  In vivo diffusion tensor imaging (DTI) is a promising technique to
investigate noninvasively the fiber structures of the in vivo human heart.
However, signal loss due to motions remains a persistent problem in in vivo
cardiac DTI. We propose a novel motion-compensation method for investigating in
vivo myocardium structures in DTI with free-breathing acquisitions. The method
is based on an invertible Wavelet Scattering achieved by means of Convolutional
Neural Network (WSCNN). It consists of first extracting translation-invariant
wavelet scattering features from DW images acquired at different trigger delays
and then mapping the fused scattering features into motion-compensated spatial
DW images by performing an inverse wavelet scattering transform achieved using
CNN. The results on both simulated and acquired in vivo cardiac DW images
showed that the proposed WSCNN method effectively compensates for
motion-induced signal loss and produces in vivo cardiac DW images with better
quality and more coherent fiber structures with respect to existing methods,
which makes it an interesting method for measuring correctly the diffusion
properties of the in vivo human heart in DTI under free breathing.
