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Water-fat separation incorporating spatial smoothing is robust to noise
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, Molndal, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Antaros Med, Molndal, Sweden.
2018 (English)In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 50, p. 78-83, article id S0730-725X(18)30040-7Article in journal (Refereed) Published
Abstract [en]

PURPOSE: To develop and evaluate a noise-robust method for reconstruction of water and fat images for spoiled gradient multi-echo sequences.

METHODS: The proposed method performs water-fat separation by using a graph cut to minimize an energy function consisting of unary and binary terms. Spatial smoothing is incorporated to increase robustness to noise. The graph cut can fail to find a solution covering the entire image, in which case the relative weighting of the unary term is iteratively increased until a complete solution is found. The proposed method was compared to two previously published methods. Reconstructions were performed on 16 cases taken from the 2012 ISMRM water-fat reconstruction challenge dataset, for which reference reconstructions were provided. Robustness towards noise was evaluated by reconstructing images with different levels of noise added. The percentage of water-fat swaps were calculated to measure performance.

RESULTS: At low noise levels the proposed method produced similar results to one of the previously published methods, while outperforming the other. The proposed method significantly outperformed both of the previously published methods at moderate and high noise levels.

CONCLUSION: By incorporating spatial smoothing, an increased robustness towards noise is achieved when performing water-fat reconstruction of spoiled gradient multi-echo sequences.

Place, publisher, year, edition, pages
2018. Vol. 50, p. 78-83, article id S0730-725X(18)30040-7
Keywords [en]
Chemical shift imaging, Dixon, Graph cuts, Multi-scale, Quadratic pseudo-Boolean optimization, Water-fat separation
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:uu:diva-347450DOI: 10.1016/j.mri.2018.03.015ISI: 000434750700011PubMedID: 29601865OAI: oai:DiVA.org:uu-347450DiVA, id: diva2:1194466
Funder
Swedish Research Council, 2016-01040Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2019-08-14Bibliographically approved
In thesis
1. Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
Open this publication in new window or tab >>Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Virtually all the magnetic resonance imaging (MRI) signal of a human originates from water and fat molecules. By utilizing the property chemical shift the signal can be separated, creating water- and fat-only images. From these images it is possible to calculate quantitative fat fraction (FF) images, where the value of each voxel is equal to the percentage of its signal originating from fat. In papers I and II methods for water–fat signal separation are presented and evaluated.

The method in paper I utilizes a graph-cut to separate the signal and was designed to perform well even for a low signal-to-noise ratio (SNR). The method was shown to perform as well as previous methods at high SNRs, and better at low SNRs.

The method presented in paper II uses convolutional neural networks to perform the signal separation. The method was shown to perform similarly to a previous method using a graph-cut when provided non-undersampled input data. Furthermore, the method was shown to be able to separate the signal using undersampled data. This may allow for accelerated MRI scans in the future.

Brown adipose tissue (BAT) is a thermogenic organ with the main purpose of expending chemical energy to prevent the body temperature from falling too low. Its energy expending capability makes it a potential target for treating overweight/obesity and metabolic dysfunctions, such as type 2 diabetes. The most well-established way of estimating the metabolic potential of BAT is through measuring glucose uptake using 18F-fludeoxyglucose (18F-FDG) positron emission tomography (PET) during cooling. This technique exposes subjects to potentially harmful ionizing radiation, and alternative methods are desired. One alternative method is measuring the BAT FF using MRI.

In paper III the BAT FF in 7-year olds was shown to be negatively associated with blood serum levels of the bone-specific protein osteocalcin and, after correction for adiposity, thigh muscle volume. This may have implications for how BAT interacts with both bone and muscle tissue.

In paper IV the glucose uptake of BAT during cooling of adult humans was measured using 18F-FDG PET. Additionally, their BAT FF was measured using MRI, and their skin temperature during cooling near a major BAT depot was measured using infrared thermography (IRT). It was found that both the BAT FF and the temperature measured using IRT correlated with the BAT glucose uptake, meaning these measurements could be potential alternatives to 18F-FDG PET in future studies of BAT.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 65
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1589
Keywords
brown adipose tissue, magnetic resonance imaging, water–fat signal separation, graph-cut, positron emission tomography, 18F-fludeoxyglucose, infrared thermography, machine learning, artificial neural networks, deep learning, convolutional neural networks
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Research subject
Radiology
Identifiers
urn:nbn:se:uu:diva-390436 (URN)978-91-513-0718-3 (ISBN)
Public defence
2019-09-13, Enghoffsalen, Entrance 50, Akademiska sjukhuset, Uppsala, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2019-08-23 Created: 2019-08-14 Last updated: 2019-09-17

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Andersson, JonathanAhlström, Håkan

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