Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase
Purpose It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifact...
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Veröffentlicht in: | Medical physics (Lancaster) 2022-11, Vol.49 (11), p.7247-7261 |
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creator | Jang, Jinseong Chung, Yong Eun Kim, Sungwon Hwang, Dosik |
description | Purpose
It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium–enhanced MR imaging.
Methods
The proposed method was based on the construction of quality‐aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality‐aware features, the method calculated quantitative quality scores of gadoxetate disodium–enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5‐point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).
Results
Pearson's linear correlation coefficient (PLCC) and Spearman's rank–ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies
Conclusions
This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images. |
doi_str_mv | 10.1002/mp.15831 |
format | Article |
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It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium–enhanced MR imaging.
Methods
The proposed method was based on the construction of quality‐aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality‐aware features, the method calculated quantitative quality scores of gadoxetate disodium–enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5‐point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).
Results
Pearson's linear correlation coefficient (PLCC) and Spearman's rank–ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies
Conclusions
This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.</description><identifier>ISSN: 0094-2405</identifier><identifier>EISSN: 2473-4209</identifier><identifier>DOI: 10.1002/mp.15831</identifier><identifier>PMID: 35754384</identifier><language>eng</language><publisher>United States</publisher><subject>Automation ; gadoxetate disodium ; Humans ; image quality assessment ; Magnetic Resonance Imaging ; motion artifact ; Respiration ; transient severe respiratory motion</subject><ispartof>Medical physics (Lancaster), 2022-11, Vol.49 (11), p.7247-7261</ispartof><rights>2022 American Association of Physicists in Medicine.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2821-d096744f3e531a2615a3d8a8c8803b843cc3820b6a044f1d9bb6dd6c252d022d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fmp.15831$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmp.15831$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35754384$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jang, Jinseong</creatorcontrib><creatorcontrib>Chung, Yong Eun</creatorcontrib><creatorcontrib>Kim, Sungwon</creatorcontrib><creatorcontrib>Hwang, Dosik</creatorcontrib><title>Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase</title><title>Medical physics (Lancaster)</title><addtitle>Med Phys</addtitle><description>Purpose
It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium–enhanced MR imaging.
Methods
The proposed method was based on the construction of quality‐aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality‐aware features, the method calculated quantitative quality scores of gadoxetate disodium–enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5‐point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).
Results
Pearson's linear correlation coefficient (PLCC) and Spearman's rank–ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies
Conclusions
This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.</description><subject>Automation</subject><subject>gadoxetate disodium</subject><subject>Humans</subject><subject>image quality assessment</subject><subject>Magnetic Resonance Imaging</subject><subject>motion artifact</subject><subject>Respiration</subject><subject>transient severe respiratory motion</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10M9O1UAUBvCJ0cgVTHwCMks2xTN_2jtdEgJKAtEYWTenM6cwpO2UmSl6d67d-oY-ib1clJWrk5P8vm_xMfZOwLEAkO-H6ViURokXbCX1WhVaQv2SrQBqXUgN5R57k9IdAFSqhNdsT5XrUiujV-zn-dz3G45zDgNmb_n9jGP2nbfLF0YeOp4jjsnTmHmiB4rEI6XJR8whbvgQHhnGJYM2b_0NuvCdMmbizqfg_Dz8_vGLxlscLTl-9eWCuzn68Wabouix59MtJjpgrzrsE719uvvs-vzs6-nH4vLTh4vTk8vCSiNF4aCu1lp3ikolUFaiROUMGmsMqNZoZa0yEtoKYVHC1W1bOVdZWUoHUjq1z452vVMM9zOl3Aw-Wep7HCnMqZGVEVrLStXP1MaQUqSumaIfMG4aAc12-WaYmsflF3r41Dq3A7l_8O_UCyh24JvvafPfoubq867wDwSCj_4</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Jang, Jinseong</creator><creator>Chung, Yong Eun</creator><creator>Kim, Sungwon</creator><creator>Hwang, Dosik</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202211</creationdate><title>Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase</title><author>Jang, Jinseong ; Chung, Yong Eun ; Kim, Sungwon ; Hwang, Dosik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2821-d096744f3e531a2615a3d8a8c8803b843cc3820b6a044f1d9bb6dd6c252d022d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Automation</topic><topic>gadoxetate disodium</topic><topic>Humans</topic><topic>image quality assessment</topic><topic>Magnetic Resonance Imaging</topic><topic>motion artifact</topic><topic>Respiration</topic><topic>transient severe respiratory motion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jang, Jinseong</creatorcontrib><creatorcontrib>Chung, Yong Eun</creatorcontrib><creatorcontrib>Kim, Sungwon</creatorcontrib><creatorcontrib>Hwang, Dosik</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jang, Jinseong</au><au>Chung, Yong Eun</au><au>Kim, Sungwon</au><au>Hwang, Dosik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2022-11</date><risdate>2022</risdate><volume>49</volume><issue>11</issue><spage>7247</spage><epage>7261</epage><pages>7247-7261</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><abstract>Purpose
It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium–enhanced MR imaging.
Methods
The proposed method was based on the construction of quality‐aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality‐aware features, the method calculated quantitative quality scores of gadoxetate disodium–enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5‐point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).
Results
Pearson's linear correlation coefficient (PLCC) and Spearman's rank–ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies
Conclusions
This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.</abstract><cop>United States</cop><pmid>35754384</pmid><doi>10.1002/mp.15831</doi><tpages>15</tpages></addata></record> |
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subjects | Automation gadoxetate disodium Humans image quality assessment Magnetic Resonance Imaging motion artifact Respiration transient severe respiratory motion |
title | Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase |
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