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.

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.

Context: Brown adipose tissue is of metabolic interest. The tissue is however poorly explored in children.

Methods: Sixty-three 7-year old subjects from the Swedish birth-cohort Halland Health and Growth Study were recruited. Care was taken to include both normal weight and overweight children, but the subjects were otherwise healthy. Only children born full term were included. Water-fat separated whole-body MRI scans, anthropometric measurements, and measurements of fasting glucose and levels of energy homeostasis related hormones, including the insulin-sensitizer osteocalcin, were performed. The fat fraction (FF) and effective transverse relaxation time (T-2(star)) of suspected brown adipose tissue in the cervical-supraclavicular-axillary fat depot (sBAT) and the FFs of abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) were measured. Volumes of sBAT, abdominal VAT and SAT, and thigh muscle volumes were measured.

Results: The FF in the sBAT depot was lower than in VAT and SAT for all children. In linear correlations including sex and age as explanatory variables, sBAT FF correlated positively with all measures of adiposity (p < 0.01), except for VAT FF and weight, positively with sBAT T-2* (p = 0.036), and negatively with osteocalcin (p = 0.017). When adding measures of adiposity as explanatory variables, sBAT FF also correlated negatively with thigh muscle volume (p < 0.01).

Conclusions: Whole-body water-fat MRI of children allows for measurements of sBAT. The FF of sBAT was lower than that of VAT and SAT, indicating presence of BAT. Future studies could confirm whether the observed correlations corresponds to a hormonally active BAT.

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.