Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance
Purpose Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of N...
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| Veröffentlicht in: | Japanese journal of radiology 2023-11, Vol.41 (11), p.1216-1225 |
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| creator | Oshima, Sonoko Fushimi, Yasutaka Miyake, Kanae Kawai Nakajima, Satoshi Sakata, Akihiko Okuchi, Sachi Hinoda, Takuya Otani, Sayo Numamoto, Hitomi Fujimoto, Koji Shima, Atsushi Nambu, Masahito Sawamoto, Nobukatsu Takahashi, Ryosuke Ueno, Kentaro Saga, Tsuneo Nakamoto, Yuji |
| description | Purpose
Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).
Materials and methods
We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwent NM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1 + dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for differentiating PD from non-PD were also compared between NEX1 and NEX1 + dDLR.
Results
Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1 + dDLR were significantly higher than in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1 + dDLR were 0.87 and 0.75, respectively, which had no significant difference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.
Conclusion
Image quality for NEX1 + dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD. |
| doi_str_mv | 10.1007/s11604-023-01452-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10613599</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2821340787</sourcerecordid><originalsourceid>FETCH-LOGICAL-c519t-635ce063925d78a5764334e122e8b4e64a88bd878b7ea191c980a9dfa50018b13</originalsourceid><addsrcrecordid>eNp9kctrFjEUxQdR7EP_ARcScOMmmtfk4UZKrVqoCKLgLmQy95svZSaZJjOVrv3HTf3q52PhKhfu756cw2maJ5S8oISol4VSSQQmjGNCRcuwudccUi0VpkR_vb-fFT1ojkq5JEQKLsTD5oAr1kqhyGHz_Q3EFEqIA3LznJPzW_QtLFvUA8xoBJdj3eHOFehRBp9iWfLql5Ai2qSMIqw5TTC6iuECsYQlXAP68On8FQqTGwBdrW4Myw1ysUd9cENMZQkezZDr_eSih0fNg40bCzy-e4-bL2_PPp--xxcf352fnlxg31KzYMlbD0Ryw9peadeqmoYLoIyB7gRI4bTueq10p8BRQ73RxJl-41pCqO4oP25e73TntZug9xCX7EY752o039jkgv17E8PWDunaUiIpb42pCs_vFHK6WqEsdgrFw1jjQ1qLZZpRLojSqqLP_kEv05pjzVcpQzg3SraVYjvK51RKhs3eDSX2tmS7K9nWku3Pku2ti6d_5tif_Gq1AnwHlLqKA-Tff_9H9gfg9LUs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2890339765</pqid></control><display><type>article</type><title>Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance</title><source>Springer Nature - Complete Springer Journals</source><creator>Oshima, Sonoko ; Fushimi, Yasutaka ; Miyake, Kanae Kawai ; Nakajima, Satoshi ; Sakata, Akihiko ; Okuchi, Sachi ; Hinoda, Takuya ; Otani, Sayo ; Numamoto, Hitomi ; Fujimoto, Koji ; Shima, Atsushi ; Nambu, Masahito ; Sawamoto, Nobukatsu ; Takahashi, Ryosuke ; Ueno, Kentaro ; Saga, Tsuneo ; Nakamoto, Yuji</creator><creatorcontrib>Oshima, Sonoko ; Fushimi, Yasutaka ; Miyake, Kanae Kawai ; Nakajima, Satoshi ; Sakata, Akihiko ; Okuchi, Sachi ; Hinoda, Takuya ; Otani, Sayo ; Numamoto, Hitomi ; Fujimoto, Koji ; Shima, Atsushi ; Nambu, Masahito ; Sawamoto, Nobukatsu ; Takahashi, Ryosuke ; Ueno, Kentaro ; Saga, Tsuneo ; Nakamoto, Yuji</creatorcontrib><description>Purpose
Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).
Materials and methods
We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwent NM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1 + dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for differentiating PD from non-PD were also compared between NEX1 and NEX1 + dDLR.
Results
Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1 + dDLR were significantly higher than in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1 + dDLR were 0.87 and 0.75, respectively, which had no significant difference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.
Conclusion
Image quality for NEX1 + dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD.</description><identifier>ISSN: 1867-1071</identifier><identifier>EISSN: 1867-108X</identifier><identifier>DOI: 10.1007/s11604-023-01452-9</identifier><identifier>PMID: 37256470</identifier><language>eng</language><publisher>Singapore: Springer Nature Singapore</publisher><subject>Deep learning ; Diagnostic systems ; Image acquisition ; Image contrast ; Image degradation ; Image processing ; Image quality ; Image reconstruction ; Imaging ; Locus coeruleus ; Magnetic resonance imaging ; Medical imaging ; Medicine ; Medicine & Public Health ; Movement disorders ; Neurodegenerative diseases ; Noise reduction ; Nuclear Medicine ; Original ; Original Article ; Parkinson's disease ; Quality assessment ; Radiology ; Radiotherapy ; Signal to noise ratio ; Substantia nigra</subject><ispartof>Japanese journal of radiology, 2023-11, Vol.41 (11), p.1216-1225</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c519t-635ce063925d78a5764334e122e8b4e64a88bd878b7ea191c980a9dfa50018b13</citedby><cites>FETCH-LOGICAL-c519t-635ce063925d78a5764334e122e8b4e64a88bd878b7ea191c980a9dfa50018b13</cites><orcidid>0000-0002-1982-3168</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/s11604-023-01452-9$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11604-023-01452-9$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51298</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37256470$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oshima, Sonoko</creatorcontrib><creatorcontrib>Fushimi, Yasutaka</creatorcontrib><creatorcontrib>Miyake, Kanae Kawai</creatorcontrib><creatorcontrib>Nakajima, Satoshi</creatorcontrib><creatorcontrib>Sakata, Akihiko</creatorcontrib><creatorcontrib>Okuchi, Sachi</creatorcontrib><creatorcontrib>Hinoda, Takuya</creatorcontrib><creatorcontrib>Otani, Sayo</creatorcontrib><creatorcontrib>Numamoto, Hitomi</creatorcontrib><creatorcontrib>Fujimoto, Koji</creatorcontrib><creatorcontrib>Shima, Atsushi</creatorcontrib><creatorcontrib>Nambu, Masahito</creatorcontrib><creatorcontrib>Sawamoto, Nobukatsu</creatorcontrib><creatorcontrib>Takahashi, Ryosuke</creatorcontrib><creatorcontrib>Ueno, Kentaro</creatorcontrib><creatorcontrib>Saga, Tsuneo</creatorcontrib><creatorcontrib>Nakamoto, Yuji</creatorcontrib><title>Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance</title><title>Japanese journal of radiology</title><addtitle>Jpn J Radiol</addtitle><addtitle>Jpn J Radiol</addtitle><description>Purpose
Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).
Materials and methods
We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwent NM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1 + dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for differentiating PD from non-PD were also compared between NEX1 and NEX1 + dDLR.
Results
Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1 + dDLR were significantly higher than in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1 + dDLR were 0.87 and 0.75, respectively, which had no significant difference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.
Conclusion
Image quality for NEX1 + dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD.</description><subject>Deep learning</subject><subject>Diagnostic systems</subject><subject>Image acquisition</subject><subject>Image contrast</subject><subject>Image degradation</subject><subject>Image processing</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Locus coeruleus</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Movement disorders</subject><subject>Neurodegenerative diseases</subject><subject>Noise reduction</subject><subject>Nuclear Medicine</subject><subject>Original</subject><subject>Original Article</subject><subject>Parkinson's disease</subject><subject>Quality assessment</subject><subject>Radiology</subject><subject>Radiotherapy</subject><subject>Signal to noise ratio</subject><subject>Substantia nigra</subject><issn>1867-1071</issn><issn>1867-108X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kctrFjEUxQdR7EP_ARcScOMmmtfk4UZKrVqoCKLgLmQy95svZSaZJjOVrv3HTf3q52PhKhfu756cw2maJ5S8oISol4VSSQQmjGNCRcuwudccUi0VpkR_vb-fFT1ojkq5JEQKLsTD5oAr1kqhyGHz_Q3EFEqIA3LznJPzW_QtLFvUA8xoBJdj3eHOFehRBp9iWfLql5Ai2qSMIqw5TTC6iuECsYQlXAP68On8FQqTGwBdrW4Myw1ysUd9cENMZQkezZDr_eSih0fNg40bCzy-e4-bL2_PPp--xxcf352fnlxg31KzYMlbD0Ryw9peadeqmoYLoIyB7gRI4bTueq10p8BRQ73RxJl-41pCqO4oP25e73TntZug9xCX7EY752o039jkgv17E8PWDunaUiIpb42pCs_vFHK6WqEsdgrFw1jjQ1qLZZpRLojSqqLP_kEv05pjzVcpQzg3SraVYjvK51RKhs3eDSX2tmS7K9nWku3Pku2ti6d_5tif_Gq1AnwHlLqKA-Tff_9H9gfg9LUs</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Oshima, Sonoko</creator><creator>Fushimi, Yasutaka</creator><creator>Miyake, Kanae Kawai</creator><creator>Nakajima, Satoshi</creator><creator>Sakata, Akihiko</creator><creator>Okuchi, Sachi</creator><creator>Hinoda, Takuya</creator><creator>Otani, Sayo</creator><creator>Numamoto, Hitomi</creator><creator>Fujimoto, Koji</creator><creator>Shima, Atsushi</creator><creator>Nambu, Masahito</creator><creator>Sawamoto, Nobukatsu</creator><creator>Takahashi, Ryosuke</creator><creator>Ueno, Kentaro</creator><creator>Saga, Tsuneo</creator><creator>Nakamoto, Yuji</creator><general>Springer Nature Singapore</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7TK</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1982-3168</orcidid></search><sort><creationdate>20231101</creationdate><title>Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance</title><author>Oshima, Sonoko ; Fushimi, Yasutaka ; Miyake, Kanae Kawai ; Nakajima, Satoshi ; Sakata, Akihiko ; Okuchi, Sachi ; Hinoda, Takuya ; Otani, Sayo ; Numamoto, Hitomi ; Fujimoto, Koji ; Shima, Atsushi ; Nambu, Masahito ; Sawamoto, Nobukatsu ; Takahashi, Ryosuke ; Ueno, Kentaro ; Saga, Tsuneo ; Nakamoto, Yuji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c519t-635ce063925d78a5764334e122e8b4e64a88bd878b7ea191c980a9dfa50018b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Deep learning</topic><topic>Diagnostic systems</topic><topic>Image acquisition</topic><topic>Image contrast</topic><topic>Image degradation</topic><topic>Image processing</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Locus coeruleus</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Movement disorders</topic><topic>Neurodegenerative diseases</topic><topic>Noise reduction</topic><topic>Nuclear Medicine</topic><topic>Original</topic><topic>Original Article</topic><topic>Parkinson's disease</topic><topic>Quality assessment</topic><topic>Radiology</topic><topic>Radiotherapy</topic><topic>Signal to noise ratio</topic><topic>Substantia nigra</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oshima, Sonoko</creatorcontrib><creatorcontrib>Fushimi, Yasutaka</creatorcontrib><creatorcontrib>Miyake, Kanae Kawai</creatorcontrib><creatorcontrib>Nakajima, Satoshi</creatorcontrib><creatorcontrib>Sakata, Akihiko</creatorcontrib><creatorcontrib>Okuchi, Sachi</creatorcontrib><creatorcontrib>Hinoda, Takuya</creatorcontrib><creatorcontrib>Otani, Sayo</creatorcontrib><creatorcontrib>Numamoto, Hitomi</creatorcontrib><creatorcontrib>Fujimoto, Koji</creatorcontrib><creatorcontrib>Shima, Atsushi</creatorcontrib><creatorcontrib>Nambu, Masahito</creatorcontrib><creatorcontrib>Sawamoto, Nobukatsu</creatorcontrib><creatorcontrib>Takahashi, Ryosuke</creatorcontrib><creatorcontrib>Ueno, Kentaro</creatorcontrib><creatorcontrib>Saga, Tsuneo</creatorcontrib><creatorcontrib>Nakamoto, Yuji</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 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Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Japanese journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oshima, Sonoko</au><au>Fushimi, Yasutaka</au><au>Miyake, Kanae Kawai</au><au>Nakajima, Satoshi</au><au>Sakata, Akihiko</au><au>Okuchi, Sachi</au><au>Hinoda, Takuya</au><au>Otani, Sayo</au><au>Numamoto, Hitomi</au><au>Fujimoto, Koji</au><au>Shima, Atsushi</au><au>Nambu, Masahito</au><au>Sawamoto, Nobukatsu</au><au>Takahashi, Ryosuke</au><au>Ueno, Kentaro</au><au>Saga, Tsuneo</au><au>Nakamoto, Yuji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance</atitle><jtitle>Japanese journal of radiology</jtitle><stitle>Jpn J Radiol</stitle><addtitle>Jpn J Radiol</addtitle><date>2023-11-01</date><risdate>2023</risdate><volume>41</volume><issue>11</issue><spage>1216</spage><epage>1225</epage><pages>1216-1225</pages><issn>1867-1071</issn><eissn>1867-108X</eissn><abstract>Purpose
Neuromelanin-sensitive MRI (NM-MRI) has proven useful for diagnosing Parkinson’s disease (PD) by showing reduced signals in the substantia nigra (SN) and locus coeruleus (LC), but requires a long scan time. The aim of this study was to assess the image quality and diagnostic performance of NM-MRI with a shortened scan time using a denoising approach with deep learning-based reconstruction (dDLR).
Materials and methods
We enrolled 22 healthy volunteers, 22 non-PD patients and 22 patients with PD who underwent NM-MRI, and performed manual ROI-based analysis. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in ten healthy volunteers were compared among images with a number of excitations (NEX) of 1 (NEX1), NEX1 images with dDLR (NEX1 + dDLR) and 5-NEX images (NEX5). Acquisition times for NEX1 and NEX5 were 3 min 12 s and 15 min 58 s, respectively. Diagnostic performances using the contrast ratio (CR) of the SN (CR_SN) and LC (CR_LC) and those by visual assessment for differentiating PD from non-PD were also compared between NEX1 and NEX1 + dDLR.
Results
Image quality analyses revealed that SNRs and CNRs of the SN and LC in NEX1 + dDLR were significantly higher than in NEX1, and comparable to those in NEX5. In diagnostic performance analysis, areas under the receiver operating characteristic curve (AUC) using CR_SN and CR_LC of NEX1 + dDLR were 0.87 and 0.75, respectively, which had no significant difference with those of NEX1. Visual assessment showed improvement of diagnostic performance by applying dDLR.
Conclusion
Image quality for NEX1 + dDLR was comparable to that of NEX5. dDLR has the potential to reduce scan time of NM-MRI without degrading image quality. Both 1-NEX NM-MRI with and without dDLR showed high AUCs for diagnosing PD by CR. The results of visual assessment suggest advantages of dDLR. Further tuning of dDLR would be expected to provide clinical merits in diagnosing PD.</abstract><cop>Singapore</cop><pub>Springer Nature Singapore</pub><pmid>37256470</pmid><doi>10.1007/s11604-023-01452-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-1982-3168</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
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| ispartof | Japanese journal of radiology, 2023-11, Vol.41 (11), p.1216-1225 |
| issn | 1867-1071 1867-108X |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10613599 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Deep learning Diagnostic systems Image acquisition Image contrast Image degradation Image processing Image quality Image reconstruction Imaging Locus coeruleus Magnetic resonance imaging Medical imaging Medicine Medicine & Public Health Movement disorders Neurodegenerative diseases Noise reduction Nuclear Medicine Original Original Article Parkinson's disease Quality assessment Radiology Radiotherapy Signal to noise ratio Substantia nigra |
| title | Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance |
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