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Multi-scale graph-cut algorithm for efficient water-fat separation
Karolinska Univ Hosp, Dept Med Radiat Phys, P9 02, SE-17176 Stockholm, Sweden.;Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden..
Department of Radiology, Uppsala University Hospital; Umea Univ, Dept Radiat Sci, Umea, Sweden..
2017 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 3, 941-949 p.Article in journal (Refereed) Published
Abstract [en]

Purpose

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B-0-correction.

Methods

A previously proposed water-fat separation algorithm that corrects for B-0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation.

Results

Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time.

Conclusion

The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017.

Place, publisher, year, edition, pages
2017. Vol. 78, no 3, 941-949 p.
Keyword [en]
chemical shift imaging, Dixon, fat suppression, graph cuts, multi-scale processing, water-fat separation
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:uu:diva-333747DOI: 10.1002/mrm.26479ISI: 000407855700013OAI: oai:DiVA.org:uu-333747DiVA: diva2:1157807
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2017-11-16Bibliographically approved

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