Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck
Objectives To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). Materials and methods We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demons...
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| Veröffentlicht in: | Magma (New York, N.Y.) N.Y.), 2024-07, Vol.37 (3), p.439-447 |
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| creator | Fujima, Noriyuki Nakagawa, Junichi Kameda, Hiroyuki Ikebe, Yohei Harada, Taisuke Shimizu, Yukie Tsushima, Nayuta Kano, Satoshi Homma, Akihiro Kwon, Jihun Yoneyama, Masami Kudo, Kohsuke |
| description | Objectives
To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials and methods
We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results
Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (
p
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| doi_str_mv | 10.1007/s10334-023-01129-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2892658624</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2892658624</sourcerecordid><originalsourceid>FETCH-LOGICAL-c347t-6b346b4cee08802c825d1e560880d25ec39b22e55d0e1a4d1cfdb8a7ee2b80e43</originalsourceid><addsrcrecordid>eNp9UblOxDAQtRCI-wcokEsag48k65QIcUkr0UBtOfZk10tiL7YD4iP4ZxIWEBXVzOgdo5mH0Amj54zS2UViVIiCUC4IZYzXpNhC-0yUnMiqYtt_-j10kNKKUs5KKnbRnpjVsq4530cf9_06hlfowWccWux6vQD8MujO5XfsPLaubYfkgidv4BbLDPaL4_wCv7m8xH2w0JFGpxGwAGvcgY5-giOY4FOOg8mjHLchYnjV3aCnMU3L8hLwErTF2lvswTwfoZ1WdwmOv-sherq5fry6I_OH2_uryzkxophlUjWiqJrCAFApKTeSl5ZBWU2T5SUYUTecQ1laCkwXlpnWNlLPAHgjKRTiEJ1tfMfbXwZIWfUuGeg67SEMSXFZ86qUFZ-ofEM1MaQUoVXrOD4gvitG1RSD2sSgxhjUVwxqEp1--w9ND_ZX8vP3kSA2hDRCfgFRrcIQ_Xjzf7af3eaWEg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2892658624</pqid></control><display><type>article</type><title>Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Fujima, Noriyuki ; Nakagawa, Junichi ; Kameda, Hiroyuki ; Ikebe, Yohei ; Harada, Taisuke ; Shimizu, Yukie ; Tsushima, Nayuta ; Kano, Satoshi ; Homma, Akihiro ; Kwon, Jihun ; Yoneyama, Masami ; Kudo, Kohsuke</creator><creatorcontrib>Fujima, Noriyuki ; Nakagawa, Junichi ; Kameda, Hiroyuki ; Ikebe, Yohei ; Harada, Taisuke ; Shimizu, Yukie ; Tsushima, Nayuta ; Kano, Satoshi ; Homma, Akihiro ; Kwon, Jihun ; Yoneyama, Masami ; Kudo, Kohsuke</creatorcontrib><description>Objectives
To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials and methods
We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results
Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (
p
< 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (
p
= 0.002 ~
p
< 0.001).
Discussion
DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.</description><identifier>ISSN: 1352-8661</identifier><identifier>EISSN: 1352-8661</identifier><identifier>DOI: 10.1007/s10334-023-01129-4</identifier><identifier>PMID: 37989922</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Basic Science - Diffusion ; Biomedical Engineering and Bioengineering ; Computer Appl. in Life Sciences ; Deep Learning ; Diffusion Magnetic Resonance Imaging - methods ; Female ; Head - diagnostic imaging ; Head and Neck Neoplasms - diagnostic imaging ; Health Informatics ; Humans ; Image Interpretation, Computer-Assisted - methods ; Image Processing, Computer-Assisted - methods ; Imaging ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Neck - diagnostic imaging ; Parotid Gland - diagnostic imaging ; Radiology ; Research Article ; Retrospective Studies ; Signal-To-Noise Ratio ; Solid State Physics</subject><ispartof>Magma (New York, N.Y.), 2024-07, Vol.37 (3), p.439-447</ispartof><rights>The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-6b346b4cee08802c825d1e560880d25ec39b22e55d0e1a4d1cfdb8a7ee2b80e43</citedby><cites>FETCH-LOGICAL-c347t-6b346b4cee08802c825d1e560880d25ec39b22e55d0e1a4d1cfdb8a7ee2b80e43</cites><orcidid>0000-0001-9021-347X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10334-023-01129-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10334-023-01129-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37989922$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fujima, Noriyuki</creatorcontrib><creatorcontrib>Nakagawa, Junichi</creatorcontrib><creatorcontrib>Kameda, Hiroyuki</creatorcontrib><creatorcontrib>Ikebe, Yohei</creatorcontrib><creatorcontrib>Harada, Taisuke</creatorcontrib><creatorcontrib>Shimizu, Yukie</creatorcontrib><creatorcontrib>Tsushima, Nayuta</creatorcontrib><creatorcontrib>Kano, Satoshi</creatorcontrib><creatorcontrib>Homma, Akihiro</creatorcontrib><creatorcontrib>Kwon, Jihun</creatorcontrib><creatorcontrib>Yoneyama, Masami</creatorcontrib><creatorcontrib>Kudo, Kohsuke</creatorcontrib><title>Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck</title><title>Magma (New York, N.Y.)</title><addtitle>Magn Reson Mater Phy</addtitle><addtitle>MAGMA</addtitle><description>Objectives
To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials and methods
We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results
Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (
p
< 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (
p
= 0.002 ~
p
< 0.001).
Discussion
DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Basic Science - Diffusion</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Computer Appl. in Life Sciences</subject><subject>Deep Learning</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>Female</subject><subject>Head - diagnostic imaging</subject><subject>Head and Neck Neoplasms - diagnostic imaging</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Imaging</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neck - diagnostic imaging</subject><subject>Parotid Gland - diagnostic imaging</subject><subject>Radiology</subject><subject>Research Article</subject><subject>Retrospective Studies</subject><subject>Signal-To-Noise Ratio</subject><subject>Solid State Physics</subject><issn>1352-8661</issn><issn>1352-8661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UblOxDAQtRCI-wcokEsag48k65QIcUkr0UBtOfZk10tiL7YD4iP4ZxIWEBXVzOgdo5mH0Amj54zS2UViVIiCUC4IZYzXpNhC-0yUnMiqYtt_-j10kNKKUs5KKnbRnpjVsq4530cf9_06hlfowWccWux6vQD8MujO5XfsPLaubYfkgidv4BbLDPaL4_wCv7m8xH2w0JFGpxGwAGvcgY5-giOY4FOOg8mjHLchYnjV3aCnMU3L8hLwErTF2lvswTwfoZ1WdwmOv-sherq5fry6I_OH2_uryzkxophlUjWiqJrCAFApKTeSl5ZBWU2T5SUYUTecQ1laCkwXlpnWNlLPAHgjKRTiEJ1tfMfbXwZIWfUuGeg67SEMSXFZ86qUFZ-ofEM1MaQUoVXrOD4gvitG1RSD2sSgxhjUVwxqEp1--w9ND_ZX8vP3kSA2hDRCfgFRrcIQ_Xjzf7af3eaWEg</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Fujima, Noriyuki</creator><creator>Nakagawa, Junichi</creator><creator>Kameda, Hiroyuki</creator><creator>Ikebe, Yohei</creator><creator>Harada, Taisuke</creator><creator>Shimizu, Yukie</creator><creator>Tsushima, Nayuta</creator><creator>Kano, Satoshi</creator><creator>Homma, Akihiro</creator><creator>Kwon, Jihun</creator><creator>Yoneyama, Masami</creator><creator>Kudo, Kohsuke</creator><general>Springer International Publishing</general><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><orcidid>https://orcid.org/0000-0001-9021-347X</orcidid></search><sort><creationdate>20240701</creationdate><title>Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck</title><author>Fujima, Noriyuki ; Nakagawa, Junichi ; Kameda, Hiroyuki ; Ikebe, Yohei ; Harada, Taisuke ; Shimizu, Yukie ; Tsushima, Nayuta ; Kano, Satoshi ; Homma, Akihiro ; Kwon, Jihun ; Yoneyama, Masami ; Kudo, Kohsuke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-6b346b4cee08802c825d1e560880d25ec39b22e55d0e1a4d1cfdb8a7ee2b80e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Basic Science - Diffusion</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Computer Appl. in Life Sciences</topic><topic>Deep Learning</topic><topic>Diffusion Magnetic Resonance Imaging - methods</topic><topic>Female</topic><topic>Head - diagnostic imaging</topic><topic>Head and Neck Neoplasms - diagnostic imaging</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Imaging</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neck - diagnostic imaging</topic><topic>Parotid Gland - diagnostic imaging</topic><topic>Radiology</topic><topic>Research Article</topic><topic>Retrospective Studies</topic><topic>Signal-To-Noise Ratio</topic><topic>Solid State Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fujima, Noriyuki</creatorcontrib><creatorcontrib>Nakagawa, Junichi</creatorcontrib><creatorcontrib>Kameda, Hiroyuki</creatorcontrib><creatorcontrib>Ikebe, Yohei</creatorcontrib><creatorcontrib>Harada, Taisuke</creatorcontrib><creatorcontrib>Shimizu, Yukie</creatorcontrib><creatorcontrib>Tsushima, Nayuta</creatorcontrib><creatorcontrib>Kano, Satoshi</creatorcontrib><creatorcontrib>Homma, Akihiro</creatorcontrib><creatorcontrib>Kwon, Jihun</creatorcontrib><creatorcontrib>Yoneyama, Masami</creatorcontrib><creatorcontrib>Kudo, Kohsuke</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>Magma (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fujima, Noriyuki</au><au>Nakagawa, Junichi</au><au>Kameda, Hiroyuki</au><au>Ikebe, Yohei</au><au>Harada, Taisuke</au><au>Shimizu, Yukie</au><au>Tsushima, Nayuta</au><au>Kano, Satoshi</au><au>Homma, Akihiro</au><au>Kwon, Jihun</au><au>Yoneyama, Masami</au><au>Kudo, Kohsuke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck</atitle><jtitle>Magma (New York, N.Y.)</jtitle><stitle>Magn Reson Mater Phy</stitle><addtitle>MAGMA</addtitle><date>2024-07-01</date><risdate>2024</risdate><volume>37</volume><issue>3</issue><spage>439</spage><epage>447</epage><pages>439-447</pages><issn>1352-8661</issn><eissn>1352-8661</eissn><abstract>Objectives
To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials and methods
We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results
Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (
p
< 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (
p
= 0.002 ~
p
< 0.001).
Discussion
DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>37989922</pmid><doi>10.1007/s10334-023-01129-4</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-9021-347X</orcidid></addata></record> |
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| subjects | Adult Aged Aged, 80 and over Basic Science - Diffusion Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Deep Learning Diffusion Magnetic Resonance Imaging - methods Female Head - diagnostic imaging Head and Neck Neoplasms - diagnostic imaging Health Informatics Humans Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Imaging Male Medicine Medicine & Public Health Middle Aged Neck - diagnostic imaging Parotid Gland - diagnostic imaging Radiology Research Article Retrospective Studies Signal-To-Noise Ratio Solid State Physics |
| title | Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck |
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