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Comparison of noise-magnitude and noise-texture across two generations of iterative reconstruction algorithms from three manufacturers
To compare the noise-magnitude and noise-texture across two generations of iterative reconstruction (IR) algorithms proposed by three manufacturers according to the dose level. Five computed tomography (CT) systems equipped with two generations of IR algorithms (hybrid/statistical IR [H/SIR] or full...
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| Published in: | Diagnostic and interventional imaging 2019-07, Vol.100 (7-8), p.401-410 |
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| container_title | Diagnostic and interventional imaging |
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| creator | Greffier, J. Larbi, A. Frandon, J. Moliner, G. Beregi, J.P. Pereira, F. |
| description | To compare the noise-magnitude and noise-texture across two generations of iterative reconstruction (IR) algorithms proposed by three manufacturers according to the dose level.
Five computed tomography (CT) systems equipped with two generations of IR algorithms (hybrid/statistical IR [H/SIR] or full/partial model-based IR [MBIR]) were compared. Acquisitions on Catphan 600 phantom were performed at 120kV and three dose levels (3-, 7- and 12-mGy). Raw data were reconstructed using standard “soft tissue” kernel for filtered back projection and one iterative level of two generations of IR algorithms. Contrast to-noise-ratio (CNR) was computed using three regions of interest: two of them placed in the low-density polyethylene (LDPE) and Teflon® inserts and another placed on the solid water. Noise power spectrum (NPS) was computed to assess the noise-magnitude (NPS peak) and noise-texture (NPS spatial frequency).
CNR increased significantly in MBIR compared to H/SIR algorithms for General-Electric (GE) Healthcare (45%±12 [SD]) and Philips Healthcare systems (62%±11 [SD]) (P |
| doi_str_mv | 10.1016/j.diii.2019.04.006 |
| format | article |
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Five computed tomography (CT) systems equipped with two generations of IR algorithms (hybrid/statistical IR [H/SIR] or full/partial model-based IR [MBIR]) were compared. Acquisitions on Catphan 600 phantom were performed at 120kV and three dose levels (3-, 7- and 12-mGy). Raw data were reconstructed using standard “soft tissue” kernel for filtered back projection and one iterative level of two generations of IR algorithms. Contrast to-noise-ratio (CNR) was computed using three regions of interest: two of them placed in the low-density polyethylene (LDPE) and Teflon® inserts and another placed on the solid water. Noise power spectrum (NPS) was computed to assess the noise-magnitude (NPS peak) and noise-texture (NPS spatial frequency).
CNR increased significantly in MBIR compared to H/SIR algorithms for General-Electric (GE) Healthcare (45%±12 [SD]) and Philips Healthcare systems (62%±11 [SD]) (P<0.001). Regarding Siemens Healthineers systems, CNR of MBIR was significantly lower than that of H/SIR (mean difference: −4%±5 [SD]) (P<0.001) for Teflon® insert but not for LDPE insert (mean difference: −4%±7 [SD]) (P=N.S.). NPS peaks were lower with MBIR than with H/SIR for GE Healthcare (-42%±8 [SD]) and Philips Healthcare (−75%±4 [SD]) systems, whereas it was greater with MBIR than with H/SIR for Siemens Healthineers (13%±11 [SD]) systems. NPS spatial frequencies were higher with MBIR than with H/SIR for Siemens (14%±10 [SD]) but lower for others (−17%±5 [SD] for GE Healthineers and −55%±3 [SD] for Philips Healthcare systems).
This study demonstrates that recent MBIR algorithms, by comparison with the preceding generation, differ according to the main manufacturers with respect to noise-magnitude and noise-texture.</description><identifier>ISSN: 2211-5684</identifier><identifier>EISSN: 2211-5684</identifier><identifier>DOI: 10.1016/j.diii.2019.04.006</identifier><identifier>PMID: 31130375</identifier><language>eng</language><publisher>France: Elsevier Masson SAS</publisher><subject>Algorithms ; Humans ; Image Processing, Computer-Assisted ; Image quality enhancement ; Iterative reconstruction ; Life Sciences ; Models, Statistical ; Multidetector computed tomography ; Noise power spectrum ; Optimization ; Phantoms, Imaging ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed</subject><ispartof>Diagnostic and interventional imaging, 2019-07, Vol.100 (7-8), p.401-410</ispartof><rights>2019 Société française de radiologie</rights><rights>Copyright © 2019 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.</rights><rights>Attribution - NonCommercial</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c500t-b244f80840a46aefd3961996809452d97e1d96398a07a485ff44f80864d61e273</citedby><cites>FETCH-LOGICAL-c500t-b244f80840a46aefd3961996809452d97e1d96398a07a485ff44f80864d61e273</cites><orcidid>0000-0002-7425-5298 ; 0000-0002-1735-5129 ; 0000-0001-7816-8178 ; 0000-0003-4768-5708</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31130375$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.umontpellier.fr/hal-02931820$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Greffier, J.</creatorcontrib><creatorcontrib>Larbi, A.</creatorcontrib><creatorcontrib>Frandon, J.</creatorcontrib><creatorcontrib>Moliner, G.</creatorcontrib><creatorcontrib>Beregi, J.P.</creatorcontrib><creatorcontrib>Pereira, F.</creatorcontrib><title>Comparison of noise-magnitude and noise-texture across two generations of iterative reconstruction algorithms from three manufacturers</title><title>Diagnostic and interventional imaging</title><addtitle>Diagn Interv Imaging</addtitle><description>To compare the noise-magnitude and noise-texture across two generations of iterative reconstruction (IR) algorithms proposed by three manufacturers according to the dose level.
Five computed tomography (CT) systems equipped with two generations of IR algorithms (hybrid/statistical IR [H/SIR] or full/partial model-based IR [MBIR]) were compared. Acquisitions on Catphan 600 phantom were performed at 120kV and three dose levels (3-, 7- and 12-mGy). Raw data were reconstructed using standard “soft tissue” kernel for filtered back projection and one iterative level of two generations of IR algorithms. Contrast to-noise-ratio (CNR) was computed using three regions of interest: two of them placed in the low-density polyethylene (LDPE) and Teflon® inserts and another placed on the solid water. Noise power spectrum (NPS) was computed to assess the noise-magnitude (NPS peak) and noise-texture (NPS spatial frequency).
CNR increased significantly in MBIR compared to H/SIR algorithms for General-Electric (GE) Healthcare (45%±12 [SD]) and Philips Healthcare systems (62%±11 [SD]) (P<0.001). Regarding Siemens Healthineers systems, CNR of MBIR was significantly lower than that of H/SIR (mean difference: −4%±5 [SD]) (P<0.001) for Teflon® insert but not for LDPE insert (mean difference: −4%±7 [SD]) (P=N.S.). NPS peaks were lower with MBIR than with H/SIR for GE Healthcare (-42%±8 [SD]) and Philips Healthcare (−75%±4 [SD]) systems, whereas it was greater with MBIR than with H/SIR for Siemens Healthineers (13%±11 [SD]) systems. NPS spatial frequencies were higher with MBIR than with H/SIR for Siemens (14%±10 [SD]) but lower for others (−17%±5 [SD] for GE Healthineers and −55%±3 [SD] for Philips Healthcare systems).
This study demonstrates that recent MBIR algorithms, by comparison with the preceding generation, differ according to the main manufacturers with respect to noise-magnitude and noise-texture.</description><subject>Algorithms</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image quality enhancement</subject><subject>Iterative reconstruction</subject><subject>Life Sciences</subject><subject>Models, Statistical</subject><subject>Multidetector computed tomography</subject><subject>Noise power spectrum</subject><subject>Optimization</subject><subject>Phantoms, Imaging</subject><subject>Signal-To-Noise Ratio</subject><subject>Tomography, X-Ray Computed</subject><issn>2211-5684</issn><issn>2211-5684</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1DAUha0K1FZDX6CLyktYJPgvnlhiU42AIo3EBtaWG9_MeJTYg-0M5QV4bpxmqFjhzbWPv3MW9yB0S0lNCZXvD7V1ztWMUFUTURMiL9A1Y5RWjWzFq3_uV-gmpQMpRxajEJfoilPKCV831-j3JoxHE10KHoce--ASVKPZeZcnC9h4e9YyPOUpFqWLISWcfwa8Aw_RZBd8mr0uP79OgCN0Rctx6uZPbIZdiC7vx4T7GEac9xEAj8ZPvenm0JjeoNe9GRLcnOcKff_08dvmodp-_fxlc7-tuoaQXD0yIfqWtIIYIQ30litJlZItUaJhVq2BWiW5ag1ZG9E2fb_wUlhJga35Cr1bcvdm0MfoRhN_6WCcfrjf6lkjTHHaMnKihX27sMcYfkyQsh5d6mAYjIcwJc0YZ5RJXuYKsQV9Xk6E_iWbEj3XpQ96rkvPdWkidOmimO7O-dPjCPbF8recAnxYACgbOTmIOnUOfAfWlQ1nbYP7X_4foj-nmA</recordid><startdate>20190701</startdate><enddate>20190701</enddate><creator>Greffier, J.</creator><creator>Larbi, A.</creator><creator>Frandon, J.</creator><creator>Moliner, G.</creator><creator>Beregi, J.P.</creator><creator>Pereira, F.</creator><general>Elsevier Masson SAS</general><general>Elsevier</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><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-7425-5298</orcidid><orcidid>https://orcid.org/0000-0002-1735-5129</orcidid><orcidid>https://orcid.org/0000-0001-7816-8178</orcidid><orcidid>https://orcid.org/0000-0003-4768-5708</orcidid></search><sort><creationdate>20190701</creationdate><title>Comparison of noise-magnitude and noise-texture across two generations of iterative reconstruction algorithms from three manufacturers</title><author>Greffier, J. ; Larbi, A. ; Frandon, J. ; Moliner, G. ; Beregi, J.P. ; Pereira, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c500t-b244f80840a46aefd3961996809452d97e1d96398a07a485ff44f80864d61e273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Image quality enhancement</topic><topic>Iterative reconstruction</topic><topic>Life Sciences</topic><topic>Models, Statistical</topic><topic>Multidetector computed tomography</topic><topic>Noise power spectrum</topic><topic>Optimization</topic><topic>Phantoms, Imaging</topic><topic>Signal-To-Noise Ratio</topic><topic>Tomography, X-Ray Computed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Greffier, J.</creatorcontrib><creatorcontrib>Larbi, A.</creatorcontrib><creatorcontrib>Frandon, J.</creatorcontrib><creatorcontrib>Moliner, G.</creatorcontrib><creatorcontrib>Beregi, J.P.</creatorcontrib><creatorcontrib>Pereira, F.</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><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Diagnostic and interventional imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Greffier, J.</au><au>Larbi, A.</au><au>Frandon, J.</au><au>Moliner, G.</au><au>Beregi, J.P.</au><au>Pereira, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of noise-magnitude and noise-texture across two generations of iterative reconstruction algorithms from three manufacturers</atitle><jtitle>Diagnostic and interventional imaging</jtitle><addtitle>Diagn Interv Imaging</addtitle><date>2019-07-01</date><risdate>2019</risdate><volume>100</volume><issue>7-8</issue><spage>401</spage><epage>410</epage><pages>401-410</pages><issn>2211-5684</issn><eissn>2211-5684</eissn><abstract>To compare the noise-magnitude and noise-texture across two generations of iterative reconstruction (IR) algorithms proposed by three manufacturers according to the dose level.
Five computed tomography (CT) systems equipped with two generations of IR algorithms (hybrid/statistical IR [H/SIR] or full/partial model-based IR [MBIR]) were compared. Acquisitions on Catphan 600 phantom were performed at 120kV and three dose levels (3-, 7- and 12-mGy). Raw data were reconstructed using standard “soft tissue” kernel for filtered back projection and one iterative level of two generations of IR algorithms. Contrast to-noise-ratio (CNR) was computed using three regions of interest: two of them placed in the low-density polyethylene (LDPE) and Teflon® inserts and another placed on the solid water. Noise power spectrum (NPS) was computed to assess the noise-magnitude (NPS peak) and noise-texture (NPS spatial frequency).
CNR increased significantly in MBIR compared to H/SIR algorithms for General-Electric (GE) Healthcare (45%±12 [SD]) and Philips Healthcare systems (62%±11 [SD]) (P<0.001). Regarding Siemens Healthineers systems, CNR of MBIR was significantly lower than that of H/SIR (mean difference: −4%±5 [SD]) (P<0.001) for Teflon® insert but not for LDPE insert (mean difference: −4%±7 [SD]) (P=N.S.). NPS peaks were lower with MBIR than with H/SIR for GE Healthcare (-42%±8 [SD]) and Philips Healthcare (−75%±4 [SD]) systems, whereas it was greater with MBIR than with H/SIR for Siemens Healthineers (13%±11 [SD]) systems. NPS spatial frequencies were higher with MBIR than with H/SIR for Siemens (14%±10 [SD]) but lower for others (−17%±5 [SD] for GE Healthineers and −55%±3 [SD] for Philips Healthcare systems).
This study demonstrates that recent MBIR algorithms, by comparison with the preceding generation, differ according to the main manufacturers with respect to noise-magnitude and noise-texture.</abstract><cop>France</cop><pub>Elsevier Masson SAS</pub><pmid>31130375</pmid><doi>10.1016/j.diii.2019.04.006</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-7425-5298</orcidid><orcidid>https://orcid.org/0000-0002-1735-5129</orcidid><orcidid>https://orcid.org/0000-0001-7816-8178</orcidid><orcidid>https://orcid.org/0000-0003-4768-5708</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Diagnostic and interventional imaging, 2019-07, Vol.100 (7-8), p.401-410 |
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| source | ScienceDirect Freedom Collection |
| subjects | Algorithms Humans Image Processing, Computer-Assisted Image quality enhancement Iterative reconstruction Life Sciences Models, Statistical Multidetector computed tomography Noise power spectrum Optimization Phantoms, Imaging Signal-To-Noise Ratio Tomography, X-Ray Computed |
| title | Comparison of noise-magnitude and noise-texture across two generations of iterative reconstruction algorithms from three manufacturers |
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