Super-resolution reconstruction of diffusion-weighted images from distortion compensated orthogonal anisotropic acquisitions

Increasing the spatial resolution in diffusion-weighted imaging (DWI) is challenging with a single-shot EPI acquisition because of the decreased SNR and T2* relaxation. Recently, acquisition of orthogonal anisotropic acquisitions and super-resolution reconstruction (SRR) of the underlying high-resolution image has been proposed to achieve higher resolution. Promising results have been shown with simulations. However, practical evidence that SRR enables resolution enhancement remains unclear. Particularly, real DWI scans acquired in orthogonal directions are subject to very different distortion. This makes the precise alignment of the images impossible and strongly perturbs the reconstruction. In this work we propose to combine distortion compensation and SRR. Distortion compensation is achieved by acquisition of a dual echo field map, providing an estimate of the field inhomogeneity. The SRR is formulated as a maximum a posteriori problem and relies on a realistic image generation model. We evaluate our approach with real anisotropic acquisitions. Importantly, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. The SRR provides more detailed structures and better tractography results. This work provides the first evidence that SRR, which employs conventional SS-EPI techniques, may enable resolution enhancement in DWI, and may dramatically impact the way to achieve DW imaging in both neuroscience and clinical applications.