Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques
Volumetric and radiomic analysis of atherosclerotic plaques on coronary CT angiography have been shown to predict high-risk plaque morphology and to predict patient outcomes. However, there is limited information whether image reconstruction algorithms and preprocessing steps (type of binning, numbe...
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Veröffentlicht in: | Journal of cardiovascular computed tomography 2019-11, Vol.13 (6), p.325-330 |
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container_title | Journal of cardiovascular computed tomography |
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description | Volumetric and radiomic analysis of atherosclerotic plaques on coronary CT angiography have been shown to predict high-risk plaque morphology and to predict patient outcomes. However, there is limited information whether image reconstruction algorithms and preprocessing steps (type of binning, number of bins used for discretization) may influence parameter values.
We retrospectively identified 60 coronary lesions on coronary CT angiography (CTA). All images were reconstructed using filtered back projection (FBP), hybrid (HIR) and model-based (MIR) iterative reconstruction. Plaques were segmented manually on HIR images and copied to FBP and MIR images to ensure identical voxels were analyzed. Overall, 4 volumetric and 169 radiomic parameters were calculated. Intra-class correlation coefficient (ICC) was used to assess reproducibility between image reconstructions, while linear regression analysis was used to assess the effect of preprocessing steps done before calculating radiomic metrics.
All volumetric and radiomic metrics had ICC>0.90 except for first-order statistics: mode, harmonic mean, minimum (0.45, 0.76, 0.84; respectively) and gray level co-occurrence (GLCM) parameters: inverse difference sum and sum variance (0.01, 0.04; respectively). Among GLCM parameters 90% were significantly affected by the type of binning and 100% by the number of bins. In case of gray level run length matrix parameters 100% of metrics were affected by both preprocessing steps.
Volumetric and radiomic statistics are robust to image reconstruction algorithms. However, all radiomic variables were affected by preprocessing steps therefore, showing the need for standardization before being implemented into everyday clinical practice. |
doi_str_mv | 10.1016/j.jcct.2018.11.004 |
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We retrospectively identified 60 coronary lesions on coronary CT angiography (CTA). All images were reconstructed using filtered back projection (FBP), hybrid (HIR) and model-based (MIR) iterative reconstruction. Plaques were segmented manually on HIR images and copied to FBP and MIR images to ensure identical voxels were analyzed. Overall, 4 volumetric and 169 radiomic parameters were calculated. Intra-class correlation coefficient (ICC) was used to assess reproducibility between image reconstructions, while linear regression analysis was used to assess the effect of preprocessing steps done before calculating radiomic metrics.
All volumetric and radiomic metrics had ICC>0.90 except for first-order statistics: mode, harmonic mean, minimum (0.45, 0.76, 0.84; respectively) and gray level co-occurrence (GLCM) parameters: inverse difference sum and sum variance (0.01, 0.04; respectively). Among GLCM parameters 90% were significantly affected by the type of binning and 100% by the number of bins. In case of gray level run length matrix parameters 100% of metrics were affected by both preprocessing steps.
Volumetric and radiomic statistics are robust to image reconstruction algorithms. However, all radiomic variables were affected by preprocessing steps therefore, showing the need for standardization before being implemented into everyday clinical practice.</description><identifier>ISSN: 1934-5925</identifier><identifier>EISSN: 1876-861X</identifier><identifier>DOI: 10.1016/j.jcct.2018.11.004</identifier><identifier>PMID: 30447949</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Aged ; Algorithms ; Computed Tomography Angiography - methods ; Computer-assisted image analysis ; Coronary Angiography - methods ; Coronary Artery Disease - diagnostic imaging ; Coronary atherosclerosis ; Coronary Vessels - diagnostic imaging ; Female ; Humans ; Image reconstruction ; Male ; Middle Aged ; Multidetector Computed Tomography - methods ; Multislice computed tomography ; Plaque, Atherosclerotic ; Predictive Value of Tests ; Radiographic Image Interpretation, Computer-Assisted - methods ; Reproducibility of Results ; Retrospective Studies</subject><ispartof>Journal of cardiovascular computed tomography, 2019-11, Vol.13 (6), p.325-330</ispartof><rights>2019 Society of Cardiovascular Computed Tomography</rights><rights>Copyright © 2019 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-cd2e2d4fa13b189ab9e022f9ffd666f4d1f1976b496fd34cdb579f940a15b54c3</citedby><cites>FETCH-LOGICAL-c356t-cd2e2d4fa13b189ab9e022f9ffd666f4d1f1976b496fd34cdb579f940a15b54c3</cites><orcidid>0000-0003-0885-736X ; 0000-0002-6640-6260</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jcct.2018.11.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30447949$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kolossváry, Márton</creatorcontrib><creatorcontrib>Szilveszter, Bálint</creatorcontrib><creatorcontrib>Karády, Júlia</creatorcontrib><creatorcontrib>Drobni, Zsófia Dóra</creatorcontrib><creatorcontrib>Merkely, Béla</creatorcontrib><creatorcontrib>Maurovich-Horvat, Pál</creatorcontrib><title>Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques</title><title>Journal of cardiovascular computed tomography</title><addtitle>J Cardiovasc Comput Tomogr</addtitle><description>Volumetric and radiomic analysis of atherosclerotic plaques on coronary CT angiography have been shown to predict high-risk plaque morphology and to predict patient outcomes. However, there is limited information whether image reconstruction algorithms and preprocessing steps (type of binning, number of bins used for discretization) may influence parameter values.
We retrospectively identified 60 coronary lesions on coronary CT angiography (CTA). All images were reconstructed using filtered back projection (FBP), hybrid (HIR) and model-based (MIR) iterative reconstruction. Plaques were segmented manually on HIR images and copied to FBP and MIR images to ensure identical voxels were analyzed. Overall, 4 volumetric and 169 radiomic parameters were calculated. Intra-class correlation coefficient (ICC) was used to assess reproducibility between image reconstructions, while linear regression analysis was used to assess the effect of preprocessing steps done before calculating radiomic metrics.
All volumetric and radiomic metrics had ICC>0.90 except for first-order statistics: mode, harmonic mean, minimum (0.45, 0.76, 0.84; respectively) and gray level co-occurrence (GLCM) parameters: inverse difference sum and sum variance (0.01, 0.04; respectively). Among GLCM parameters 90% were significantly affected by the type of binning and 100% by the number of bins. In case of gray level run length matrix parameters 100% of metrics were affected by both preprocessing steps.
Volumetric and radiomic statistics are robust to image reconstruction algorithms. However, all radiomic variables were affected by preprocessing steps therefore, showing the need for standardization before being implemented into everyday clinical practice.</description><subject>Aged</subject><subject>Algorithms</subject><subject>Computed Tomography Angiography - methods</subject><subject>Computer-assisted image analysis</subject><subject>Coronary Angiography - methods</subject><subject>Coronary Artery Disease - diagnostic imaging</subject><subject>Coronary atherosclerosis</subject><subject>Coronary Vessels - diagnostic imaging</subject><subject>Female</subject><subject>Humans</subject><subject>Image reconstruction</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multidetector Computed Tomography - methods</subject><subject>Multislice computed tomography</subject><subject>Plaque, Atherosclerotic</subject><subject>Predictive Value of Tests</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><issn>1934-5925</issn><issn>1876-861X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE9r3DAQxUVJ6G7SfoEego-92NHYsmxBLyUkaSCQSwq9CVkapVpsayPJC_n2kdltjznNH968x_wI-Qa0Agr8elfttE5VTaGvACpK2Seyhb7jZc_hz1nuRcPKVtTthlzEuKO07YD2n8mmoYx1gokt0bfWok6Ft4Wb1AsWAbWfYwqLTs7PhRpffHDp7xSLPB38uEyYgtOFmk0RlHF-ysNeBZX3GOJqpH3wswpvxX5UrwvGL-TcqjHi11O9JL_vbp9vfpWPT_cPNz8fS920PJXa1FgbZhU0A_RCDQJpXVthreGcW2bAguj4wAS3pmHaDG0nrGBUQTu0TDeX5PvRdx_8mpvk5KLGcVQz-iXKGnJOw3nTZ2l9lOrgYwxo5T7k_8ObBCpXuHInV7hyhSsBZIabj65O_sswofl_8o9mFvw4CjB_eXAYZNQOZ43GZaxJGu8-8n8HgRuNhg</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Kolossváry, Márton</creator><creator>Szilveszter, Bálint</creator><creator>Karády, Júlia</creator><creator>Drobni, Zsófia Dóra</creator><creator>Merkely, Béla</creator><creator>Maurovich-Horvat, Pál</creator><general>Elsevier Inc</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/0000-0003-0885-736X</orcidid><orcidid>https://orcid.org/0000-0002-6640-6260</orcidid></search><sort><creationdate>201911</creationdate><title>Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques</title><author>Kolossváry, Márton ; Szilveszter, Bálint ; Karády, Júlia ; Drobni, Zsófia Dóra ; Merkely, Béla ; Maurovich-Horvat, Pál</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-cd2e2d4fa13b189ab9e022f9ffd666f4d1f1976b496fd34cdb579f940a15b54c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Aged</topic><topic>Algorithms</topic><topic>Computed Tomography Angiography - methods</topic><topic>Computer-assisted image analysis</topic><topic>Coronary Angiography - methods</topic><topic>Coronary Artery Disease - diagnostic imaging</topic><topic>Coronary atherosclerosis</topic><topic>Coronary Vessels - diagnostic imaging</topic><topic>Female</topic><topic>Humans</topic><topic>Image reconstruction</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Multidetector Computed Tomography - methods</topic><topic>Multislice computed tomography</topic><topic>Plaque, Atherosclerotic</topic><topic>Predictive Value of Tests</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kolossváry, Márton</creatorcontrib><creatorcontrib>Szilveszter, Bálint</creatorcontrib><creatorcontrib>Karády, Júlia</creatorcontrib><creatorcontrib>Drobni, Zsófia Dóra</creatorcontrib><creatorcontrib>Merkely, Béla</creatorcontrib><creatorcontrib>Maurovich-Horvat, Pál</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>Journal of cardiovascular computed tomography</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kolossváry, Márton</au><au>Szilveszter, Bálint</au><au>Karády, Júlia</au><au>Drobni, Zsófia Dóra</au><au>Merkely, Béla</au><au>Maurovich-Horvat, Pál</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques</atitle><jtitle>Journal of cardiovascular computed tomography</jtitle><addtitle>J Cardiovasc Comput Tomogr</addtitle><date>2019-11</date><risdate>2019</risdate><volume>13</volume><issue>6</issue><spage>325</spage><epage>330</epage><pages>325-330</pages><issn>1934-5925</issn><eissn>1876-861X</eissn><abstract>Volumetric and radiomic analysis of atherosclerotic plaques on coronary CT angiography have been shown to predict high-risk plaque morphology and to predict patient outcomes. However, there is limited information whether image reconstruction algorithms and preprocessing steps (type of binning, number of bins used for discretization) may influence parameter values.
We retrospectively identified 60 coronary lesions on coronary CT angiography (CTA). All images were reconstructed using filtered back projection (FBP), hybrid (HIR) and model-based (MIR) iterative reconstruction. Plaques were segmented manually on HIR images and copied to FBP and MIR images to ensure identical voxels were analyzed. Overall, 4 volumetric and 169 radiomic parameters were calculated. Intra-class correlation coefficient (ICC) was used to assess reproducibility between image reconstructions, while linear regression analysis was used to assess the effect of preprocessing steps done before calculating radiomic metrics.
All volumetric and radiomic metrics had ICC>0.90 except for first-order statistics: mode, harmonic mean, minimum (0.45, 0.76, 0.84; respectively) and gray level co-occurrence (GLCM) parameters: inverse difference sum and sum variance (0.01, 0.04; respectively). Among GLCM parameters 90% were significantly affected by the type of binning and 100% by the number of bins. In case of gray level run length matrix parameters 100% of metrics were affected by both preprocessing steps.
Volumetric and radiomic statistics are robust to image reconstruction algorithms. However, all radiomic variables were affected by preprocessing steps therefore, showing the need for standardization before being implemented into everyday clinical practice.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30447949</pmid><doi>10.1016/j.jcct.2018.11.004</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0003-0885-736X</orcidid><orcidid>https://orcid.org/0000-0002-6640-6260</orcidid></addata></record> |
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subjects | Aged Algorithms Computed Tomography Angiography - methods Computer-assisted image analysis Coronary Angiography - methods Coronary Artery Disease - diagnostic imaging Coronary atherosclerosis Coronary Vessels - diagnostic imaging Female Humans Image reconstruction Male Middle Aged Multidetector Computed Tomography - methods Multislice computed tomography Plaque, Atherosclerotic Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted - methods Reproducibility of Results Retrospective Studies |
title | Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques |
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