Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA

Satyananda Kashyap; Zhili Yang; Mathews Jacob

doi:10.1016/j.mri.2010.08.017

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Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA

Journal article

Peer reviewed

Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA

Satyananda Kashyap, Zhili Yang and Mathews Jacob

Magnetic resonance imaging, Vol.29(2), pp.222-229

2011

DOI: 10.1016/j.mri.2010.08.017

PMID: 21144688

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Abstract

We introduce a novel noniterative algorithm for the fast and accurate reconstruction of nonuniformly sampled MRI data. The proposed scheme derives the reconstructed image as the nonuniform inverse Fourier transform of a compensated dataset. We derive each sample in the compensated dataset as a weighted linear combination of a few measured k-space samples. The specific k-space samples and the weights involved in the linear combination are derived such that the reconstruction error is minimized. The computational complexity of the proposed scheme is comparable to that of gridding. At the same time, it provides significantly improved accuracy and is considerably more robust to noise and undersampling. The advantages of the proposed scheme makes it ideally suited for the fast reconstruction of large multidimensional datasets, which routinely arise in applications such as f-MRI and MR spectroscopy. The comparisons with state-of-the-art algorithms on numerical phantoms and MRI data clearly demonstrate the performance improvement.

Matching pursuits

Magnetic resonance imaging

Tikhonov regularization

Non-Cartesian sampling

Details

Title: Subtitle: Non-Iterative Regularized reconstruction Algorithm for Non-CartesiAn MRI: NIRVANA
Creators: Satyananda Kashyap - Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
Zhili Yang - Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
Mathews Jacob - Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
Resource Type: Journal article
Publication Details: Magnetic resonance imaging, Vol.29(2), pp.222-229
DOI: 10.1016/j.mri.2010.08.017
PMID: 21144688
NLM abbreviation: Magn Reson Imaging
ISSN: 0730-725X
eISSN: 1873-5894
Publisher: Elsevier Inc
Language: English
Date published: 2011
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
Record Identifier: 9984070521602771

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