Cardiac motion estimation using pyramid, warping, and cost volume neural network

Cardiac motion quantification in magnetic resonance (MR) images provides vital information to diagnose and evaluate cardiovascular diseases. Motion quantification can be obtained from routinely acquired MR images. However, the methods available for motion estimation present many sources of inconsistencies, thus creating constraints to use it as a reliable diagnostic technique. Recently, convolutional neural networks (CNNs) have demonstrated to be a powerful tool for many different imaging tasks, including optical flow estimation, a technique widely used for motion estimation. In this work, we evaluate the suitability of a compact and powerful CNN architecture based on Pyramid, Warping, and Cost Volume (PWC) for motion estimation in synthetic cardiac resonance images. The synthetic images were generated using the extended cardiac-torso (XCAT) and MRXCAT software, which generates temporal series of highly detailed MR images and their corresponding ground-truth motion field, which would be impossible to obtain in real-life data. The CNN training was unsupervised, simulating real data. The ground-truth provided by the synthetic images was used to evaluate the PWC performance, determining its reliability. The CNN achieved an average end-point-error of 0.61 ± 0.25 pixel and a mean absolute error of 0.38 ± 0.15 pixel in the test set, surpassing state-of-the-art methods. The results obtained in this work indicate a high potential of the unsupervised PWC network for future applications in real cardiac images.