Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)
Purpose To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V). Methods Sixty-two patients underwent routine noncontrast brain CT scans and datasets were r...
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| Veröffentlicht in: | Neuroradiology 2021-06, Vol.63 (6), p.905-912 |
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| creator | Kim, Injoong Kang, Hyunkoo Yoon, Hyun Jung Chung, Bo Mi Shin, Na-Young |
| description | Purpose
To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.
Results
There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all
P
|
| doi_str_mv | 10.1007/s00234-020-02574-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2449960418</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2528314407</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-12b43f55d2d86598fd046583bf755b67d1e6b04a7e4f202f7f9baa8eae8c81c03</originalsourceid><addsrcrecordid>eNp9kctu1TAQhi0EoofCC7BAltiUhWF8ixN21eFWqRISlG4tJxkfXOUkqe2Udsc7sOH5eBJMTymCBQvL1sw3_8z4J-Qxh-ccwLxIAEIqBgLK0UaxyztkxZUUjDcC7pJVyddMNgr2yIOUzgBAGmnukz0py0uDXJHvrxBnOqCLYxg3P75-a13Cnoat2yCN2E1jynHpcphG6qdI2-jCSNcnLwsyx-nilj1f3BDyFe2m7exiCX8J-TN1vZtzuECasssh5dC5gYaM0V1H_27ATnGiB4cfjz6w02cPyT3vhoSPbu598unN65P1O3b8_u3R-vCYdWWDzLholfRa96KvK93UvgdV6Vq23mjdVqbnWLWgnEHlBQhvfNM6V6PDuqt5B3KfHOx0yzbnC6ZstyF1OAxuxGlJVijVNBUoXhf06T_o2bTEsUxnhRa15EqBKZTYUV2cUoro7RzLF8Ury8H-8s3ufLPFN3vtm70sRU9upJd2i_1tyW-jCiB3QCqpcYPxT-__yP4EtYKmuw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2528314407</pqid></control><display><type>article</type><title>Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)</title><source>SpringerLink Journals - AutoHoldings</source><creator>Kim, Injoong ; Kang, Hyunkoo ; Yoon, Hyun Jung ; Chung, Bo Mi ; Shin, Na-Young</creator><creatorcontrib>Kim, Injoong ; Kang, Hyunkoo ; Yoon, Hyun Jung ; Chung, Bo Mi ; Shin, Na-Young</creatorcontrib><description>Purpose
To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.
Results
There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all
P
< 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all
P
< 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.
Conclusion
On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.</description><identifier>ISSN: 0028-3940</identifier><identifier>EISSN: 1432-1920</identifier><identifier>DOI: 10.1007/s00234-020-02574-x</identifier><identifier>PMID: 33037503</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Attenuation ; Basal ganglia ; Brain ; Computed tomography ; Datasets ; Deep learning ; Diagnostic Neuroradiology ; Ganglia ; High strength ; Image processing ; Image quality ; Image reconstruction ; Imaging ; Iterative methods ; Medical imaging ; Medicine ; Medicine & Public Health ; Neuroimaging ; Neurology ; Neuroradiology ; Neurosciences ; Neurosurgery ; Noise ; Noise reduction ; Parameters ; Quality assessment ; Radiology ; Sharpness ; Skull ; Statistics ; Substantia alba ; Substantia grisea</subject><ispartof>Neuroradiology, 2021-06, Vol.63 (6), p.905-912</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-12b43f55d2d86598fd046583bf755b67d1e6b04a7e4f202f7f9baa8eae8c81c03</citedby><cites>FETCH-LOGICAL-c375t-12b43f55d2d86598fd046583bf755b67d1e6b04a7e4f202f7f9baa8eae8c81c03</cites><orcidid>0000-0003-1157-6366</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/s00234-020-02574-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00234-020-02574-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33037503$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Injoong</creatorcontrib><creatorcontrib>Kang, Hyunkoo</creatorcontrib><creatorcontrib>Yoon, Hyun Jung</creatorcontrib><creatorcontrib>Chung, Bo Mi</creatorcontrib><creatorcontrib>Shin, Na-Young</creatorcontrib><title>Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)</title><title>Neuroradiology</title><addtitle>Neuroradiology</addtitle><addtitle>Neuroradiology</addtitle><description>Purpose
To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.
Results
There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all
P
< 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all
P
< 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.
Conclusion
On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.</description><subject>Attenuation</subject><subject>Basal ganglia</subject><subject>Brain</subject><subject>Computed tomography</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Diagnostic Neuroradiology</subject><subject>Ganglia</subject><subject>High strength</subject><subject>Image processing</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Iterative methods</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroimaging</subject><subject>Neurology</subject><subject>Neuroradiology</subject><subject>Neurosciences</subject><subject>Neurosurgery</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Parameters</subject><subject>Quality assessment</subject><subject>Radiology</subject><subject>Sharpness</subject><subject>Skull</subject><subject>Statistics</subject><subject>Substantia alba</subject><subject>Substantia grisea</subject><issn>0028-3940</issn><issn>1432-1920</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kctu1TAQhi0EoofCC7BAltiUhWF8ixN21eFWqRISlG4tJxkfXOUkqe2Udsc7sOH5eBJMTymCBQvL1sw3_8z4J-Qxh-ccwLxIAEIqBgLK0UaxyztkxZUUjDcC7pJVyddMNgr2yIOUzgBAGmnukz0py0uDXJHvrxBnOqCLYxg3P75-a13Cnoat2yCN2E1jynHpcphG6qdI2-jCSNcnLwsyx-nilj1f3BDyFe2m7exiCX8J-TN1vZtzuECasssh5dC5gYaM0V1H_27ATnGiB4cfjz6w02cPyT3vhoSPbu598unN65P1O3b8_u3R-vCYdWWDzLholfRa96KvK93UvgdV6Vq23mjdVqbnWLWgnEHlBQhvfNM6V6PDuqt5B3KfHOx0yzbnC6ZstyF1OAxuxGlJVijVNBUoXhf06T_o2bTEsUxnhRa15EqBKZTYUV2cUoro7RzLF8Ury8H-8s3ufLPFN3vtm70sRU9upJd2i_1tyW-jCiB3QCqpcYPxT-__yP4EtYKmuw</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Kim, Injoong</creator><creator>Kang, Hyunkoo</creator><creator>Yoon, Hyun Jung</creator><creator>Chung, Bo Mi</creator><creator>Shin, Na-Young</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><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>8G5</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>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1157-6366</orcidid></search><sort><creationdate>20210601</creationdate><title>Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)</title><author>Kim, Injoong ; Kang, Hyunkoo ; Yoon, Hyun Jung ; Chung, Bo Mi ; Shin, Na-Young</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-12b43f55d2d86598fd046583bf755b67d1e6b04a7e4f202f7f9baa8eae8c81c03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Attenuation</topic><topic>Basal ganglia</topic><topic>Brain</topic><topic>Computed tomography</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Diagnostic Neuroradiology</topic><topic>Ganglia</topic><topic>High strength</topic><topic>Image processing</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Iterative methods</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroimaging</topic><topic>Neurology</topic><topic>Neuroradiology</topic><topic>Neurosciences</topic><topic>Neurosurgery</topic><topic>Noise</topic><topic>Noise reduction</topic><topic>Parameters</topic><topic>Quality assessment</topic><topic>Radiology</topic><topic>Sharpness</topic><topic>Skull</topic><topic>Statistics</topic><topic>Substantia alba</topic><topic>Substantia grisea</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Injoong</creatorcontrib><creatorcontrib>Kang, Hyunkoo</creatorcontrib><creatorcontrib>Yoon, Hyun Jung</creatorcontrib><creatorcontrib>Chung, Bo Mi</creatorcontrib><creatorcontrib>Shin, Na-Young</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences 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Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace 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>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Neuroradiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Injoong</au><au>Kang, Hyunkoo</au><au>Yoon, Hyun Jung</au><au>Chung, Bo Mi</au><au>Shin, Na-Young</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)</atitle><jtitle>Neuroradiology</jtitle><stitle>Neuroradiology</stitle><addtitle>Neuroradiology</addtitle><date>2021-06-01</date><risdate>2021</risdate><volume>63</volume><issue>6</issue><spage>905</spage><epage>912</epage><pages>905-912</pages><issn>0028-3940</issn><eissn>1432-1920</eissn><abstract>Purpose
To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning–based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).
Methods
Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.
Results
There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all
P
< 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all
P
< 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.
Conclusion
On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33037503</pmid><doi>10.1007/s00234-020-02574-x</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-1157-6366</orcidid></addata></record> |
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| identifier | ISSN: 0028-3940 |
| ispartof | Neuroradiology, 2021-06, Vol.63 (6), p.905-912 |
| issn | 0028-3940 1432-1920 |
| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Attenuation Basal ganglia Brain Computed tomography Datasets Deep learning Diagnostic Neuroradiology Ganglia High strength Image processing Image quality Image reconstruction Imaging Iterative methods Medical imaging Medicine Medicine & Public Health Neuroimaging Neurology Neuroradiology Neurosciences Neurosurgery Noise Noise reduction Parameters Quality assessment Radiology Sharpness Skull Statistics Substantia alba Substantia grisea |
| title | Deep learning–based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V) |
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