A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion-Weighted Imaging Using Denoising Auto-Encoder

To present a novel, nonlinear subspace modeling and joint k-q-space reconstruction technique for high-resolution, multi-band, multi-shell diffusion-weighted imaging (DWI).

High b-value (> 1000 s/mm²), high resolution DWI has the drawback of generally low signal-to-noise ratios (SNRs). We present an approach that leverages a denoising autoencoder (DAE) to learn a latent subspace from biophysically simulated diffusion-weighted signals. The decoder of this network is then used in the forward operator of the image reconstruction process. The decoded latent images are scaled by the $b_0$ image, phase is added and processed as regular DWI images with the forward operator for multi-shot, multicoil, multi-slice, k-q-undersampled acquisition schemes. The performance is investigated with a multi-shell, multi-direction imaging brain scan and compared to the results of the multiplexed sensitivity-encoding (MUSE) reconstruction and locally low-rank (LLR) regularized reconstruction. The results are further validated by a bias and precision analysis of reconstructed fiber directions.

Comparing the reconstructed data using the proposed method shows improved noise suppression compared to MUSE and more details than LLR-reconstructed images. Specifically, in the higher b-value domain, the reconstruction results show improved detectability of small structures. This bias and precision analysis showed minimal bias introduction, but higher precision with the proposed method.

Our method combines deep learning, latent signal modeling, joint k-q-space reconstruction, and biophysical simulation for diffusion data and shows strong noise suppression and a high degree of detail in reconstructed diffusion-weighted images.