Deep learning method with integrated invertible wavelet scattering for improving the quality of in vivo cardiac DTI.

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Abstract

Objective
Respiratory motion, cardiac motion, and inherently low signal-to-noise ratio (SNR) are major limitations of in vivo cardiac diffusion tensor imaging (DTI). We propose a novel enhancement method that uses unsupervised learning based invertible wavelet scattering (IWS) to improve the quality of in vivo cardiac DTI.

Approach
Our method starts by extracting nearly transformation-invariant features from multiple cardiac diffusion-weighted (DW) image acquisitions using multi-scale wavelet scattering (WS). The relationship between the WS coefficients and DW images is learned through a multiscale encoder and a decoder network. Using the trained encoder, the deep features of WS coefficients of multiple DW image acquisitions are further extracted and then fused using an average rule. Finally, using the fused WS features and trained decoder, the enhanced DW images are derived.

Main Results
We evaluated the performance of the proposed method by comparing it with several methods on three in vivo cardiac DTI datasets in terms of SNR, contrast to noise ratio (CNR), fractional anisotropy (FA), mean diffusivity (MD), and helix angle (HA). Compared to the best comparison method, SNR/CNR of diastolic, gastric peristalsis influenced, and end systolic DW images were improved by 1%/16%, 5%/6%, and 56%/30%, respectively. The approach also yielded consistent FA and MD values and more coherent helical fiber structures than the comparison methods used in this work.

Significance
The ablation results verify that using the transformation-invariant and noise-robust wavelet scattering features enables effective exploration of useful information from limited data. This provides a potential means to alleviate the dependence of the fusion results on the number of repeated acquisitions, which is beneficial for dealing with noise and residual motion issues simultaneously, thereby improving the quality of in vivo cardiac DTI.© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.