Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury
This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of t...
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| description | This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference (P>0.05), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious (P |
| doi_str_mv | 10.1155/2021/6329020 |
| format | Article |
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In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference (P>0.05), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious (P<0.05). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences (P<0.05). In short, the algorithm in this study showed better denoising performance with the most retained image information. In addition, denoising MRI images based on the algorithm constructed in this study can improve the diagnostic accuracy of MI.</description><identifier>ISSN: 1058-9244</identifier><identifier>EISSN: 1875-919X</identifier><identifier>DOI: 10.1155/2021/6329020</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Decomposition ; Diagnosis ; Diagnostic systems ; Hospitals ; Injuries ; Magnetic resonance imaging ; Medical imaging ; Noise ; Noise reduction</subject><ispartof>Scientific programming, 2021-11, Vol.2021, p.1-9</ispartof><rights>Copyright © 2021 Qimao Fu et al.</rights><rights>Copyright © 2021 Qimao Fu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-6b034fe6ac2e3401c224edd5ad33d707b3d3afb517358a558208ff4bd06b37903</cites><orcidid>0000-0001-7391-0257 ; 0000-0001-7022-9925 ; 0000-0003-3012-9932 ; 0000-0002-9340-3639 ; 0000-0001-8132-2113 ; 0000-0002-3201-8585</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><contributor>Pallikonda Rajasekaran, M</contributor><contributor>M Pallikonda Rajasekaran</contributor><creatorcontrib>Fu, Qimao</creatorcontrib><creatorcontrib>Huang, Chuizhi</creatorcontrib><creatorcontrib>Chen, Yan</creatorcontrib><creatorcontrib>Jia, Nailong</creatorcontrib><creatorcontrib>Huang, Jinghui</creatorcontrib><creatorcontrib>Lin, Changkun</creatorcontrib><title>Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury</title><title>Scientific programming</title><description>This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference (P>0.05), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious (P<0.05). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences (P<0.05). In short, the algorithm in this study showed better denoising performance with the most retained image information. In addition, denoising MRI images based on the algorithm constructed in this study can improve the diagnostic accuracy of MI.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Decomposition</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Hospitals</subject><subject>Injuries</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Noise</subject><subject>Noise reduction</subject><issn>1058-9244</issn><issn>1875-919X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp9kU9v1DAQRyMEEqVw4wOMxBHSju04f47VtoWVdgWqisQtmsSTXS9Zu9gOSz8PX7RZtmdOvzk8vTm8LHsv8EIIrS8lSnFZKtmgxBfZmagrnTei-fFyvlHXeSOL4nX2JsYdoqgF4ln2d00bx8n2cMfRO3I9w3JPG-s2_5ZhcoYDrPwhvyP3E9aUgv0D1-y8jUfqatz4YNN2D9ZB2jJc21npo41AzsDNbxonStY78APc287SCN9GSkwT3Abq0xQYFn7fWccGDrMJ1uxs7KcIS7ebwuPb7NVAY-R3z3uefb-9uV98yVdfPy8XV6u8l02R8rJDVQxcUi9ZFSh6KQs2RpNRylRYdcooGjotKqVr0rqWWA9D0RksO1U1qM6zDyfvQ_C_Jo6p3fkpuPllK0ssS60qPFKfTlQffIyBh_Yh2D2Fx1Zge8zQHjO0zxlm_OMJ31pn6GD_Tz8Bf3mI6A</recordid><startdate>20211124</startdate><enddate>20211124</enddate><creator>Fu, Qimao</creator><creator>Huang, Chuizhi</creator><creator>Chen, Yan</creator><creator>Jia, Nailong</creator><creator>Huang, Jinghui</creator><creator>Lin, Changkun</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-7391-0257</orcidid><orcidid>https://orcid.org/0000-0001-7022-9925</orcidid><orcidid>https://orcid.org/0000-0003-3012-9932</orcidid><orcidid>https://orcid.org/0000-0002-9340-3639</orcidid><orcidid>https://orcid.org/0000-0001-8132-2113</orcidid><orcidid>https://orcid.org/0000-0002-3201-8585</orcidid></search><sort><creationdate>20211124</creationdate><title>Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury</title><author>Fu, Qimao ; Huang, Chuizhi ; Chen, Yan ; Jia, Nailong ; Huang, Jinghui ; Lin, Changkun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-6b034fe6ac2e3401c224edd5ad33d707b3d3afb517358a558208ff4bd06b37903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Decomposition</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Hospitals</topic><topic>Injuries</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Noise</topic><topic>Noise reduction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fu, Qimao</creatorcontrib><creatorcontrib>Huang, Chuizhi</creatorcontrib><creatorcontrib>Chen, Yan</creatorcontrib><creatorcontrib>Jia, Nailong</creatorcontrib><creatorcontrib>Huang, Jinghui</creatorcontrib><creatorcontrib>Lin, Changkun</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Scientific programming</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fu, Qimao</au><au>Huang, Chuizhi</au><au>Chen, Yan</au><au>Jia, Nailong</au><au>Huang, Jinghui</au><au>Lin, Changkun</au><au>Pallikonda Rajasekaran, M</au><au>M Pallikonda Rajasekaran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury</atitle><jtitle>Scientific programming</jtitle><date>2021-11-24</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1058-9244</issn><eissn>1875-919X</eissn><abstract>This study was carried out to explore the diagnostic effect of magnetic resonance imaging (MRI) based on the low-rank matrix (LRM) denoising algorithm under gradient sparse prior for the tibial plateau fracture (TPF) combined with meniscus injury (TPF + MI). In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed, which was compared with the anisotropic diffusion fusion (ADF) algorithm. After that, the LRM denoising algorithm based on gradient sparse prior was applied to the diagnosis of 112 patients with TPF + MI admitted to hospital, and the results were compared with those of the undenoised MRI image. Then, the incidence of patients with all kinds of different meniscus injury parting was observed. A total of 66 cases (58.93%) of meniscus tears (MT) were found, including 56 cases (50.00%) of lateral meniscus (LM), 10 cases (8.93%) of medial meniscus (MM), 16 cases (14.29%) of meniscus contusion (MC), and 18 cases (16.07%) of meniscus degenerative injury (MDI). The incidences of MI in Schatzker subtypes were 0%, 53.33% (24/45), 41.67% (5/12), 76.47% (13/17), 78.94% (15/19), and 23.53% (4/17), showing no statistically significant difference (P>0.05), but the incidence of MT was 54.46% (61/112), which was greatly higher than that of MC (15.18% (17/112)), and the difference was statistically obvious (P<0.05). The diagnostic specificity (93.83%) and accuracy (95.33%) of denoised MRI images were dramatically higher than those of undenoised MRI images, which were 78.34% and 71.23%, respectively, showing statistically observable differences (P<0.05). In short, the algorithm in this study showed better denoising performance with the most retained image information. 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| subjects | Algorithms Artificial intelligence Decomposition Diagnosis Diagnostic systems Hospitals Injuries Magnetic resonance imaging Medical imaging Noise Noise reduction |
| title | Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury |
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