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Estimating the cold-induced brown adipose tissue glucose uptake rate measured by 18F-FDG PET using infrared thermography and water-fat separated MRI
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.ORCID-id: 0000-0001-6477-2331
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
GE Healthcare.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
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2019 (Engelska)Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikel-id 12358Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Brown adipose tissue (BAT) expends chemical energy to produce heat, which makes it a potential therapeutic target for combating metabolic dysfunction and overweight/obesity by increasing its metabolic activity. The most well-established method for measuring BAT metabolic activity is glucose uptake rate (GUR) measured using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). However, this is expensive and exposes the subjects to potentially harmful radiation. Cheaper and safer methods are warranted for large-scale or longitudinal studies. Potential alternatives include infrared thermography (IRT) and magnetic resonance imaging (MRI). The aim of this study was to evaluate and further develop these techniques. Twelve healthy adult subjects were studied. The BAT GUR was measured using 18F-FDG PET during individualized cooling. The temperatures of the supraclavicular fossae and a control region were measured using IRT during a simple cooling protocol. The fat fraction and effective transverse relaxation rate of BAT were measured using MRI without any cooling intervention. Simple and multiple linear regressions were employed to evaluate how well the MRI and IRT measurements could estimate the GUR. Results showed that both IRT and MRI measurements correlated with the GUR. This suggest that these measurements may be suitable for estimating the cold-induced BAT GUR in future studies.

Ort, förlag, år, upplaga, sidor
2019. Vol. 9, artikel-id 12358
Nyckelord [en]
brown adipose tissue, 18F-FDG positron emission tomography, infrared thermography, magnetic resonance imagingm PET/MRI, water–fat signal separation
Nationell ämneskategori
Radiologi och bildbehandling
Forskningsämne
Radiologi
Identifikatorer
URN: urn:nbn:se:uu:diva-390410DOI: 10.1038/s41598-019-48879-7ISI: 000482564800014PubMedID: 31451711OAI: oai:DiVA.org:uu-390410DiVA, id: diva2:1341693
Forskningsfinansiär
Vetenskapsrådet, 2016-01040Hjärt-Lungfonden, 2170492EXODIAB - Excellence of Diabetes Research in SwedenTillgänglig från: 2019-08-09 Skapad: 2019-08-09 Senast uppdaterad: 2019-10-18Bibliografiskt granskad
Ingår i avhandling
1. Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
Öppna denna publikation i ny flik eller fönster >>Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 65
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1589
Nyckelord
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
Nationell ämneskategori
Radiologi och bildbehandling Medicinsk bildbehandling
Forskningsämne
Radiologi
Identifikatorer
urn:nbn:se:uu:diva-390436 (URN)978-91-513-0718-3 (ISBN)
Disputation
2019-09-13, Enghoffsalen, Entrance 50, Akademiska sjukhuset, Uppsala, 13:15 (Svenska)
Opponent
Handledare
Tillgänglig från: 2019-08-23 Skapad: 2019-08-14 Senast uppdaterad: 2019-09-17

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