OBJECTIVE: Noninvasive contrast-enhanced coronary computed tomography (CT) angiography enables distinction between calcified and noncalcified atherosclerotic plaques. However, separation of noncalcified plaques into rupture prone lipid-rich and stable fibrous subtypes is challenging because CT density of the plaque, characterized by Hounsfield Units (HU), varies with intraluminal contrast density and acquisition protocol. This study aims at testing the influence of intraluminal contrast densities and kV-settings on coronary plaque density values in vitro. MATERIALS AND METHODS: We scanned 16 coronary arteries with 3 different contrast solutions (no contrast, 1:70, and 1:23 Iomeron, 350 mgI/mL) and 3 different kV-settings (80, 120, and 140 kV). The arteries were sectioned into 5-mm segments. Every segment was evaluated with CT and histopathology for suitability of analysis, presence, and subtype of plaque. RESULTS: Sixty-four segments were analyzed and classified with CT. Agreement between plaques classified with CT angiography in vitro and histopathology was poor-to-moderate, with no kappa-values above 0.21. The kV-settings affected the CT density in all plaque types. The CT density decreased 0.25 (0.07) HU, P=0.013 in noncalcified plaques, and 5.5 (0.7) HU, P<0.0001, in calcified plaques for every kV increase. CT densities in noncalcified plaques changed when the contrast concentration was changed. From no to high contrast concentration resulted in a 21.7 (8.3) HU increase, P=0.041, and from low to high contrast concentration resulted in a 21.5 (6) HU increase, P=0.011, causing several plaques to change in subtype from lipid-rich (low contrast concentration) to fibrotic (high contrast concentration). CONCLUSION: Agreement between CT angiography in vitro and histopathology for classification of coronary plaque subtype is poor to moderate. However, no specific combination seems superior to the most commonly used protocols for distinction between lipid-rich and fibrotic plaque subtypes in current clinical practice.
OBJECTIVE: Noninvasive contrast-enhanced coronary computed tomography (CT) angiography enables distinction between calcified and noncalcified atherosclerotic plaques. However, separation of noncalcified plaques into rupture prone lipid-rich and stable fibrous subtypes is challenging because CT density of the plaque, characterized by Hounsfield Units (HU), varies with intraluminal contrast density and acquisition protocol. This study aims at testing the influence of intraluminal contrast densities and kV-settings on coronary plaque density values in vitro. MATERIALS AND METHODS: We scanned 16 coronary arteries with 3 different contrast solutions (no contrast, 1:70, and 1:23 Iomeron, 350 mgI/mL) and 3 different kV-settings (80, 120, and 140 kV). The arteries were sectioned into 5-mm segments. Every segment was evaluated with CT and histopathology for suitability of analysis, presence, and subtype of plaque. RESULTS: Sixty-four segments were analyzed and classified with CT. Agreement between plaques classified with CT angiography in vitro and histopathology was poor-to-moderate, with no kappa-values above 0.21. The kV-settings affected the CT density in all plaque types. The CT density decreased 0.25 (0.07) HU, P=0.013 in noncalcified plaques, and 5.5 (0.7) HU, P<0.0001, in calcified plaques for every kV increase. CT densities in noncalcified plaques changed when the contrast concentration was changed. From no to high contrast concentration resulted in a 21.7 (8.3) HU increase, P=0.041, and from low to high contrast concentration resulted in a 21.5 (6) HU increase, P=0.011, causing several plaques to change in subtype from lipid-rich (low contrast concentration) to fibrotic (high contrast concentration). CONCLUSION: Agreement between CT angiography in vitro and histopathology for classification of coronary plaque subtype is poor to moderate. However, no specific combination seems superior to the most commonly used protocols for distinction between lipid-rich and fibrotic plaque subtypes in current clinical practice.
Authors: Sang-Eun Lee; Ji Min Sung; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Kavitha Chinnaiyan; Jung Hyun Choi; Eun Ju Chun; Edoardo Conte; Ilan Gottlieb; Martin Hadamitzky; Yong Jin Kim; Amit Kumar; Byoung Kwon Lee; Jonathon A Leipsic; Erica Maffei; Hugo Marques; Gianluca Pontone; Gilbert Raff; Sanghoon Shin; Peter H Stone; Habib Samady; Renu Virmani; Jagat Narula; Daniel S Berman; Leslee J Shaw; Jeroen J Bax; Fay Y Lin; James K Min; Hyuk-Jae Chang Journal: Eur Heart J Cardiovasc Imaging Date: 2019-11-01 Impact factor: 6.875
Authors: Wei Hua Yin; Yan Zhang; Xiang Nan Li; Hong Yue Wang; Yun Qiang An; Yang Sun; Zhi Hui Hou; Yang Gao; Bin Lu; Zhe Zheng Journal: Korean J Radiol Date: 2020-02 Impact factor: 3.500
Authors: Kajetan Grodecki; Sebastien Cadet; Adam D Staruch; Anna M Michalowska; Cezary Kepka; Rafal Wolny; Jerzy Pregowski; Mariusz Kruk; Mariusz Debski; Artur Debski; Ilona Michalowska; Piotr J Slomka; Adam Witkowski; Damini Dey; Maksymilian P Opolski Journal: Clin Res Cardiol Date: 2020-05-08 Impact factor: 5.460