In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI)
technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights
into cardiac function and enabling the development of innovative therapeutic strategies. However,
the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles
involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In
this study, we investigated and implemented three different types of deep learning-based MRI
reconstruction models for cDTI reconstruction. We evaluated the performance of these models based
on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the
computational cost assessment. Our results indicate that the models discussed in this study can be
applied for clinical use at an acceleration factor (AF) of ×2 and ×4, with the D5C5 model showing
superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There
is no statistical difference from the reference for all diffusion tensor parameters at AF ×2 or most DT
parameters at AF ×4, and the quality of most diffusion tensor parameter maps is visually acceptable.
SwinMR is recommended as the optimal approach for reconstruction at AF ×2 and AF ×4. However,
we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At
AF ×8, the performance of all models discussed remains limited, with only half of the diffusion tensor
parameters being recovered to a level with no statistical difference from the reference. Some diffusion
tensor parameter maps even provide wrong and misleading information.