Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data

We present a methodology for reconstructing full-cycle respiratory and cardiac gated short-axis cine MR sequences from real-time MR data. For patients who are too ill or otherwise incapable of consistent breath holds, real-time MR sequences are the preferred means of acquiring cardiac images, but suffer from inferior image quality compared to standard short-axis sequences and lack cardiac ECG gating. To construct a sequence from real-time images which, as close as possible, replicates the characteristics of short-axis series, the phase of the cardiac cycle must be estimated for each image and the left ventricle identified, to be used as a landmark for slice re-alignment. Our method employs CNN-based deep learning to segment the left ventricle in the real-time sequence, which is then used to estimate the pool volume and thus the position of each image in the cardiac cycle. We then use manifold learning to account for the respiratory cycle so as to select images of the best quality at expiration. From these images a selection is made to automatically reconstruct a single cardiac cycle, and the images and segmentations are then aligned. The aligned pool segmentations can then be used to calculate volume over time and thus volume-based biomarkers.