Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Merry Mani; Mathews Jacob; Arnaud Guidon; Vincent Magnotta; Jianhui Zhong

doi:10.1002/mrm.25119

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Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Journal article

Peer reviewed

Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Merry Mani, Mathews Jacob, Arnaud Guidon, Vincent Magnotta and Jianhui Zhong

Magnetic resonance in medicine, Vol.73(1), pp.126-138

01/2015

DOI: 10.1002/mrm.25119

PMID: 24443248

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Abstract

To accelerate the acquisition of simultaneously high spatial and angular resolution diffusion imaging. Accelerated imaging is achieved by recovering the diffusion signal at all voxels simultaneously from under-sampled k-q space data using a compressed sensing algorithm. The diffusion signal at each voxel is modeled as a sparse complex Gaussian mixture model. The joint recovery scheme enables incoherent under-sampling of the 5-D k-q space, obtained by randomly skipping interleaves of a multishot variable density spiral trajectory. This sampling and reconstruction strategy is observed to provide considerably improved reconstructions than classical k-q under-sampling and reconstruction schemes. The complex model enables to account for the noise statistics without compromising the computational efficiency and theoretical convergence guarantees. The reconstruction framework also incorporates compensation of motion induced phase errors that result from the multishot acquisition. Reconstructions of the diffusion signal from under-sampled data using the proposed method yields accurate results with errors less that 5% for different accelerations and b-values. The proposed method is also shown to perform better than standard k-q acceleration schemes. The proposed scheme can significantly accelerate the acquisition of high spatial and angular resolution diffusion imaging by accurately reconstructing crossing fiber architectures from under-sampled data.

Diffusion Tensor Imaging - methods

Data Compression - methods

Reproducibility of Results

Algorithms

Brain - anatomy & histology

White Matter - anatomy & histology

Computer Simulation

Humans

Image Interpretation, Computer-Assisted - methods

Sensitivity and Specificity

Image Enhancement - methods

Models, Statistical

Details

Title: Subtitle: Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data
Creators: Merry Mani - Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
Mathews Jacob - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
Arnaud Guidon - Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
Vincent Magnotta - Department of Radiology, University of Iowa, Iowa City, Iowa, USA
Jianhui Zhong - Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.73(1), pp.126-138
DOI: 10.1002/mrm.25119
PMID: 24443248
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Publisher: United States
Grant note: R21 HL109710 / NHLBI NIH HHS
Language: English
Date published: 01/2015
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Psychiatry; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984051516702771

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