Journal of Cardiovascular Magnetic Resonance (Jan 2024)

Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T1-weighted characterization to predict periprocedural myocardial injury

  • Motoki Nakazawa,
  • Hidenari Matsumoto,
  • Debiao Li,
  • Piotr J. Slomka,
  • Damini Dey,
  • Sebastien Cadet,
  • Koji Isodono,
  • Daisuke Irie,
  • Satoshi Higuchi,
  • Hiroki Tanisawa,
  • Hidefumi Ohya,
  • Ryoji Kitamura,
  • Yoshiaki Komori,
  • Tetsuichi Hondera,
  • Ikumi Sato,
  • Hsu-Lei Lee,
  • Anthony G. Christodoulou,
  • Yibin Xie,
  • Toshiro Shinke

Journal volume & issue
Vol. 26, no. 1
p. 100999

Abstract

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Background: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). Methods: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit. Results: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805–0.936] vs. 0.787 [95% CI, 0.706–0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754–0.962) vs. 0.750 for PMR (95% CI, 0.597–0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-μL increase; 95% CI, 1.01–1.30, p = 0.035). Conclusions: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.

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