Yasutoshi Ohta1, Junichi Kishimoto2, Shinichiro Kitao3, Hiroto Yunaga3, Natsuko Mukai-Yatagai4, Shinya Fujii3, Kazuhiro Yamamoto4, Tetsuya Fukuda5, Toshihide Ogawa6. 1. Division of Radiology, Department of Pathophysiological Therapeutic Science, Tottori University Faculty of Medicine, Yonago City, Tottori, 683-8504, Japan; National Cerebral and Cardiovascular Center, Suita City, Osaka, 565-8565, Japan. Electronic address: ota.yasutoshi@ncvc.go.jp. 2. Tottori University Hospital, Department of Clinical Radiology, Yonago City, Tottori, 683-8504, Japan. 3. Division of Radiology, Department of Pathophysiological Therapeutic Science, Tottori University Faculty of Medicine, Yonago City, Tottori, 683-8504, Japan. 4. Division of Molecular Medicine and Therapeutics, Department of Multidisciplinary Internal Medicine, Tottori University Faculty of Medicine, Yonago City, Tottori, 683-8504, Japan. 5. National Cerebral and Cardiovascular Center, Suita City, Osaka, 565-8565, Japan. 6. Division of Radiology, Department of Pathophysiological Therapeutic Science, Tottori University Faculty of Medicine, Yonago City, Tottori, 683-8504, Japan; Kurashiki Heisei Hospital, Department of Radiology, Kurashiki City, Okayama, 710-0826, Japan.
Abstract
PURPOSE: To measure myocardial extracellular volume fraction (ECV) for each region or segment using iodine density image (IDI) with single-source dual-energy computed tomography (DECT) and compare the results with an MRI T1 mapping approach. MATERIALS AND METHODS: For this prospective study, 79 consecutive heart failure patients referred for MRI were included and 23 patients (14 men, 63 ± 14 years) who underwent both MRI and late contrast enhancement DECT following coronary CT angiography were evaluated. CT-ECV was computed from IDI using late acquisition projection data. MR-ECV was computed from native and post-contrast T1 maps using non-rigid image registration for segments with evaluable image quality from 3.0-T MRI. Regional CT-ECV and MR-ECV were measured based on 16-segment models. CT-ECV and MR-ECV were compared using Pearson correlations. Agreement among methods was assessed using Bland-Altman comparisons. RESULTS: In the 368 segments, although all segments were evaluable on IDI, 37 segments were rated as non-evaluable on T1 maps. Overall, 331 segments were analyzed. Mean CT-ECV and MR-ECV were 31.6 ± 9.1 and 33.2 ± 9.1, respectively. Strong correlations were seen between CT-ECV and MR-ECV for each region, as follows: all segments, r = 0.837; septal, r = 0.871; mid-septal, r = 0.895; anterior, r = 0.869; inferior, r = 0.793; and lateral, 0.864 (all p < 0.001). Differences between CT-ECV and MR-ECV were as follows: all segments, 1.13 ± 4.98; septal, -1.51 ± 4.37; mid-septal, -1.85 ± 4.22; anterior, 2.54 ± 4.89; inferior, 1.2 ± 5.78; and lateral, 2.65 ± 3.98. CONCLUSION: ECV using DECT and from cardiac MRI showed a strong correlation on regional and segmental evaluations. DECT is useful for characterizing myocardial ECV changes as well as MRI.
PURPOSE: To measure myocardial extracellular volume fraction (ECV) for each region or segment using iodine density image (IDI) with single-source dual-energy computed tomography (DECT) and compare the results with an MRI T1 mapping approach. MATERIALS AND METHODS: For this prospective study, 79 consecutive heart failurepatients referred for MRI were included and 23 patients (14 men, 63 ± 14 years) who underwent both MRI and late contrast enhancement DECT following coronary CT angiography were evaluated. CT-ECV was computed from IDI using late acquisition projection data. MR-ECV was computed from native and post-contrast T1 maps using non-rigid image registration for segments with evaluable image quality from 3.0-T MRI. Regional CT-ECV and MR-ECV were measured based on 16-segment models. CT-ECV and MR-ECV were compared using Pearson correlations. Agreement among methods was assessed using Bland-Altman comparisons. RESULTS: In the 368 segments, although all segments were evaluable on IDI, 37 segments were rated as non-evaluable on T1 maps. Overall, 331 segments were analyzed. Mean CT-ECV and MR-ECV were 31.6 ± 9.1 and 33.2 ± 9.1, respectively. Strong correlations were seen between CT-ECV and MR-ECV for each region, as follows: all segments, r = 0.837; septal, r = 0.871; mid-septal, r = 0.895; anterior, r = 0.869; inferior, r = 0.793; and lateral, 0.864 (all p < 0.001). Differences between CT-ECV and MR-ECV were as follows: all segments, 1.13 ± 4.98; septal, -1.51 ± 4.37; mid-septal, -1.85 ± 4.22; anterior, 2.54 ± 4.89; inferior, 1.2 ± 5.78; and lateral, 2.65 ± 3.98. CONCLUSION: ECV using DECT and from cardiac MRI showed a strong correlation on regional and segmental evaluations. DECT is useful for characterizing myocardial ECV changes as well as MRI.
Authors: Gianluca Pontone; Alexia Rossi; Marco Guglielmo; Marc R Dweck; Oliver Gaemperli; Koen Nieman; Francesca Pugliese; Pal Maurovich-Horvat; Alessia Gimelli; Bernard Cosyns; Stephan Achenbach Journal: Eur Heart J Cardiovasc Imaging Date: 2022-03-22 Impact factor: 9.130
Authors: Serena Dell'Aversana; Raffaele Ascione; Marco De Giorgi; Davide Raffaele De Lucia; Renato Cuocolo; Marco Boccalatte; Gerolamo Sibilio; Giovanni Napolitano; Giuseppe Muscogiuri; Sandro Sironi; Giuseppe Di Costanzo; Enrico Cavaglià; Massimo Imbriaco; Andrea Ponsiglione Journal: J Imaging Date: 2022-09-01
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