Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology
Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows highe...
Gespeichert in:
| Veröffentlicht in: | International Journal of Cardiovascular Imaging 2013-06, Vol.29 (5), p.1177-1190 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1190 |
|---|---|
| container_issue | 5 |
| container_start_page | 1177 |
| container_title | International Journal of Cardiovascular Imaging |
| container_volume | 29 |
| creator | de Graaf, Michiel A. Broersen, Alexander Kitslaar, Pieter H. Roos, Cornelis J. Dijkstra, Jouke Lelieveldt, Boudewijn P. F. Jukema, J. Wouter Schalij, Martin J. Delgado, Victoria Bax, Jeroen J. Reiber, Johan H. C. Scholte, Arthur J. |
| description | Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland–Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928,
p
|
| doi_str_mv | 10.1007/s10554-013-0194-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1412519415</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1412519415</sourcerecordid><originalsourceid>FETCH-LOGICAL-c405t-25195c8cbd167b311f6ecfbf8a61d675a60c7b536a85769bec784f90715eb95e3</originalsourceid><addsrcrecordid>eNqFUslu1TAUtRCIlsIHsEGW2LAJ2ImHmF1VMUmV2MDachznxZVjv3po-_g0vg6neaAKCbHwcO8951wPB4CXGL3FCPF3CSNKSYNwV4cgzd0jcIoprxEn3eN1z0RDuSAn4FlKVwihFrXdU3DSdgRzQvgp-HleclhUthpeF-WznayuUfBQ-RHqWUWls4n2x5YME9QhBq_iAao8mxiSdutsE7y1ea7VZV-yGWFVDbuo9vPhAcPv7DH5HurKSk2tReM28XsB63NUNyrp4lSExdUohVLPcmNjLsrB2aYcXNgdnoMnk3LJvDiuZ-D7xw_fLj43l18_fbk4v2w0QTQ3LcWC6l4PI2Z86DCemNHTMPWK4ZFxqhjSfKAdUz3lTAxG855MAnFMzSCo6c7Am013H8N1MSnLxSZtnFPehJIkJnjtQTD9P7QTPWkJZ6RCX_8FvQol-nqRiuoRQW0vVhTeUPevFc0k99Eu9S0lRnL1gNw8IKsH5OoBeVc5r47KZVjM-Ifx-9MroN0AqZb8zsQHrf-p-gt9h8O5</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1380402894</pqid></control><display><type>article</type><title>Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>de Graaf, Michiel A. ; Broersen, Alexander ; Kitslaar, Pieter H. ; Roos, Cornelis J. ; Dijkstra, Jouke ; Lelieveldt, Boudewijn P. F. ; Jukema, J. Wouter ; Schalij, Martin J. ; Delgado, Victoria ; Bax, Jeroen J. ; Reiber, Johan H. C. ; Scholte, Arthur J.</creator><creatorcontrib>de Graaf, Michiel A. ; Broersen, Alexander ; Kitslaar, Pieter H. ; Roos, Cornelis J. ; Dijkstra, Jouke ; Lelieveldt, Boudewijn P. F. ; Jukema, J. Wouter ; Schalij, Martin J. ; Delgado, Victoria ; Bax, Jeroen J. ; Reiber, Johan H. C. ; Scholte, Arthur J.</creatorcontrib><description>Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland–Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928,
p
< 0.001) (Fig.
1
). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714,
p
< 0.001 and r = 0.695,
p
< 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm
3
(−42; 90) and 7.7 mm
3
(−54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523,
p
< 0.001) and (r = 0.736,
p
< 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool.
Fig. 1
Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (
Panel I
). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (
Panel II
). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (
Panel III
). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in
panel IV
. At the level of the minimal lumen area (MLA) (
yellow lines
), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (
blue markers
). Fibrotic tissue was labeled in
dark green
, Fibro-fatty tissue in
light green
, dense calcium in
white
and necrotic core was labeled in
red</description><identifier>ISSN: 1569-5794</identifier><identifier>EISSN: 1573-0743</identifier><identifier>EISSN: 1875-8312</identifier><identifier>DOI: 10.1007/s10554-013-0194-x</identifier><identifier>PMID: 23417447</identifier><identifier>CODEN: IJCIBI</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Aged ; Algorithms ; Automation, Laboratory ; Cardiac Imaging ; Cardiology ; Coronary Angiography - methods ; Coronary Artery Disease - diagnostic imaging ; Coronary Stenosis - diagnostic imaging ; Coronary Vessels - diagnostic imaging ; Feasibility Studies ; Female ; Fibrosis ; Humans ; Imaging ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Multidetector Computed Tomography ; Necrosis ; Observer Variation ; Original Paper ; Plaque, Atherosclerotic ; Predictive Value of Tests ; Radiographic Image Interpretation, Computer-Assisted ; Radiology ; Reproducibility of Results ; Severity of Illness Index ; Software ; Ultrasonography, Interventional ; Vascular Calcification - diagnostic imaging</subject><ispartof>International Journal of Cardiovascular Imaging, 2013-06, Vol.29 (5), p.1177-1190</ispartof><rights>Springer Science+Business Media Dordrecht 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c405t-25195c8cbd167b311f6ecfbf8a61d675a60c7b536a85769bec784f90715eb95e3</citedby><cites>FETCH-LOGICAL-c405t-25195c8cbd167b311f6ecfbf8a61d675a60c7b536a85769bec784f90715eb95e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10554-013-0194-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10554-013-0194-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23417447$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>de Graaf, Michiel A.</creatorcontrib><creatorcontrib>Broersen, Alexander</creatorcontrib><creatorcontrib>Kitslaar, Pieter H.</creatorcontrib><creatorcontrib>Roos, Cornelis J.</creatorcontrib><creatorcontrib>Dijkstra, Jouke</creatorcontrib><creatorcontrib>Lelieveldt, Boudewijn P. F.</creatorcontrib><creatorcontrib>Jukema, J. Wouter</creatorcontrib><creatorcontrib>Schalij, Martin J.</creatorcontrib><creatorcontrib>Delgado, Victoria</creatorcontrib><creatorcontrib>Bax, Jeroen J.</creatorcontrib><creatorcontrib>Reiber, Johan H. C.</creatorcontrib><creatorcontrib>Scholte, Arthur J.</creatorcontrib><title>Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology</title><title>International Journal of Cardiovascular Imaging</title><addtitle>Int J Cardiovasc Imaging</addtitle><addtitle>Int J Cardiovasc Imaging</addtitle><description>Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland–Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928,
p
< 0.001) (Fig.
1
). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714,
p
< 0.001 and r = 0.695,
p
< 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm
3
(−42; 90) and 7.7 mm
3
(−54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523,
p
< 0.001) and (r = 0.736,
p
< 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool.
Fig. 1
Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (
Panel I
). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (
Panel II
). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (
Panel III
). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in
panel IV
. At the level of the minimal lumen area (MLA) (
yellow lines
), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (
blue markers
). Fibrotic tissue was labeled in
dark green
, Fibro-fatty tissue in
light green
, dense calcium in
white
and necrotic core was labeled in
red</description><subject>Aged</subject><subject>Algorithms</subject><subject>Automation, Laboratory</subject><subject>Cardiac Imaging</subject><subject>Cardiology</subject><subject>Coronary Angiography - methods</subject><subject>Coronary Artery Disease - diagnostic imaging</subject><subject>Coronary Stenosis - diagnostic imaging</subject><subject>Coronary Vessels - diagnostic imaging</subject><subject>Feasibility Studies</subject><subject>Female</subject><subject>Fibrosis</subject><subject>Humans</subject><subject>Imaging</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multidetector Computed Tomography</subject><subject>Necrosis</subject><subject>Observer Variation</subject><subject>Original Paper</subject><subject>Plaque, Atherosclerotic</subject><subject>Predictive Value of Tests</subject><subject>Radiographic Image Interpretation, Computer-Assisted</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Severity of Illness Index</subject><subject>Software</subject><subject>Ultrasonography, Interventional</subject><subject>Vascular Calcification - diagnostic imaging</subject><issn>1569-5794</issn><issn>1573-0743</issn><issn>1875-8312</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFUslu1TAUtRCIlsIHsEGW2LAJ2ImHmF1VMUmV2MDachznxZVjv3po-_g0vg6neaAKCbHwcO8951wPB4CXGL3FCPF3CSNKSYNwV4cgzd0jcIoprxEn3eN1z0RDuSAn4FlKVwihFrXdU3DSdgRzQvgp-HleclhUthpeF-WznayuUfBQ-RHqWUWls4n2x5YME9QhBq_iAao8mxiSdutsE7y1ea7VZV-yGWFVDbuo9vPhAcPv7DH5HurKSk2tReM28XsB63NUNyrp4lSExdUohVLPcmNjLsrB2aYcXNgdnoMnk3LJvDiuZ-D7xw_fLj43l18_fbk4v2w0QTQ3LcWC6l4PI2Z86DCemNHTMPWK4ZFxqhjSfKAdUz3lTAxG855MAnFMzSCo6c7Am013H8N1MSnLxSZtnFPehJIkJnjtQTD9P7QTPWkJZ6RCX_8FvQol-nqRiuoRQW0vVhTeUPevFc0k99Eu9S0lRnL1gNw8IKsH5OoBeVc5r47KZVjM-Ifx-9MroN0AqZb8zsQHrf-p-gt9h8O5</recordid><startdate>20130601</startdate><enddate>20130601</enddate><creator>de Graaf, Michiel A.</creator><creator>Broersen, Alexander</creator><creator>Kitslaar, Pieter H.</creator><creator>Roos, Cornelis J.</creator><creator>Dijkstra, Jouke</creator><creator>Lelieveldt, Boudewijn P. F.</creator><creator>Jukema, J. Wouter</creator><creator>Schalij, Martin J.</creator><creator>Delgado, Victoria</creator><creator>Bax, Jeroen J.</creator><creator>Reiber, Johan H. C.</creator><creator>Scholte, Arthur J.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M7Z</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7QO</scope></search><sort><creationdate>20130601</creationdate><title>Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology</title><author>de Graaf, Michiel A. ; Broersen, Alexander ; Kitslaar, Pieter H. ; Roos, Cornelis J. ; Dijkstra, Jouke ; Lelieveldt, Boudewijn P. F. ; Jukema, J. Wouter ; Schalij, Martin J. ; Delgado, Victoria ; Bax, Jeroen J. ; Reiber, Johan H. C. ; Scholte, Arthur J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-25195c8cbd167b311f6ecfbf8a61d675a60c7b536a85769bec784f90715eb95e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Aged</topic><topic>Algorithms</topic><topic>Automation, Laboratory</topic><topic>Cardiac Imaging</topic><topic>Cardiology</topic><topic>Coronary Angiography - methods</topic><topic>Coronary Artery Disease - diagnostic imaging</topic><topic>Coronary Stenosis - diagnostic imaging</topic><topic>Coronary Vessels - diagnostic imaging</topic><topic>Feasibility Studies</topic><topic>Female</topic><topic>Fibrosis</topic><topic>Humans</topic><topic>Imaging</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multidetector Computed Tomography</topic><topic>Necrosis</topic><topic>Observer Variation</topic><topic>Original Paper</topic><topic>Plaque, Atherosclerotic</topic><topic>Predictive Value of Tests</topic><topic>Radiographic Image Interpretation, Computer-Assisted</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Severity of Illness Index</topic><topic>Software</topic><topic>Ultrasonography, Interventional</topic><topic>Vascular Calcification - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Graaf, Michiel A.</creatorcontrib><creatorcontrib>Broersen, Alexander</creatorcontrib><creatorcontrib>Kitslaar, Pieter H.</creatorcontrib><creatorcontrib>Roos, Cornelis J.</creatorcontrib><creatorcontrib>Dijkstra, Jouke</creatorcontrib><creatorcontrib>Lelieveldt, Boudewijn P. F.</creatorcontrib><creatorcontrib>Jukema, J. Wouter</creatorcontrib><creatorcontrib>Schalij, Martin J.</creatorcontrib><creatorcontrib>Delgado, Victoria</creatorcontrib><creatorcontrib>Bax, Jeroen J.</creatorcontrib><creatorcontrib>Reiber, Johan H. C.</creatorcontrib><creatorcontrib>Scholte, Arthur J.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</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>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>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biochemistry Abstracts 1</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>Biotechnology Research Abstracts</collection><jtitle>International Journal of Cardiovascular Imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Graaf, Michiel A.</au><au>Broersen, Alexander</au><au>Kitslaar, Pieter H.</au><au>Roos, Cornelis J.</au><au>Dijkstra, Jouke</au><au>Lelieveldt, Boudewijn P. F.</au><au>Jukema, J. Wouter</au><au>Schalij, Martin J.</au><au>Delgado, Victoria</au><au>Bax, Jeroen J.</au><au>Reiber, Johan H. C.</au><au>Scholte, Arthur J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology</atitle><jtitle>International Journal of Cardiovascular Imaging</jtitle><stitle>Int J Cardiovasc Imaging</stitle><addtitle>Int J Cardiovasc Imaging</addtitle><date>2013-06-01</date><risdate>2013</risdate><volume>29</volume><issue>5</issue><spage>1177</spage><epage>1190</epage><pages>1177-1190</pages><issn>1569-5794</issn><eissn>1573-0743</eissn><eissn>1875-8312</eissn><coden>IJCIBI</coden><abstract>Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland–Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928,
p
< 0.001) (Fig.
1
). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714,
p
< 0.001 and r = 0.695,
p
< 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm
3
(−42; 90) and 7.7 mm
3
(−54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523,
p
< 0.001) and (r = 0.736,
p
< 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool.
Fig. 1
Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (
Panel I
). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (
Panel II
). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (
Panel III
). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in
panel IV
. At the level of the minimal lumen area (MLA) (
yellow lines
), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (
blue markers
). Fibrotic tissue was labeled in
dark green
, Fibro-fatty tissue in
light green
, dense calcium in
white
and necrotic core was labeled in
red</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>23417447</pmid><doi>10.1007/s10554-013-0194-x</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1569-5794 |
| ispartof | International Journal of Cardiovascular Imaging, 2013-06, Vol.29 (5), p.1177-1190 |
| issn | 1569-5794 1573-0743 1875-8312 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1412519415 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Aged Algorithms Automation, Laboratory Cardiac Imaging Cardiology Coronary Angiography - methods Coronary Artery Disease - diagnostic imaging Coronary Stenosis - diagnostic imaging Coronary Vessels - diagnostic imaging Feasibility Studies Female Fibrosis Humans Imaging Male Medicine Medicine & Public Health Middle Aged Multidetector Computed Tomography Necrosis Observer Variation Original Paper Plaque, Atherosclerotic Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Radiology Reproducibility of Results Severity of Illness Index Software Ultrasonography, Interventional Vascular Calcification - diagnostic imaging |
| title | Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T20%3A44%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automatic%20quantification%20and%20characterization%20of%20coronary%20atherosclerosis%20with%20computed%20tomography%20coronary%20angiography:%20cross-correlation%20with%20intravascular%20ultrasound%20virtual%20histology&rft.jtitle=International%20Journal%20of%20Cardiovascular%20Imaging&rft.au=de%20Graaf,%20Michiel%20A.&rft.date=2013-06-01&rft.volume=29&rft.issue=5&rft.spage=1177&rft.epage=1190&rft.pages=1177-1190&rft.issn=1569-5794&rft.eissn=1573-0743&rft.coden=IJCIBI&rft_id=info:doi/10.1007/s10554-013-0194-x&rft_dat=%3Cproquest_cross%3E1412519415%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1380402894&rft_id=info:pmid/23417447&rfr_iscdi=true |