CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging
This addresses a persistent problem in noninvasive investigation of human heart fiber structures for medical imaging applications, though it appears incremental as it builds on existing DTI techniques with a new hybrid method.
The researchers tackled motion-induced signal loss in in vivo cardiac diffusion tensor imaging (DTI) during free-breathing acquisitions by proposing a novel motion-compensation method based on an invertible Wavelet Scattering Convolutional Neural Network (WSCNN), which effectively compensates for motion and produces higher-quality images with more coherent fiber structures compared to existing methods.
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.