  Understanding the structure of the heart at the microscopic scale of
cardiomyocytes and their aggregates provides new insights into the mechanisms
of heart disease and enables the investigation of effective therapeutics.
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive
technique that can resolve the microscopic structure, organisation, and
integrity of the myocardium without the need for exogenous contrast agents.
However, this technique suffers from relatively low signal-to-noise ratio (SNR)
and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR
techniques rely on acquiring and averaging multiple signal acquisitions to
improve the SNR. Moreover, in order to mitigate the influence of respiratory
movement, patients are required to perform many breath holds which results in
prolonged acquisition durations (e.g., ~30 mins using the existing technology).
In this study, we propose a novel cascaded Convolutional Neural Networks (CNN)
based compressive sensing (CS) technique and explore its applicability to
improve DT-CMR acquisitions. Our simulation based studies have achieved high
reconstruction fidelity and good agreement between DT-CMR parameters obtained
with the proposed reconstruction and fully sampled ground truth. When compared
to other state-of-the-art methods, our proposed deep cascaded CNN method and
its stochastic variation demonstrated significant improvements. To the best of
our knowledge, this is the first study using deep CNN based CS for the DT-CMR
reconstruction. In addition, with relatively straightforward modifications to
the acquisition scheme, our method can easily be translated into a method for
online, at-the-scanner reconstruction enabling the deployment of accelerated
DT-CMR in various clinical applications.
