title: 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.
authors: Department of Radiology, Computational Radiology Laboratory, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA. benoit.scherrer@childrens.harvard.edu
authors: Benoit
authors: B
authors: Scherrer
authors: Ali
authors: A
authors: Gholipour
authors: Simon K
authors: SK
authors: Warfield
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity