A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE)

Chen Cui; Abhay Shah; Xiaodong Wu; Mathews Jacob

doi:10.1002/mrm.26843

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A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE)

Journal article

Peer reviewed

A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE)

Chen Cui, Abhay Shah, Xiaodong Wu and Mathews Jacob

Magnetic resonance in medicine, Vol.79(4), pp.2401-2407

04/2018

DOI: 10.1002/mrm.26843

PMCID: PMC5817637

PMID: 28726301

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Abstract

To improve the graph model of our previous work GOOSE for fat-water decomposition with higher computational efficiency and quantitative accuracy. A modification of the GOOSE fat water decomposition algorithm is introduced while the global convergence guarantees of GOOSE are still inherited to minimize fat-water swaps and phase wraps. In this paper, two non-equidistant graph optimization frameworks are proposed as a single-step framework termed as rapid GOOSE (R-GOOSE), and a multi-step framework termed as multi-scale R-GOOSE (mR-GOOSE). Both frameworks contain considerably less graph connectivity than GOOSE, resulting in a great computation reduction thus making it readily applicable to multidimensional fat water applications. The quantitative accuracy and computational time of the novel frameworks are compared with GOOSE on the 2012 ISMRM Challenge datasets to demonstrate the improvement in performance. Both frameworks accomplish the same level of high accuracy as GOOSE among all datasets. Compared to 100 layers in GOOSE, only 8 layers were used in the new graph model. Computational time is lowered by an order of magnitude to around 5 s for each dataset in (mR-GOOSE), R-GOOSE achieves an average run-time of 8 s. The proposed method provides fat-water decomposition results with a lower run-time and higher accuracy compared to the previously proposed GOOSE algorithm. Magn Reson Med 79:2401-2407, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Water

Magnetic Resonance Imaging

Reproducibility of Results

Algorithms

Image Processing, Computer-Assisted

Humans

Imaging, Three-Dimensional - methods

Adipose Tissue - diagnostic imaging

Software

Pattern Recognition, Automated

Body Water

Details

Title: Subtitle: A rapid 3D fat-water decomposition method using globally optimal surface estimation (R-GOOSE)
Creators: Chen Cui - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA
Abhay Shah - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA
Xiaodong Wu - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA
Mathews Jacob - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, Iowa, USA
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.79(4), pp.2401-2407
DOI: 10.1002/mrm.26843
PMID: 28726301
PMCID: PMC5817637
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Publisher: United States
Grant note: R01 EB019961 / NIBIB NIH HHS
Language: English
Date published: 04/2018
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology; The Iowa Institute for Biomedical Imaging
Record Identifier: 9984070608302771

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