Background: The underlying pathophysiological mechanisms of chronic Whiplash Associated Disorders (WAD) are not fully understood. More knowledge of morphology is needed to better understand the disorder, improve diagnostics and treatments. The aim was to investigate dorsal neck muscle volume (MV) and muscle fat infiltration (MFI) in relation to self-reported neck disability among 30 participants with chronic WAD grade II-III compared to 30 matched healthy controls.

Methods: MV and MFI at spinal segments C4 through C7 in both sexes with mild- to moderate chronic WAD (n = 20), severe chronic WAD (n = 10), and age- and sex matched healthy controls (n = 30) was compared. Muscles: trapezius, splenius, semispinalis capitis and semispinalis cervicis were segmented by a blinded assessor and analyzed.

Results: Higher MFI was found in right trapezius (p = 0.007, Cohen’s d = 0.9) among participants with severe chronic WAD compared to healthy controls. No other significant difference was found for MFI (p = 0.22–0.95) or MV (p = 0.20–0.76).

Conclusions: There are quantifiable changes in muscle composition of right trapezius on the side of dominant pain and/or symptoms, among participants with severe chronic WAD. No other statistically significant differences were shown for MFI or MV. These findings add knowledge of the association between MFI, muscle size and self-reported neck disability in chronic WAD.

Objectives: We examined the longitudinal and cross- sectional relationship between automated MRI-analysis and single-slice axial CT imaging for determining muscle size and muscle fat infiltration (MFI) of the anterior thigh.

Methods: Twenty-two patients completing sex-hormone treatment expected to result in muscle hypertrophy (n = 12) and atrophy (n = 10) underwent MRI scans using 2-point Dixon fat/water-separated sequences and CT scans using a system operating at 120 kV and a fixed flux of 100 mA. At baseline and 12 months after, auto- mated volumetric MRI analysis of the anterior thigh was performed bilaterally, and fat-free muscle volume and MFI were computed. In addition, cross-sectional area (CSA) and radiological attenuation (RA) (as a marker of fat infiltration) were calculated from single slice axial CT-images using threshold-assisted planimetry. Linear regression models were used to convert units.

Results: There was a strong correlation between MRI- derived fat-free muscle volume and CT-derived CSA (R = 0.91), and between MRI-derived MFI and CT-derived RA (R = −0.81). The 95% limits of agreement were ±0.32 L for muscle volume and ±1.3% units for %MFI. The longi- tudinal change in muscle size and MFI was comparable across imaging modalities.

Conclusions: Both automated MRI and single-slice CT-imaging can be used to reliably quantify anterior thigh muscle size and MFI.

Advances in knowledge: This is the first study examining the intermodal agreement between automated MRI anal- ysis and CT-image assessment of muscle size and MFI in the anterior thigh muscles. Our results support that both CT- and MRI-derived measures of muscle size and MFI can be used in clinical settings.

Objective: To evaluate the cardiometabolic outcomes associated with discordant visceral adipose tissue (VAT) and liver fat (LF) phenotypes in 2 cohorts.

Patients and Methods: Participants in the Dallas Heart Study underwent baseline imaging from January 1, 2000, through December 31, 2002, and were followed for incident cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) through 2013. Associations between VAT-LF groups (low-low, high-low, low-high, and high-high) and outcomes were assessed using multivariable- adjusted regression and were replicated in the independent UK Biobank.

Results: The Dallas Heart Study included 2064 participants (mean SD age, 449 years; 54% female; 47% black). High VATehigh LF and high VATelow LF were associated with prevalent atheroscle- rosis, whereas low VATehigh LF was not. Of 1731 participants without CVD/T2DM, 128 (7.4%) developed CVD and 95 (5.5%) T2DM over a median of 12 years. High VATehigh LF and high VATelow LF were associated with increased risk of CVD (hazard ratios [HRs], 2.0 [95% CI, 1.3 to 3.2] and 2.4 [95% CI, 1.4 to 4.1], respectively) and T2DM (odds ratios [ORs], 7.8 [95% CI, 3.8 to 15.8] and 3.3 [95% CI, 1.4 to 7.8], respectively), whereas low VATehigh LF was associated with T2DM (OR, 2.7 [95% CI, 1.1 to 6.7]). In the UK Biobank (N1⁄422,354; April 2014-May 2020), only high VATelow LF remained associated with CVD after multivariable adjustment for age and body mass index (HR, 1.5 [95% CI, 1.2 to 1.9]).

Conclusion: Although VAT and LF are each associated with cardiometabolic risk, these observations demonstrate the importance of separating their cardiometabolic implications when there is presence or absence of either or both in an individual.

Magnetic resonance imaging (MRI) is considered being state-of-the-art technology for body composition analysis. Compared to other indirect techniques such as scales, calipers, bioimpedance, and dual-energy X-ray absorptiometry (DXA), MRI offers direct and precise measurements of the volumes of different tissue compartments and also enables quantification of diffuse fat infiltration in organs. Here, we describe a protocol for acquiring of fat–water-separated MRI data and the image postprocessing required for the quantification of several body composition biomarkers relevant for metabolic research. This protocol has successfully been used in several clinical studies and also in the large UK Biobank population study.

Investigation of the effect on accuracy and precision of different parameter settings is important for quantitative Magnetic Resonance Imaging. The purpose of this study was to investigate T1-bias and precision for muscle fat infiltration (MFI) using fat-referenced chemical shift magnetic resonance imaging at 5° and 10° flip angle. This [MB1] experimental study was done on forty postmenopausal women using 3T MRI test and retest images using 4-point 3D spoiled gradient multi-echo acquisition including real and imaginary images for reconstruction acquired at Flip angles 5° and 10°. Post-processing included T2* correction and fat-referenced calibration of the fat signal. The mean MFI was calculated in six different automatically segmented muscle regions using both the fat-referenced fat signal and the fat fraction calculated from the fat and water image pair for each acquisition. The variance of the difference between mean MFI from test and retest was used as measure of precision. The SNR characteristics were analyzed by measuring difference of the full width half maximum of the fat signal distribution using Student’s t-test.There was no difference in the mean fat-referenced MFI at different flip angles with the fat-referenced technique, which was the case using the fat fraction. No significant difference in the precision was found in any of the muscles analyzed. However, the full width half maximum of the fat signal distribution was significantly lower at 10° flip angle compared to 5°. Fat-referenced MFI is insensitive to T1 bias in chemical shift magnetic resonance imaging enabling usage of a higher and more SNR effective flip angle. The lower full-width-at half-maximum in fat-referenced MFI at 10° indicates that high flip angle acquisition is advantageous although no significant differences in precision was observed comparing 5° and 10°.

Objective: This study performed individual-centric, data-driven calculations of propensity for coronary heart disease (CHD) and type 2 diabetes (T2D), utilizing magnetic resonance imaging-acquired body composition measurements, for sub-phenotyping of obesity and nonalcoholic fatty liver disease (NAFLD).Methods: A total of 10,019 participants from the UK Biobank imaging substudy were included and analyzed for visceral and abdominal subcutaneous adipose tissue, muscle fat infiltration, and liver fat. An adaption of the k-nearest neighbors algorithm was applied to the imaging variable space to calculate individualized CHD and T2D propensity and explore metabolic sub-phenotyping within obesity and NAFLD.

Results: The ranges of CHD and T2D propensity for the whole cohort were 1.3% to 58.0% and 0.6% to 42.0%, respectively. The diagnostic performance, area under the receiver operating characteristic curve (95% CI), using disease propensities for CHD and T2D detection was 0.75 (0.73-0.77) and 0.79 (0.77-0.81). Exploring individualized disease propensity, CHD phenotypes, T2D phenotypes, comorbid phenotypes, and metabolically healthy phenotypes were found within obesity and NAFLD.

Conclusions: The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individual’s metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and conse- quently enable optimization of treatment.

The objective of this study was to investigate the relationship between fat infiltration in the cervical multifidi and fat infiltration measured in the lower extremities to move further into understanding the complex signs and symptoms arising from a whiplash trauma. Thirty-one individuals with chronic whiplash associated disorders, stratified into a mild/moderate group and a severe group, together with 31 age- and gender matched controls were enrolled in this study. Magnetic resonance imaging was used to acquire a 3D volume of the neck and of the whole-body. Cervical multifidi was used to represent muscles local to the whiplash trauma and all muscles below the hip joint, the lower extremities, were representing widespread muscles distal to the site of the trauma. The fat infiltration was determined by fat fraction in the segmented images. There was a linear correlation between local and distal muscle fat infiltration (p<0.001, r2 = 0.28). The correlation remained significant when adjusting for age and WAD group (p = 0.009) as well as when correcting for age, WAD group and BMI (p = 0.002). There was a correlation between local and distal muscle fat infiltration within the severe WAD group (p = 0.0016, r2 = 0.69) and in the healthy group (p = 0.022, r2 = 0.17) but not in the mild/moderate group (p = 0.29, r2 = 0.06). No significant differences (p = 0.11) in the lower extremities' MFI between the different groups were found. The absence of differences between the groups in terms of lower extremities' muscle fat infiltration indicates that, in this particular population, the whiplash trauma has a local effect on muscle fat infiltration rather than a generalized.

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative magnetic resonance imaging (MRI). Earlier published studies of this method are summarized, and a previously un-published validation study, based on 4.753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy x-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRI show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 % and 4.6 % for fat (computed from AT) and lean tissue respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of more than 20 %. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat in combination with rapid scanning protocols and efficient image analysis tools make quantitative MRI a powerful tool for advanced body composition assessment. 

Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.

MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.