Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Abstract

Acquisition of a series of anisotropically oversampled acquisitions (so-called anisotropic “snapshots”) and reconstruction in the image space has recently been proposed to increase the spatial resolution in diffusion weighted imaging (DWI), providing a theoretical 8× acceleration at equal signal-to-noise ratio (SNR) compared to conventional dense k-space sampling. However, in most works, each DW image is reconstructed separately and the fact that the DW images constitute different views of the same anatomy is ignored. In addition, current approaches are limited by their inability to reconstruct a high resolution (HR) acquisition from snapshots with different subsets of diffusion gradients: an isotropic HR gradient image cannot be reconstructed if one of its anisotropic snapshots is missing, for example due to intra-scan motion, even if other snapshots for this gradient were successfully acquired. In this work, we propose a novel multi-snapshot DWI reconstruction technique that simultaneously achieves HR reconstruction and local tissue model estimation while enabling reconstruction from snapshots containing different subsets of diffusion gradients, providing increased robustness to patient motion and potential for acceleration. Our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We evaluate our approach with synthetic simulations, simulated multi-snapshot scenario and in vivo multi-snapshot imaging. We show that 1) our combined approach ultimately provides both better HR reconstruction and better tissue model estimation and 2) the error in the case of missing snapshots can be quantified. Our novel multi-snapshot technique will enable improved high spatial characterization of the brain connectivity and microstructure in vivo.

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

“…Future work also involves the integration of this motion correction scheme with multi-compartment models of the diffusion signal [43], [50]. The reconstruction part of the algorithm may be further improved by incorporating more realistic physical models of DWI acquisition [51]. …”

Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising

“…This indeed suggests that high resolution acquisition can now be acquired on clinical scanners. Recently, other algorithms enabling a high spatial resolution at the acquisition level have been suggested (Ning et al, 2016;Scherrer et al, 2015). These methods both rely on smartly fusing the (complementary) data of multiple acquisitions acquired at a lower spatial resolution to obtain a single high resolution volume.…”

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

“…For example, SR reconstruction can be carried out with sub-voxel shifted scans in the in-plane [18] and sliceselect [8] dimensions. Scherrer and his colleagues [21] proposed an SR method to reconstruct each DW volume from multiple anisotropic orthogonal scans, which they extended in [20] for estimation of tissue microstructural properties using diffusion compartment imaging. Ning et al [17] proposed a compressed-sensing SR reconstruction framework that increases the spatio-angular resolution of dMRI data by using multiple overlapping thick-slice datasets with undersampling in q-space [17].…”