing non-local self-similarity (NSS) prior based CS-MRI models mainly take similar image patches as the processing objects, this patch-level non-local sparse representation method can not make full use of the self-similarity among pixels in the image, so it can not recover the weak edge information in the undersampled MRI image well and there will still be some artifacts. In this paper, a pixel-level non-local method based compressed sensing undersampled MRI image reconstruction method is introduced. First, zero filling is performed on the undersampled k-space data to obtain a full-size 2D signal, and IFFT is performed to obtain a preliminary reconstructed MRI image. Block-matching and row-matching are successively performed on the reconstructed image in turn to obtain similar pixel groups, so as to establish a better sparse representation under the non-local self-similarity (NSS) prior. The separable Haar transform is performed on similar pixel groups, and the hard threshold of the transform coefficients and Wiener filtering can effectively remove the artifacts introduced in the undersampled reconstructed MRI images. The proposed pixel-level non-local iterative thinning model based on compressed sensing theory can ensure the removal of artifacts and better restore the details in the image. The qualitative and quantitative results under different undersampling modes and undersampling rates prove the advantages of the proposed method in subjective visual quality and objective evaluation (peak signal to noise ratio and structure similarity index). The performance of this method is not only superior to the existing traditional CS-MRI methods, but also competitive with the existing deep neural network (DNN) based models. The code will be released at <uri>https://github.com/HaoHou-98/PNCS</uri>.
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spelling doaj.art-b426205598ab46af9bd9ae5c5646b6dd2023-05-05T23:00:20ZengIEEEIEEE Access2169-35362023-01-0111423894240210.1109/ACCESS.2023.327090010109723PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image ReconstructionHao Hou0https://orcid.org/0000-0002-7925-5756Yuchen Shao1Yang Geng2Yingkun Hou3https://orcid.org/0000-0003-2153-9040Peng Ding4Benzheng Wei5https://orcid.org/0000-0001-9640-4947College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao, ChinaQingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, ChinaSchool of Information Science and Technology, Taishan University, Tai&#x2019;an, ChinaCollege of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, ChinaQingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, ChinaCompressed sensing magnetic resonance imaging (CS-MRI) has made great progress in speeding up MRI imaging. The existing non-local self-similarity (NSS) prior based CS-MRI models mainly take similar image patches as the processing objects, this patch-level non-local sparse representation method can not make full use of the self-similarity among pixels in the image, so it can not recover the weak edge information in the undersampled MRI image well and there will still be some artifacts. In this paper, a pixel-level non-local method based compressed sensing undersampled MRI image reconstruction method is introduced. First, zero filling is performed on the undersampled k-space data to obtain a full-size 2D signal, and IFFT is performed to obtain a preliminary reconstructed MRI image. Block-matching and row-matching are successively performed on the reconstructed image in turn to obtain similar pixel groups, so as to establish a better sparse representation under the non-local self-similarity (NSS) prior. The separable Haar transform is performed on similar pixel groups, and the hard threshold of the transform coefficients and Wiener filtering can effectively remove the artifacts introduced in the undersampled reconstructed MRI images. The proposed pixel-level non-local iterative thinning model based on compressed sensing theory can ensure the removal of artifacts and better restore the details in the image. The qualitative and quantitative results under different undersampling modes and undersampling rates prove the advantages of the proposed method in subjective visual quality and objective evaluation (peak signal to noise ratio and structure similarity index). The performance of this method is not only superior to the existing traditional CS-MRI methods, but also competitive with the existing deep neural network (DNN) based models. The code will be released at <uri>https://github.com/HaoHou-98/PNCS</uri>.https://ieeexplore.ieee.org/document/10109723/Pixel-level non-localcompressed sensingundersampled MRI image reconstruction
spellingShingle Hao Hou
Yuchen Shao
Yang Geng
Yingkun Hou
Peng Ding
Benzheng Wei
PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
IEEE Access
Pixel-level non-local
compressed sensing
undersampled MRI image reconstruction
title PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
title_full PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
title_fullStr PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
title_full_unstemmed PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
title_short PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
title_sort pncs pixel level non local method based compressed sensing undersampled mri image reconstruction
topic Pixel-level non-local
compressed sensing
undersampled MRI image reconstruction
url https://ieeexplore.ieee.org/document/10109723/
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AT yingkunhou pncspixellevelnonlocalmethodbasedcompressedsensingundersampledmriimagereconstruction
AT pengding pncspixellevelnonlocalmethodbasedcompressedsensingundersampledmriimagereconstruction
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