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2015 | OriginalPaper | Buchkapitel

A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI

verfasst von : Lipeng Ning, Kawin Setsompop, Oleg Michailovich, Nikos Makris, Carl-Fredrik Westin, Yogesh Rathi

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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Abstract

We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use sub-pixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images.

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Vorheriges Kapitel A Joint Acquisition-Estimation Framework for MR Phase Imaging

Nächstes Kapitel Accelerated High Spatial Resolution Diffusion-Weighted Imaging

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Titel: A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI
verfasst von: Lipeng Ning
Kawin Setsompop
Oleg Michailovich
Nikos Makris
Carl-Fredrik Westin
Yogesh Rathi
Verlag: Springer International Publishing
Buch: Information Processing in Medical Imaging
Print ISBN: 978-3-319-19991-7

Electronic ISBN: 978-3-319-19992-4

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-19992-4_5

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