Activated brown adipose tissue (BAT) enhances lipid catabolism and improves cardiometabolic health. Quantitative MRI of the fat fraction (FF) of supraclavicular BAT (scBAT) is a promising noninvasive measure to assess BAT activity but suffers from high scan variability. We aimed to test the effects of coregistration and mutual thresholding on the scan variability in a fast (1 min) time-resolution MRI protocol for assessing scBAT FF changes during cold exposure.Ten volunteers (age 24.8 ± 3.0 years; body mass index 21.2 ± 2.1 kg/m2 ) were scanned during thermoneutrality (32°C; 10 min) and mild cold exposure (18°C; 60 min) using a 12-point gradient-echo sequence (70 consecutive scans with breath-holds, 1.03 min per dynamic). Dynamics were coregistered to the first thermoneutral scan, which enabled drawing of single regions of interest in the scBAT depot. Voxel-wise FF changes were calculated at each time point and averaged across regions of interest. We applied mutual FF thresholding, in which voxels were included if their FF was greater than 30% FF in the reference scan and the registered dynamic. The efficacy of the coregistration was determined by using a moving average and comparing the mean squared error of residuals between registered and nonregistered data. Registered scBAT ΔFF was compared with single-scan thresholding using the moving average method.Registered scBAT ΔFF had lower mean square error values than nonregistered data (0.07 ± 0.05% vs. 0.16 ± 0.14%; p < 0.05), and mutual thresholding reduced the scBAT ΔFF variability by 30%.We demonstrate that coregistration and mutual thresholding improve stability of the data 2-fold, enabling assessment of small changes in FF following cold exposure.
Background: Natural history data are essential for trial design in Duchenne (DMD) and Becker muscular dystrophy (BMD), but recruitment for observational studies can be challenging. Objective: We reviewed reasons why patients or caregivers declined participation, and compared characteristics of participants and non-participants to assess possible selection bias in four observational studies, three on DMD and one on BMD. Methods: Three pediatric DMD studies focused on cross-sectional cognitive function and brain MRI (DMDbrain, n = 35 and DMDperfusion, n = 12), and on longitudinal upper extremity function and muscle MRI (DMDarm, n = 22). One adult BMD study assessed longitudinal functioning (n = 36). Considerations for non-participation were retrospectively reviewed from screening logs. Age, travel-time, DMD gene mutations and age at loss of ambulation (DMDarm and BMD study only), of participants and non-participants were derived from the Dutch Dystrophinopathy Database and compared using nonparametric tests (p < 0.05). Results: The perceived burden of the protocol (38.2%), use of MRI (30.4%), and travel-time to the study site (19.1%) were the most frequently reported considerations for non-participation. Only few patients reported lack of personal gain (0.0– 5.9%). Overall, participating patients were representative for the studied sub-populations, except for a younger age of DMDarm study participants and a complete lack of participants with a mutation beyond exon 63. Conclusion: Optimizing patient involvement in protocol design, improving MRI experiences, and integrating research into clinics are important factors to decrease burden and facilitate participation. Nationwide registries are essential to compare participants and non-participants and ensure representative observational research. Specific effort is needed to include patients with distal mutations in cognitive studies.
We read with interest the paper by Bianchi et al,[1][1] in which the authors report on the treatment monitoring of 2 patients with guanidinoacetate methyltransferase (GAMT) deficiency and 3 patients with an arginine:glycine amidinotransferase defect (AGAT-d). Repetitive MR measurements of these
Facioscapulohumeral muscular dystrophy (FSHD) is an untreatable disease, characterized by asymmetric progressive weakness of skeletal muscle with fatty infiltration. Although the main genetic defect has been uncovered, the downstream mechanisms causing FSHD are not understood. The objective of this study was to determine natural disease state and progression in muscles of FSHD patients and to establish diagnostic biomarkers by quantitative MRI of fat infiltration and phosphorylated metabolites. MRI was performed at 3T with dedicated coils on legs of 41 patients (28 men/13 women, age 34-76 years), of which eleven were re-examined after four months of usual care. Muscular fat fraction was determined with multi spin-echo and T1 weighted MRI, edema by TIRM and phosphorylated metabolites by 3D (31)P MR spectroscopic imaging. Fat fractions were compared to clinical severity, muscle force, age, edema and phosphocreatine (PCr)/ATP. Longitudinal intramuscular fat fraction variation was analyzed by linear regression. Increased intramuscular fat correlated with age (p<0.05), FSHD severity score (p<0.0001), inversely with muscle strength (p<0.0001), and also occurred sub-clinically. Muscles were nearly dichotomously divided in those with high and with low fat fraction, with only 13% having an intermediate fat fraction. The intramuscular fat fraction along the muscle's length, increased from proximal to distal. This fat gradient was the steepest for intermediate fat infiltrated muscles (0.07±0.01/cm, p<0.001). Leg muscles in this intermediate phase showed a decreased PCr/ATP (p<0.05) and the fastest increase in fatty infiltration over time (0.18±0.15/year, p<0.001), which correlated with initial edema (p<0.01), if present. Thus, in the MR assessment of fat infiltration as biomarker for diseased muscles, the intramuscular fat distribution needs to be taken into account. Our results indicate that healthy individual leg muscles become diseased by entering a progressive phase with distal fat infiltration and altered energy metabolite levels. Fat replacement then relatively rapidly spreads over the whole muscle.
We read with interest the paper by Bianchi et al,[1][1] in which the authors report on the treatment monitoring of 2 patients with guanidinoacetate methyltransferase (GAMT) deficiency and 3 patients with an arginine:glycine amidinotransferase defect (AGAT-d). Repetitive MR measurements of these
Families predisposed to longevity show enhanced glucose tolerance and skeletal muscle insulin sensitivity compared with controls, independent of body composition and physical activity. Intramyocellular lipid (IMCL) accumulation in skeletal muscle has been associated with insulin resistance. Here, we assessed whether subjects enriched for familial longevity have lower IMCL levels. We determined IMCL levels in 48 subjects from the Leiden Longevity Study, comprising 24 offspring of nonagenarian siblings and 24 partners thereof as control subjects. IMCL levels were assessed noninvasively using short echo time proton magnetic resonance spectroscopy ((1)H-MRS) of the tibialis anterior muscle with a 7 Tesla human MR scanner. IMCL levels were calculated relative to the total creatine (tCr) CH3 signal. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). After correction for age, sex, BMI, and physical activity, offspring of long-lived nonagenarian siblings tended to show lower IMCL levels compared with controls (IMCL/tCr: 3.1 ± 0.5 vs. 4.5 ± 0.5, respectively, P = 0.051). In a pairwise comparison, this difference reached statistical significance (P = 0.038). We conclude that offspring of nonagenarian siblings predisposed to longevity show lower IMCL levels compared with environmentally matched control subjects. Future research should focus on assessing what mechanisms may explain the lower IMCL levels in familial longevity.
An open-source, federated-learning-based segmentation software termed Dafne (Deep Anatomical Federated Network) is presented. This software continuously adapts the deep learning models used for the segmentation (currently for the muscles of the leg and thigh) based on the input of the users, who are in multiple institutions. This software was validated through data usage statistics of more than 50 users and through a retrospective study on 38 datasets of patients with suspected myositis, showing that the continuous learning approach is able to improve and generalize the performance of the original models.
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