Diagnostic accuracy of late iodine-enhancement dual-energy computed tomography for the detection of chronic myocardial infarction compared with late gadolinium-enhancement 3-T magnetic resonance imaging.

OBJECTIVES

The purpose of the study was to compare the performance of late iodine-enhancement (LIE) dual-energy computed tomography (DECT) linear blending and selective myocardial iodine mapping for the detection of chronic myocardial infarction (CMI) with late gadolinium-enhancement (LGE) 3-T magnetic resonance imaging.

MATERIALS AND METHODS

This study was approved by the institutional review board, and the patients gave informed consent. A total of 20 patients with a history of CMI underwent cardiac LIE-DECT and LGE-MRI. Images of the LIE-DECT were reconstructed as 100 kilovolt (peak) (kV[p]), 140 kV(p), and weighted-average (WA; linear blending) images from low- and high-kilovoltage peak data using 3 different weighting factors (0.8, 0.6, 0.3). Additional color-coded myocardial iodine distribution maps were calculated. The images were reviewed for the presence of late enhancement, transmural extent, signal characteristics, infarct volume, and subjective image quality.

RESULTS

Segmental analysis of LIE-DECT data from 100 kV(p), WA of 0.8, and WA of 0.6 showed identical results for the identification of CMI (89% sensitivity, 98% specificity, 96% accuracy) and correctly identified all segments with transmural scarring detected through LGE-MRI. Weighted average of 0.6 received the best subjective image quality rating (15/20 votes) and average measured infarct size correlated best with LGE-MRI (5.7% difference). In comparison with LGE-MRI, iodine distribution maps were susceptible to false-positive and false-negative findings (52% sensitivity, 88% specificity, 81% accuracy), overestimating quantity of transmural scars by 78% while underestimating infarct volume by 55%.

CONCLUSIONS

Late iodine enhancement cardiac dual-energy computed tomography correlates well with LGE-MRI for detecting CMI, whereas iodine distribution analysis provides inferior accuracy. Linear blending further improves image quality and enables more precise estimation of scar volume.