Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions

Abstract

Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white matter but suffers from a relatively poor spatial resolution. Increasing the spatial resolution in DWI is challenging with a single-shot EPI acquisition due to the decreased signal-to-noise ratio and T2* relaxation effect amplified with increased echo time. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. DWI scans acquired in different planes are not typically closely aligned due to the geometric distortion introduced by magnetic susceptibility differences in each phase-encoding direction. We compensate each scan for geometric distortion by acquisition of a dual echo gradient echo field map, providing an estimate of the field inhomogeneity. We address the problem of patient motion by aligning the volumes in both space and q-space. The SRR is formulated as a maximum a posteriori problem. It relies on a volume acquisition model which describes how the acquired scans are observations of an unknown high-resolution image which we aim to recover. Our model enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our SRR optimization procedure and report experiments including numerical simulations, synthetic SRR and real world SRR. In particular, we demonstrate that combining distortion compensation and SRR provides better results than acquisition of a single isotropic scan for the same acquisition duration time. Importantly, SRR enables DWI with resolution beyond the scanner hardware limitations. This work provides the first evidence that SRR, which employs conventional single shot EPI techniques, enables resolution enhancement in DWI, and may dramatically impact the role of DWI in both neuroscience and clinical applications.

Interpolation of diffusion weighted imaging datasets

“…Greenspan et al (2002) acquired multiple image volumes with thick but overlapping slices and could thereby reconstruct thinner slices from the overlapping information while maintaining SNR. Similar attempts have recently been presented where multiple images with anisotropic voxels are acquired with multiple orientations (Poot et al, 2012;Scherrer et al, 2012). Such super resolution methods use adapted acquisition protocols and non-standard reconstruction methods, and can therefore not be applied to already existing DWI data, which limits their use in clinical settings.…”

Collaborative patch-based super-resolution for diffusion-weighted images

“…Inspired by works on fetal imaging (Clouchoux et al, 2010;Gholipour et al, 2010;Rousseau et al, 2006), the fusion of 3 orthogonal acquisitions has been recently applied to DW images (Scherrer et al, 2012). This strategy was then extended to arbitrary orientations (Poot et al, 2010) and applied to DW images .…”

“…By using neighborhood information contained in the streamlines, intra-voxel density can be estimated with higher spatial resolution than that of the source DW image to obtain super-resolution track-density images. However, this type of method is built on approaches that are similar to tractography methods and thus do not reconstruct high frequency content of images (Scherrer et al, 2012).…”

Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model

“…Gholipour et al (2010) combined multiple anisotropic acquisitions with a high resolution image. Scherrer et al (2012) used a combination of three orthogonal acquisitions to create high resolution diffusion weighted images. An extension of this work to arbitrary directions is proposed in (Poot et al, 2013).…”