2014
DOI: 10.1007/978-3-319-11182-7_18
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Dictionary Based Super-Resolution for Diffusion MRI

Abstract: Diffusion magnetic resonance imaging (dMRI) provides unique capabilities for non-invasive mapping of fiber tracts in the brain. It however suffers from relatively low spatial resolution, often leading to partial volume effects. In this paper, we propose to use a super-resolution approach based on dictionary learning for alleviating this problem. Unlike the majority of existing super-resolution algorithms, our proposed solution does not entail acquiring multiple scans from the same subject which renders it prac… Show more

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Cited by 3 publications
(2 citation statements)
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“…Our experiments on phantom and real HARDI brain data show that it is possible to achieve accurate global HARDI reconstructions with a sparse representation of less than one dictionary atom per voxel, exceeding the theoretical limit of the state of the art in sparse coding. Sparse coding has many important applications like de-noising (Ouyang et al, 2013), dictionary learning (Cheng et al, 2015b) and super-resolution (Yoldemir et al, 2014), and, in particular, applying our joint spatial-angular sparse coding framework within the application of (k, q)-CS will be the subject of future work.…”
Section: Introductionmentioning
confidence: 99%
“…Our experiments on phantom and real HARDI brain data show that it is possible to achieve accurate global HARDI reconstructions with a sparse representation of less than one dictionary atom per voxel, exceeding the theoretical limit of the state of the art in sparse coding. Sparse coding has many important applications like de-noising (Ouyang et al, 2013), dictionary learning (Cheng et al, 2015b) and super-resolution (Yoldemir et al, 2014), and, in particular, applying our joint spatial-angular sparse coding framework within the application of (k, q)-CS will be the subject of future work.…”
Section: Introductionmentioning
confidence: 99%
“…Alexander et al [22] implemented a random forest for SR in diffusion MRI with cubic patches and features customized for diffusion tensor images (DTIs). Yoldemir et al [23] applied dictionary learning to diffusion-weighted 3-D images for volumetric SR. Recently, Tanno et al [24] integrated an uncertainty modeling with a 3-D convolutional neural network (CNN) on DTIs.…”
Section: Introductionmentioning
confidence: 99%