Mi Sun Chung1, Dong Hyun Yang2, Young-Hak Kim3, Soo-Jin Kang3, Joonho Jung3, Namkug Kim4, Seung-Ho Heo5, Seunghee Baek6, Joon Beom Seo7, Byoung Wook Choi8, Joon-Won Kang7, Tae-Hwan Lim7. 1. Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjakgu, Seoul, 06973, South Korea. 2. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Asanbyeongwon-gil 86, Seoul, 138-736, South Korea. donghyun.yang@gmail.com. 3. Heart Institute, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 4. Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 5. Asan institute for Life Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 6. Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 7. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Asanbyeongwon-gil 86, Seoul, 138-736, South Korea. 8. Department of Diagnostic Radiology, College of Medicine, Yonsei University, Seoul, South Korea.
Abstract
OBJECTIVES: To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). METHODS: Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. RESULTS: CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011). CONCLUSIONS: CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA. KEY POINTS: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis.
OBJECTIVES: To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA). METHODS: Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns. RESULTS: CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011). CONCLUSIONS: CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA. KEY POINTS: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis.
Authors: Manuel D Cerqueira; Neil J Weissman; Vasken Dilsizian; Alice K Jacobs; Sanjiv Kaul; Warren K Laskey; Dudley J Pennell; John A Rumberger; Thomas Ryan; Mario S Verani Journal: Circulation Date: 2002-01-29 Impact factor: 29.690
Authors: Gilles Montalescot; Udo Sechtem; Stephan Achenbach; Felicita Andreotti; Chris Arden; Andrzej Budaj; Raffaele Bugiardini; Filippo Crea; Thomas Cuisset; Carlo Di Mario; J Rafael Ferreira; Bernard J Gersh; Anselm K Gitt; Jean-Sebastien Hulot; Nikolaus Marx; Lionel H Opie; Matthias Pfisterer; Eva Prescott; Frank Ruschitzka; Manel Sabaté; Roxy Senior; David Paul Taggart; Ernst E van der Wall; Christiaan J M Vrints; Jose Luis Zamorano; Stephan Achenbach; Helmut Baumgartner; Jeroen J Bax; Héctor Bueno; Veronica Dean; Christi Deaton; Cetin Erol; Robert Fagard; Roberto Ferrari; David Hasdai; Arno W Hoes; Paulus Kirchhof; Juhani Knuuti; Philippe Kolh; Patrizio Lancellotti; Ales Linhart; Petros Nihoyannopoulos; Massimo F Piepoli; Piotr Ponikowski; Per Anton Sirnes; Juan Luis Tamargo; Michal Tendera; Adam Torbicki; William Wijns; Stephan Windecker; Juhani Knuuti; Marco Valgimigli; Héctor Bueno; Marc J Claeys; Norbert Donner-Banzhoff; Cetin Erol; Herbert Frank; Christian Funck-Brentano; Oliver Gaemperli; José R Gonzalez-Juanatey; Michalis Hamilos; David Hasdai; Steen Husted; Stefan K James; Kari Kervinen; Philippe Kolh; Steen Dalby Kristensen; Patrizio Lancellotti; Aldo Pietro Maggioni; Massimo F Piepoli; Axel R Pries; Francesco Romeo; Lars Rydén; Maarten L Simoons; Per Anton Sirnes; Ph Gabriel Steg; Adam Timmis; William Wijns; Stephan Windecker; Aylin Yildirir; Jose Luis Zamorano Journal: Eur Heart J Date: 2013-08-30 Impact factor: 29.983
Authors: Brian S Ko; James D Cameron; Ian T Meredith; Michael Leung; Paul R Antonis; Arthur Nasis; Marcus Crossett; Sarah A Hope; Sam J Lehman; John Troupis; Tony DeFrance; Sujith K Seneviratne Journal: Eur Heart J Date: 2011-08-02 Impact factor: 29.983
Authors: James K Min; Jonathon Leipsic; Michael J Pencina; Daniel S Berman; Bon-Kwon Koo; Carlos van Mieghem; Andrejs Erglis; Fay Y Lin; Allison M Dunning; Patricia Apruzzese; Matthew J Budoff; Jason H Cole; Farouc A Jaffer; Martin B Leon; Jennifer Malpeso; G B John Mancini; Seung-Jung Park; Robert S Schwartz; Leslee J Shaw; Laura Mauri Journal: JAMA Date: 2012-09-26 Impact factor: 56.272
Authors: Ron Blankstein; Leon D Shturman; Ian S Rogers; Jose A Rocha-Filho; David R Okada; Ammar Sarwar; Anand V Soni; Hiram Bezerra; Brian B Ghoshhajra; Milena Petranovic; Ricardo Loureiro; Gudrun Feuchtner; Henry Gewirtz; Udo Hoffmann; Wilfred S Mamuya; Thomas J Brady; Ricardo C Cury Journal: J Am Coll Cardiol Date: 2009-09-15 Impact factor: 24.094
Authors: F Y van Driest; R J van der Geest; A Broersen; J Dijkstra; M El Mahdiui; J W Jukema; A J H A Scholte Journal: Int J Cardiovasc Imaging Date: 2021-06-23 Impact factor: 2.357