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Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks.
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
2019 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594Article in journal (Refereed) Epub ahead of print
Abstract [en]

PURPOSE: To perform and evaluate water-fat signal separation of whole-body gradient echo scans using convolutional neural networks.

METHODS: Whole-body gradient echo scans of 240 subjects, each consisting of 5 bipolar echoes, were used. Reference fat fraction maps were created using a conventional method. Convolutional neural networks, more specifically 2D U-nets, were trained using 5-fold cross-validation with 1 or several echoes as input, using the squared difference between the output and the reference fat fraction maps as the loss function. The outputs of the networks were assessed by the loss function, measured liver fat fractions, and visually. Training was performed using a graphics processing unit (GPU). Inference was performed using the GPU as well as a central processing unit (CPU).

RESULTS: The loss curves indicated convergence, and the final loss of the validation data decreased when using more echoes as input. The liver fat fractions could be estimated using only 1 echo, but results were improved by use of more echoes. Visual assessment found the quality of the outputs of the networks to be similar to the reference even when using only 1 echo, with slight improvements when using more echoes. Training a network took at most 28.6 h. Inference time of a whole-body scan took at most 3.7 s using the GPU and 5.8 min using the CPU.

CONCLUSION: It is possible to perform water-fat signal separation of whole-body gradient echo scans using convolutional neural networks. Separation was possible using only 1 echo, although using more echoes improved the results.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Dixon, convolutional neural network, deep learning, magnetic resonance imaging, neural network, water-fat separation
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-382933DOI: 10.1002/mrm.27786PubMedID: 31033022OAI: oai:DiVA.org:uu-382933DiVA, id: diva2:1314025
Available from: 2019-05-07 Created: 2019-05-07 Last updated: 2019-08-14
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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