CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging

TL;DR

This paper introduces a CNN-based invertible wavelet scattering method to improve motion compensation in in vivo cardiac DTI, enhancing image quality and fiber structure coherence during free-breathing scans.

Contribution

It presents a novel motion-compensation technique using invertible wavelet scattering and CNNs for better in vivo cardiac DTI imaging under free-breathing conditions.

Findings

Effective motion compensation in cardiac DTI images.

Improved image quality and fiber coherence.

Validated on simulated and real in vivo data.

Abstract

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.