PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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 athttps://github.com/HaoHou-98/PNCS.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction
- Date Crossref
- 01/01/2023
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shandong University of Traditional Chinese Medicine Qingdao Academy of Chinese Medical Sciences pays non établi dans la noticeUniversité ou école supérieure
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Shandong University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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Taishan University pays non établi dans la noticeUniversité ou école supérieure
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Affiliated Hospital of Shandong University of Traditional Chinese Medicine pays non établi dans la noticeÉtablissement de santé
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College of Intelligence and Information Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Information Science and Technology pays non établi dans la noticeUniversité ou école supérieure
Qingdao Academy of Chinese Medical Sciences — Shandong University of Traditional Chinese Medicine, Shandong University of Science and Technology et Taishan University, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.