A rapid 3D fat–water decomposition method using globally optimal surface estimation (R‐GOOSE)
Year: 2017
Type: article
Abstract: PURPOSE: To improve the graph model of our previous work GOOSE for fat-water decomposition with higher computational efficiency and quantitative accuracy. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Source: Magnetic Resonance in Medicine
DOI: 10.1002/mrm.26843
Authors: Chen Cui, Abhay Shah, Xiaodong Wu, Mathews Jacob
Institution: University of Iowa
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2737399180