| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 5678156 | Chronic Diseases and Translational Medicine | 2016 | 7 Pages |
ObjectiveTo comparatively evaluate black-blood coronary arterial wall MRI and 64-multidetector computed tomography (64-MDCT) for detection and classification of coronary artery plaques.MethodsWe included 15 patients with confirmed coronary artery plaques in the proximal or middle segments of coronary arteries by 64-MDCT, who underwent black-blood coronary wall MRI at 1.5 T within 10 days. Cross-sectional coronary wall images were acquired using a 2D double-inversion-recovery, electrocardiograph-triggered, navigator-gated, fat-suppressed, turbo-spin-echo sequence on the coronary arteries with lesions from the ostium to the middle segment continuously without gap. The vessel cross-sectional area (CSA), luminal CSA, maximal wall thickness, plaque burden, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured in each slice and subsequently compared with computed tomography angiography (CTA) images. CTA images were divided into 5-mm segments for side-by-side comparison with MRI.ResultsOf the 15 patients, 12 were enrolled in the study. Coronary plaques were found in 46 slices on both CTA and MRI. Plaques were classified to 3 groups based on CTA: calcified plaques (n = 11), soft plaques (n = 23), and mixed plaques (n = 12). In MRI, the plaque burden, maximal wall thickness, SNR, and CNR in the coronary walls containing plaques were greater than in the normal coronary walls (0.83 ± 0.08 vs. 0.73 ± 0.08, 1.88 ± 0.51 vs. 1.51 ± 0.26 mm, 12.95 ± 2.78 vs. 9.93 ± 2.31, and 6.76 ± 2.52 vs. 3.89 ± 1.54, respectively; P < 0.05). The luminal CSA at the plaque was smaller than in normal coronary walls (2.50 ± 1.50 vs. 4.72 ± 2.28 mm2; P < 0.05). The SNR in the soft plaque was significantly greater than in calcified and mixed plaques (P < 0.05).ConclusionsCoronary wall MRI can identify coronary plaques in the proximal and middle segments and has the potential to differentiate plaque types based on signal intensity.
