  Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo
method to non-invasively examine the microstructure of the human heart. Current
research in DT-CMR aims to improve the understanding of how the cardiac
microstructure relates to the macroscopic function of the healthy heart as well
as how microstructural dysfunction contributes to disease. To get the final
DT-CMR metrics, we need to acquire diffusion weighted images of at least 6
directions. However, due to DWI's low signal-to-noise ratio, the standard voxel
size is quite big on the scale for microstructures. In this study, we explored
the potential of deep-learning-based methods in improving the image quality
volumetrically (x4 in all dimensions). This study proposed a novel framework to
enable volumetric super-resolution, with an additional model input of
high-resolution b0 DWI. We demonstrated that the additional input could offer
higher super-resolved image quality. Going beyond, the model is also able to
super-resolve DWIs of unseen b-values, proving the model framework's
generalizability for cardiac DWI superresolution. In conclusion, we would then
recommend giving the model a high-resolution reference image as an additional
input to the low-resolution image for training and inference to guide all
super-resolution frameworks for parametric imaging where a reference image is
available.
