OBJECTIVES: The purpose of this study was to estimate the myocardial area at risk (MAAR) using coronary computed tomography angiography (CTA) and Voronoi algorithm-based myocardial segmentation in comparison with single-photon emission computed tomography (SPECT). METHODS: Thirty-four patients with coronary artery disease underwent 128-slice coronary CTA, stress/rest thallium-201 SPECT, and coronary angiography (CAG). CTA-based MAAR was defined as the sum of all CAG stenosis (>50%) related territories (the ratio of the left ventricular volume). Using automated quantification software (17-segment model, 5-point scale), SPECT-based MAAR was defined as the number of segments with a score above zero as compared to the total 17 segments by summed stress score (SSS), difference (SDS) score map, and comprehensive SPECT interpretation with either SSS or SDS best correlating CAG findings (SSS/SDS). Results were compared using Pearson's correlation coefficient. RESULTS: Forty-nine stenoses were observed in 102 major coronary territories. Mean value of CTA-based MAAR was 28.3 ± 14.0%. SSS-based, SDS-based, and SSS/SDS-based MAAR was 30.1 ± 6.1%, 20.1 ± 15.8%, and 26.8 ± 15.7%, respectively. CTA-based MAAR was significantly related to SPECT-based MAAR (r = 0.531 for SSS; r = 0.494 for SDS; r = 0.814 for SSS/SDS; P < 0.05 in each). CONCLUSIONS: CTA-based Voronoi algorithm myocardial segmentation reliably quantifies SPECT-based MAAR. KEY POINTS: • Voronoi algorithm allows for three-dimensional myocardial segmentation of coronary CT angiography • Stenosis-related CT myocardial territories correlate to SPECT based area at risk • CT angiography myocardial segmentation may assist in clinical decision-making.
OBJECTIVES: The purpose of this study was to estimate the myocardial area at risk (MAAR) using coronary computed tomography angiography (CTA) and Voronoi algorithm-based myocardial segmentation in comparison with single-photon emission computed tomography (SPECT). METHODS: Thirty-four patients with coronary artery disease underwent 128-slice coronary CTA, stress/rest thallium-201 SPECT, and coronary angiography (CAG). CTA-based MAAR was defined as the sum of all CAG stenosis (>50%) related territories (the ratio of the left ventricular volume). Using automated quantification software (17-segment model, 5-point scale), SPECT-based MAAR was defined as the number of segments with a score above zero as compared to the total 17 segments by summed stress score (SSS), difference (SDS) score map, and comprehensive SPECT interpretation with either SSS or SDS best correlating CAG findings (SSS/SDS). Results were compared using Pearson's correlation coefficient. RESULTS: Forty-nine stenoses were observed in 102 major coronary territories. Mean value of CTA-based MAAR was 28.3 ± 14.0%. SSS-based, SDS-based, and SSS/SDS-based MAAR was 30.1 ± 6.1%, 20.1 ± 15.8%, and 26.8 ± 15.7%, respectively. CTA-based MAAR was significantly related to SPECT-based MAAR (r = 0.531 for SSS; r = 0.494 for SDS; r = 0.814 for SSS/SDS; P < 0.05 in each). CONCLUSIONS: CTA-based Voronoi algorithm myocardial segmentation reliably quantifies SPECT-based MAAR. KEY POINTS: • Voronoi algorithm allows for three-dimensional myocardial segmentation of coronary CT angiography • Stenosis-related CT myocardial territories correlate to SPECT based area at risk • CT angiography myocardial segmentation may assist in clinical decision-making.
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: J Nucl Cardiol Date: 2002 Mar-Apr Impact factor: 5.952
Authors: W G Austen; J E Edwards; R L Frye; G G Gensini; V L Gott; L S Griffith; D C McGoon; M L Murphy; B B Roe Journal: Circulation Date: 1975-04 Impact factor: 29.690
Authors: Henrique G Debarba; Dinamar J Zanchet; Daiane Fracaro; Anderson Maciel; Antonio N Kalil Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2010
Authors: Robert C Hendel; Daniel S Berman; Marcelo F Di Carli; Paul A Heidenreich; Robert E Henkin; Patricia A Pellikka; Gerald M Pohost; Kim A Williams Journal: J Am Coll Cardiol Date: 2009-06-09 Impact factor: 24.094
Authors: K Lance Gould; Nils P Johnson; Timothy M Bateman; Rob S Beanlands; Frank M Bengel; Robert Bober; Paolo G Camici; Manuel D Cerqueira; Benjamin J W Chow; Marcelo F Di Carli; Sharmila Dorbala; Henry Gewirtz; Robert J Gropler; Philipp A Kaufmann; Paul Knaapen; Juhani Knuuti; Michael E Merhige; K Peter Rentrop; Terrence D Ruddy; Heinrich R Schelbert; Thomas H Schindler; Markus Schwaiger; Stefano Sdringola; John Vitarello; Kim A Williams; Donald Gordon; Vasken Dilsizian; Jagat Narula Journal: J Am Coll Cardiol Date: 2013-08-28 Impact factor: 24.094
Authors: Richard T George; Armin Arbab-Zadeh; Julie M Miller; Kakuya Kitagawa; Hyuk-Jae Chang; David A Bluemke; Lewis Becker; Omair Yousuf; John Texter; Albert C Lardo; João A C Lima Journal: Circ Cardiovasc Imaging Date: 2009-03-31 Impact factor: 7.792
Authors: Frank Gijsen; Yuki Katagiri; Peter Barlis; Christos Bourantas; Carlos Collet; Umit Coskun; Joost Daemen; Jouke Dijkstra; Elazer Edelman; Paul Evans; Kim van der Heiden; Rod Hose; Bon-Kwon Koo; Rob Krams; Alison Marsden; Francesco Migliavacca; Yoshinobu Onuma; Andrew Ooi; Eric Poon; Habib Samady; Peter Stone; Kuniaki Takahashi; Dalin Tang; Vikas Thondapu; Erhan Tenekecioglu; Lucas Timmins; Ryo Torii; Jolanda Wentzel; Patrick Serruys Journal: Eur Heart J Date: 2019-11-01 Impact factor: 29.983