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Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging
German Ctr Neurodegenerat Dis DZNE, Germany; Univ Bonn, Germany.
Univ Bonn, Germany; Univ Bonn, Germany.
German Ctr Neurodegenerat Dis DZNE, Germany.
Pontificia Univ Catolica Chile, Chile.
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2019 (English)In: NMR in Biomedicine, ISSN 0952-3480, E-ISSN 1099-1492, Vol. 32, no 3, article id e4055Article in journal (Refereed) Published
Abstract [en]

Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.

Place, publisher, year, edition, pages
WILEY , 2019. Vol. 32, no 3, article id e4055
Keywords [en]
basis functions; compressed sensing; diffusion MRI; diffusion spectrum imaging; microstructure; q-space undersampling; sparse acquisition
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-154993DOI: 10.1002/nbm.4055ISI: 000458911900007PubMedID: 30637831OAI: oai:DiVA.org:liu-154993DiVA, id: diva2:1297554
Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-03-20

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Knutsson, Hans
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Division of Biomedical EngineeringFaculty of Science & EngineeringCenter for Medical Image Science and Visualization (CMIV)
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Citation style
  • apa
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  • asciidoc
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