Investigation of in Vivo Human Cardiac Diffusion Tensor Imaging Using Unsupervised Dense Encoder-Fusion-Decoder Network

Abstract

Diffusion tensor imaging (DTI) is currently the unique imaging technique that can detect the structure of in-vivo human myocardium without invasivity and radiation. However, it is particularly sensitive to motions, especially respiratory motion that results in serious signal loss in diffusion-weighted (DW) images. This makes it impossible to accurately measure cardiac microscopic structural properties. To cope with such problem, this paper proposes an unsupervised dense-encoder-fusion-decoder network (DEFD-net) to compensate for signal loss in cardiac DW images, which allows investigating in-vivo myocardium structure more accurately. The DEFD-net consists of three modules, namely dense-encoder, fusion module and decoder module. The dense-encoder and decoder are trained firstly with DW images acquired at different trigger delays in an unsupervised manner for extracting local and global features. A fusion strategy is then designed to fuse the extracted features. Finally, the well-trained decoder is used to reconstruct the fused DW image from the fused features. To validate the superiority of the proposed method, comparison with existing methods such as PCAMIP, WIF and U2Fusion is performed on both simulated and acquired datasets. The experimental results showed that the proposed method effectively compensates for motion-induced signal loss in DW images, thus leading to much better DW image quality with respect to existing methods. Moreover, the subsequently derived myocardium fiber structure is more regular.

Keywords

• Magnetic resonance diffusion tensor imaging
• in vivo cardiac Imaging
• deep learning
• feature fusion
• motion compensation