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Image post-processing corrects for cardiac and respiratory motion (MoCo) during cardiovascular magnetic resonance (CMR) stress perfusion. The study analyzed its influence on visual image evaluation. Sixty-two patients with (suspected) coronary artery disease underwent a standard CMR stress perfusion exam during free-breathing. Image post-processing was performed without (non-MoCo) and with MoCo (image intensity normalization; motion extraction with iterative non-rigid registration; motion warping with the combined displacement field). Images were evaluated regarding the perfusion pattern (perfusion deficit, dark rim artifact, uncertain signal loss, and normal perfusion), the general image quality (non-diagnostic, imperfect, good, and excellent), and the reader’s subjective confidence to assess the images (not confident, confident, very confident). Fifty-three (non-MoCo) and 52 (MoCo) myocardial segments were rated as ‘perfusion deficit’, 113 vs. 109 as ‘dark rim artifacts’, 9 vs. 7 as ‘uncertain signal loss’, and 817 vs. 824 as ‘normal’. Agreement between non-MoCo and MoCo was high with no diagnostic difference per-patient. The image quality of MoCo was rated more often as ‘good’ or ‘excellent’ (92 vs. 63%), and the diagnostic confidence more often as “very confident” (71 vs. 45%) compared to non-MoCo. The comparison of perfusion images acquired during free-breathing and post-processed with and without motion correction demonstrated that both methods led to a consistent evaluation of the perfusion pattern, while the image quality and the reader’s subjective confidence to assess the images were rated more favorably for MoCo.

Purpose Short axis CineMRI has become the gold standard to measure Right Ventricular Ejection Fraction (RVEF). However, it requires a time-consuming manual contouring of the endocardium. Therefore, many examinations do not include complete RV study. We hypothesized that a simple index could be used to detect patients with abnormal RVEF requiring a precise RV study. Two classical RV function indices were tested: 1/ RV fractional area change (FAC) measured in a mid-ventricular short-axis slice and 2/ RV shortening fraction (SF) corresponding to the longitudinal contraction measured in the horizontal long-axis view. They were then combined to obtain the Surface-Length Index: SLI=1- (1-FAC) (1-αSF). This formula was derived from the application of a basal-to-apex gradient in the RV longitudinal contraction into the crescentic shell model published in 1989 by Aebischer. Methods 400 patients underwent a conventional cardiac MRI with a horizontal long-axis view and a stack of contiguous 8 mm short-axis slices at 1.5T with SSFP sequences and were divided in 2 groups: 60 patients retrospectively included to determine α by optimization of the area under ROC curves (Group A) and 340 patients prospectively included to test SLI, FAC and SF capacity to predict a RVEF alteration ( Results In group A, the optimal value for α was 1.3. In group B, SLI, FAC and SF area under the ROC curves were respectively 0.94, 0.87 and 0.81. SLI allowed a better detection of RV dysfunction (p Conclusion SLI combines two simple RV measures in end-diastole and end-systole (around 1 minute), and allows significant improvement in post-processing efficiency by pre-selecting RV requiring a complete study. The gain in the operator processing time is close to 1/3. Download : Download full-size image Figure . Abstract 103 – Correlation between SLI (vertical) and EF (horiz.)