Abstract
In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of
diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a
non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented
with a
gold standard
built on averaging 10 high-resolution DW acquis
itions. A comparison with classical interpo-
lation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in
termsofimprovementsonimagereconstruction,fractiona
lanisotropy(FA)estimation,generalizedFAandangular
reconstruction for tensor and high angular resolut
ion diffusion imaging (HARDI) models. Besides,
fi
rst results of
reconstructed ultra high resolution DW
images are presented at 0.6 × 0.6 × 0.6 mm
3
and0.4×0.4×0.4mm
3
using our
gold standard
based on the average of 10 acquisitions, and on a single acquisition. Finally,
fi
ber tracking
results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.
