Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms
Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acq...
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| Veröffentlicht in: | European radiology 2021-08, Vol.31 (8), p.5498-5506 |
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| creator | Oostveen, Luuk J. Meijer, Frederick J. A. de Lange, Frank Smit, Ewoud J. Pegge, Sjoert A. Steens, Stefan C. A. van Amerongen, Martin J. Prokop, Mathias Sechopoulos, Ioannis |
| description | Objectives
To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).
Methods
Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired
t
tests.
Results
For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.
Conclusions
With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.
Key Points
• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.
• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.
• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time. |
| doi_str_mv | 10.1007/s00330-020-07668-x |
| format | Article |
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To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).
Methods
Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired
t
tests.
Results
For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.
Conclusions
With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.
Key Points
• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.
• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.
• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07668-x</identifier><identifier>PMID: 33693996</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Computed Tomography ; Deep learning ; Diagnostic Radiology ; Differentiation (biology) ; Evaluation ; Image contrast ; Image processing ; Image quality ; Image reconstruction ; Imaging ; Internal Medicine ; Interventional Radiology ; Iterative methods ; Machine learning ; Medical imaging ; Medicine ; Medicine & Public Health ; Neuroradiology ; Noise ; Observers ; Radiology ; Sharpness ; Substantia alba ; Ultrasound</subject><ispartof>European radiology, 2021-08, Vol.31 (8), p.5498-5506</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. 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-c540t-7f04ec77ed4a8124cccf04e51b2a820f24cb0996b3cbe222d39ce1498b3d5eca3</citedby><cites>FETCH-LOGICAL-c540t-7f04ec77ed4a8124cccf04e51b2a820f24cb0996b3cbe222d39ce1498b3d5eca3</cites><orcidid>0000-0003-0445-9436</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/s00330-020-07668-x$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-020-07668-x$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33693996$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Oostveen, Luuk J.</creatorcontrib><creatorcontrib>Meijer, Frederick J. A.</creatorcontrib><creatorcontrib>de Lange, Frank</creatorcontrib><creatorcontrib>Smit, Ewoud J.</creatorcontrib><creatorcontrib>Pegge, Sjoert A.</creatorcontrib><creatorcontrib>Steens, Stefan C. A.</creatorcontrib><creatorcontrib>van Amerongen, Martin J.</creatorcontrib><creatorcontrib>Prokop, Mathias</creatorcontrib><creatorcontrib>Sechopoulos, Ioannis</creatorcontrib><title>Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).
Methods
Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired
t
tests.
Results
For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.
Conclusions
With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.
Key Points
• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.
• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.
• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.</description><subject>Algorithms</subject><subject>Computed Tomography</subject><subject>Deep learning</subject><subject>Diagnostic Radiology</subject><subject>Differentiation (biology)</subject><subject>Evaluation</subject><subject>Image contrast</subject><subject>Image processing</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Iterative methods</subject><subject>Machine learning</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Noise</subject><subject>Observers</subject><subject>Radiology</subject><subject>Sharpness</subject><subject>Substantia alba</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kc9u1DAQxi0EokvhBTggS1y4hPpfEueChJYWkCpxKWfLcWazqRI72E7V3nrhCXjDPkln2dJCDxwsSzO_-WY-fYS85uw9Z6w-SoxJyQom8NVVpYvLJ2TFlRQFZ1o9JSvWSF3UTaMOyIuUzhljDVf1c3IgZdXIpqlW5OcngJmOYKMffH9z_au1CToawQWfclxcHoKnk72iwzTHcAHUB19gM0ebMnUQoY12pOszBGyPGtSFabYRRXKgIW8hUrfECD4_VrVjH-KQt1N6SZ5t7Jjg1d1_SL6fHJ-tvxSn3z5_XX88LVypWC7qDVPg6ho6ZTUXyjm3q5S8FVYLtsFKy9BWK10LQohONg64anQruxKclYfkw153XtoJOgc7G6OZI94er0ywg_m344et6cOF0aJmuipR4N2dQAw_FkjZTENyMI7WQ1iSESVmUvOyYoi-fYSehyV6tIcU3iS1VhVSYk-5GFKKsLk_hjOzS9nsUzaYsvmdsrnEoTd_27gf-RMrAnIPJGz5HuLD7v_I3gKyqrk9</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Oostveen, Luuk J.</creator><creator>Meijer, Frederick J. A.</creator><creator>de Lange, Frank</creator><creator>Smit, Ewoud J.</creator><creator>Pegge, Sjoert A.</creator><creator>Steens, Stefan C. A.</creator><creator>van Amerongen, Martin J.</creator><creator>Prokop, Mathias</creator><creator>Sechopoulos, Ioannis</creator><general>Springer Berlin Heidelberg</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>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>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>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0445-9436</orcidid></search><sort><creationdate>20210801</creationdate><title>Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms</title><author>Oostveen, Luuk J. ; Meijer, Frederick J. A. ; de Lange, Frank ; Smit, Ewoud J. ; Pegge, Sjoert A. ; Steens, Stefan C. A. ; van Amerongen, Martin J. ; Prokop, Mathias ; Sechopoulos, Ioannis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-7f04ec77ed4a8124cccf04e51b2a820f24cb0996b3cbe222d39ce1498b3d5eca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Computed Tomography</topic><topic>Deep learning</topic><topic>Diagnostic Radiology</topic><topic>Differentiation (biology)</topic><topic>Evaluation</topic><topic>Image contrast</topic><topic>Image processing</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Iterative methods</topic><topic>Machine learning</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Noise</topic><topic>Observers</topic><topic>Radiology</topic><topic>Sharpness</topic><topic>Substantia alba</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oostveen, Luuk J.</creatorcontrib><creatorcontrib>Meijer, Frederick J. A.</creatorcontrib><creatorcontrib>de Lange, Frank</creatorcontrib><creatorcontrib>Smit, Ewoud J.</creatorcontrib><creatorcontrib>Pegge, Sjoert A.</creatorcontrib><creatorcontrib>Steens, Stefan C. A.</creatorcontrib><creatorcontrib>van Amerongen, Martin J.</creatorcontrib><creatorcontrib>Prokop, Mathias</creatorcontrib><creatorcontrib>Sechopoulos, Ioannis</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>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oostveen, Luuk J.</au><au>Meijer, Frederick J. A.</au><au>de Lange, Frank</au><au>Smit, Ewoud J.</au><au>Pegge, Sjoert A.</au><au>Steens, Stefan C. A.</au><au>van Amerongen, Martin J.</au><au>Prokop, Mathias</au><au>Sechopoulos, Ioannis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-08-01</date><risdate>2021</risdate><volume>31</volume><issue>8</issue><spage>5498</spage><epage>5506</epage><pages>5498-5506</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).
Methods
Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired
t
tests.
Results
For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.
Conclusions
With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.
Key Points
• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction.
• Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction.
• Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33693996</pmid><doi>10.1007/s00330-020-07668-x</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0445-9436</orcidid><oa>free_for_read</oa></addata></record> |
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| source | SpringerLink Journals |
| subjects | Algorithms Computed Tomography Deep learning Diagnostic Radiology Differentiation (biology) Evaluation Image contrast Image processing Image quality Image reconstruction Imaging Internal Medicine Interventional Radiology Iterative methods Machine learning Medical imaging Medicine Medicine & Public Health Neuroradiology Noise Observers Radiology Sharpness Substantia alba Ultrasound |
| title | Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms |
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