Automatic myocardial ischemic lesion detection on magnetic resonance perfusion weighted imaging prior perfusion quantification: A pre-modeling strategy

Even if cardiovascular magnetic resonance (CMR) perfusion imaging has proven its relevance for visual detection of ischemia, myocardial blood flow (MBF) quantification at the voxel observation scale remains challenging. Integration of an automated segmentation step, prior to perfusion index estimation, might be a significant reconstruction component that could allow sustainable assumptions and constraint enlargement prior to advanced modeling. Current clustering techniques, such as bullseye representation or manual delineation, are not designed to discriminate voxels belonging to the lesion from healthy areas. Hence, the resulting average time-intensity curve, which is assumed to represent the dynamic contrast enhancement inside of a lesion, might be contaminated by voxels with perfectly healthy microcirculation. This study introduces a hierarchical lesion segmentation approach based on time-intensity curve features that considers the spatial particularities of CMR myocardial perfusion. A first k-means clustering approach enables this method to perform coarse clustering, which is refined by a novel spatiotemporal region-growing (STRG) segmentation, thus ensuring spatial and time-intensity curve homogeneity. Over a cohort of 30 patients, myocardial blood flow (MBF) measured in voxels of lesion regions detected with STRG was significantly lower than in regions drawn manually (mean difference = 0.14, 95% CI [0.07, 0.2]) and defined with the bullseye template (mean difference = 0.25, 95% CI [0.17, 0.36]). Over the 90 analyzed slices, the median Dice score calculated against the ground truth ranged between 0.62 and 0.67, the inclusion coefficients ranged between 0.62 and 0.76 and the centroid distances ranged between 0.97 and 3.88 mm. Therefore, though these metrics highlight spatial differences, they could not be used as an index to evaluate the accuracy and performance of the method, which can only be attested by the variability of the MBF clinical index.