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Super-Resolution in Diffusion-Weighted Imaging

  • Benoit Scherrer
  • Ali Gholipour
  • Simon K. Warfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white-matter but suffers from a relatively poor resolution. In this work we propose a super-resolution reconstruction (SRR) technique based on the acquisition of multiple anisotropic orthogonal DWI scans. We address the problem of patient motions by aligning the volumes both in space and in q-space. The SRR is formulated as a maximum a posteriori (MAP) problem. It relies on a volume acquisition model which describes the generation of the acquired scans from the unknown high-resolution image. It enables the introduction of image priors that exploit spatial homogeneity and enables regularized solutions. We detail our resulting SRR optimization procedure and report various experiments including numerical simulations, synthetic SRR scenario and real world SRR scenario. Super-resolution reconstruction in DWI may enable DWI to be performed with unprecedented resolution.

Keywords

Diffusion imaging super-resolution orthogonal acquisitions 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benoit Scherrer
    • 1
  • Ali Gholipour
    • 1
  • Simon K. Warfield
    • 1
  1. 1.Computational Radiology Laboratory, Department of RadiologyChildren’s Hospital BostonBostonUSA

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