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Compressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions

Institution:
1Scientific Computing and Imaging Institute, Salt Lake City, UT, USA.
2Department of Psychiatry, University of North Carolina, NC, USA.
3Tandon School of Engineering, Department of Computer Science & Engineering, NYU, NY, USA.
Publication Date:
Jan-2016
Journal:
Proc IEEE Int Symp Biomed Imaging
Volume Number:
2016
Pages:
907-910
Citation:
Proc IEEE Int Symp Biomed Imaging. 2016;2016:907-910.
PubMed ID:
29492184
PMCID:
PMC5826629
Keywords:
Artifact reduction, Compressive sensing, Diffusion weighted imaging, QBI, SHORE, Within-gradient motion
Appears in Collections:
NA-MIC
Sponsors:
R01 HD055741/HD/NICHD NIH HHS/United States
R01 HD067731/HD/NICHD NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Elhabian S., Vachet C., Piven J., Styner M., Gerig G. Compressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions. Proc IEEE Int Symp Biomed Imaging. 2016;2016:907-910. PMID: 29492184. PMCID: PMC5826629.
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Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereasslow bulkmotion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms,fast bulkmotion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenuation due to the difficulty of their retrospective correction, with the disadvantage to lose full gradients for further processing. Nonetheless, such attenuation might only affect limited number of slices within a gradient volume. Q-space resampling has recently been proposed to recover corrupted slices while saving gradients for subsequent reconstruction. However, few corrupted gradients are implicitly assumed which might not hold in case of scanning unsedated infants or patients in pain. In this paper, we propose to adopt recent advances in compressive sensing based reconstruction of the diffusion orientation distribution functions (ODF) with under sampled measurements to resample corrupted slices. We make use of Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE) basis functions which can analytically model ODF from arbitrary sampled signals. We demonstrate the impact of the proposed resampling strategy compared to state-of-art resampling and gradient exclusion on simulated intra-gradient motion as well as samples from real DWI data.

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