Nearly automated motion artifacts correction between multi breath-hold short-axis and long-axis cine CMR images

We aimed at developing and testing a nearly automated method for breath-hold artifacts compensation in short-axis (SA) cardiac magnetic resonance (CMR) images. The purpose was the reduction of potential misalignment between standard cine SA and two- and four-chamber long-axis (LA) CMR images to allow 3D reconstruction for segmentation or modeling purposes.

The 3D position of each SA image was optimized on the basis of the pixel intensities at the intersections with the two- and four-chamber LA images. The algorithm accuracy was first tested on a dedicated virtual phantom dataset, derived from a high resolution computed tomography frame where known misalignments were applied. The method was then applied to SA and LA CMR end-diastolic and end-systolic frames datasets obtained in 20 consecutive patients. Assessment of the results was performed by two independent observers by visual comparison and by quantifying the residual distances between LA and SA left ventricle endocardial contours before and after correction.

Errors on the simulation dataset were quantified as residual distance from the ground truth position of SA planes and values were found of the order of the pixel resolution. On CMR datasets, a perceived improvement was reported in about 70% of the slices in need for correction and median residual error between manual SA and LA contours was reduced from 2.4mm to 1.8mm.

RESULTS found on virtual and clinical datasets proved feasibility and usefulness of the method as a necessary pre-processing step for volumetric analysis of CMR data in clinical setting.