A method is presented for rapid simultaneous quantification of the longitudinal T 1 relaxation, the transverse T 2 relaxation, the proton density (PD), and the amplitude of the local radio frequency B 1 field. All four parameters are measured in one single scan by means of a multislice, multiecho, and multidelay acquisition. It is based on a previously reported method, which was substantially improved for routine clinical usage. The improvements comprise of the use of a multislice spin-echo technique, a background phase correction, and a spin system simulation to compensate for the slice-selective RF pulse profile effects. The aim of the optimization was to achieve the optimal result for the quantification of magnetic resonance parameters within a clinically acceptable time. One benchmark was high-resolution coverage of the brain within 5 min. In this scan time the measured intersubject standard deviation (SD) in a group of volunteers was 2% to 8%, depending on the tissue (voxel size ‫؍‬ 0.8 ؋ 0.8 ؋ 5 mm). As an example, the method was applied to a patient with multiple sclerosis in whom the diseased tissue could clearly be distinguished from healthy reference values. Additionally it was shown that, using the approach of synthetic MRI, both accurate conventional contrast images as well as quantification maps can be generated based on the same scan. Tissues in the human body can be distinguished with magnetic resonance imaging (MRI) depending on their MR parameters, such as the longitudinal T 1 relaxation, the transverse T 2 relaxation, and the proton density (PD). In clinical routine, the MR scanner settings, such as echo time (T E ), repetition time (T R ), and flip angle (␣), are most often chosen to highlight, or saturate, the image intensity of tissues, resulting in T 1 -weighting or T 2 -weighting in a contrast image. These procedures are well-established and relatively quick. A major disadvantage of using such contrast images is that the absolute intensity has no direct meaning and diagnosis relies on comparison with surrounding tissues in the image. In many cases it is therefore necessary to perform several different contrast scans. A more direct approach is the absolute quantification of the tissue parameters T 1 , T 2 , and PD. In this case, pathology can be examined on a pixel basis to establish the absolute deviation compared to the normal values. Automatic segmentation of such tissue images would be straightforward and the progress of the disease could then be expressed in absolute numbers. An excellent overview of the use of absolute quantification on neurodegenerative diseases is provided in Ref. 1.Although the advantages of absolute quantification are obvious, its clinical use is still limited. At least two major hurdles need to be addressed to stimulate widespread clinical usage. For many methods, the excessive scan time associated with the measurement of the three parameters has so far prohibited its clinical application. However, in recent years there has been substantial progress (see, e.g., R...

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 MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 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 MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. 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, makes quantitative MRI a powerful tool for advanced body composition assessment.

ObjectiveThis study aimed to investigate the value of imaging‐based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels.MethodsThe first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modeling.ResultsMatched control analysis showed that higher VAT and MFI were associated with CHD and T2D (P < 0.001). Higher liver fat was associated with T2D (P < 0.001) and lower liver fat with CHD (P < 0.05), matching on VAT. Multivariable modeling showed that lower VAT and MFI were associated with metabolic health (P < 0.001), and liver fat was nonsignificant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity.ConclusionsBody composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.

IntroductionQuantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects.Materials and Methods3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics.ResultsOf the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view.Discussion and ConclusionsIn conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource.

• A method for segmenting the brain and estimating tissue volume is presented • This method measures white matter, grey matter, cerebrospinal fluid and remaining tissue • The method calculates tissue fractions in voxel, thus accounting for partial volume • Repeatability was 2.2% for total brain volume with imaging resolution <2.0 mm.

Study Design Cross-sectional study. Background Findings of fat infiltration in cervical spine multifidus, as a sign of degenerative morphometric changes due to whiplash injury, need to be verified. Objectives To develop a method using water/fat magnetic resonance imaging (MRI) to investigate fat infiltration and cross-sectional area of multifidus muscle in individuals with whiplash-associated disorders (WADs) compared to healthy controls. Methods Fat infiltration and cross-sectional area in the multifidus muscles spanning the C4 to C7 segmental levels were investigated by manual segmentation using water/fat-separated MRI in 31 participants with WAD and 31 controls, matched for age and sex. Results Based on average values for data spanning C4 to C7, participants with severe disability related to WAD had 38% greater muscular fat infiltration compared to healthy controls (P = .03) and 45% greater fat infiltration compared to those with mild to moderate disability related to WAD (P = .02). There were no significant differences between those with mild to moderate disability and healthy controls. No significant differences between groups were found for multifidus cross-sectional area. Significant differences were observed for both cross-sectional area and fat infiltration between segmental levels. Conclusion Participants with severe disability after a whiplash injury had higher fat infiltration in the multifidus compared to controls and to those with mild/moderate disability secondary to WAD. Earlier reported findings using T1-weighted MRI were reproduced using refined imaging technology. The results of the study also indicate a risk when segmenting single cross-sectional slices, as both cross-sectional area and fat infiltration differ between cervical levels. J Orthop Sports Phys Ther 2016;46(10):886-893. Epub 2 Sep 2016. doi:10.2519/jospt.2016.6553.

Purpose There is an absence of reproducibility studies on MRI‐based body composition analysis in current literature. Therefore, the aim of this study was to investigate the between‐scanner reproducibility and the repeatability of a method for MRI‐based body composition analysis. Methods Eighteen healthy volunteers of varying body mass index and adiposity were each scanned twice on five different 1.5T and 3T scanners from three different vendors. Two‐point Dixon neck‐to knee images and two additional liver scans were acquired with similar protocols. Visceral adipose tissue (VAT) volume, abdominal subcutaneous adipose tissue (ASAT) volume, thigh muscle volume, and muscle fat infiltration (MFI) in the thigh muscle were measured. Liver proton density fat fraction (PDFF) was assessed using two different methods, the scanner vendor's 6‐point method and an in‐house 2‐point method. Within‐scanner test‐retest repeatability and between‐scanner reproducibility were calculated using analysis of variance. Results Repeatability coefficients were 13 centiliters (cl) (VAT), 24 cl (ASAT), 17 cl (total thigh muscle volume), 0.53% (MFI), and 1.27‐1.37% for liver PDFF. Reproducibility coefficients were 24 cl (VAT), 42 cl (ASAT), 31 cl (total thigh muscle volume), 1.44% (MFI), and 2.37‐2.40% for liver PDFF. Conclusion For all measures except MFI, the within‐scanner repeatability explained much of the overall reproducibility. The two methods for measuring liver fat had similar reproducibility. This study showed that the investigated method eliminates effects due to scanner differences. The results can be used for power calculations in clinical studies or to better understand the scanner‐induced variability in clinical applications.

Introduction: Magnetic Resonance Imaging is a sensitive technique for detecting white matter (WM) MS lesions, but the relation with clinical disability is low. Because of this, changes in both ‘normal appearing white matter’ (NAWM) and ‘diffusely abnormal white matter’ (DAWM) have been of interest in recent years. MR techniques, including quantitative magnetic resonance imaging (qMRI) and quantitative magnetic resonance spectroscopy (qMRS), have been developed in order to detect and quantify such changes. In this study, qMRI and qMRS were used to investigate NAWM and DAWM in typical MS patients and in MS patients with low number of WM lesions. Patient data were compared to ‘normal white matter’ (NWM) in healthy controls. Methods: QMRI and qMRS measurements were performed on a 1.5 T Philips MR-scanner. 35 patients with clinically definite MS and 20 healthy controls were included. Twenty of the patients fulfilled the ‘Barkhof-Tintoré criteria’ for MS, (‘MRIpos’), whereas 15 showed radiologically atypical findings with few WM lesions (‘MRIneg’). QMRI properties were determined in ROIs of NAWM, DAWM and lesions in the MS groups and of NWM in controls. Descriptive statistical analysis and comparisons were performed. Correlations were calculated between qMRI measurements and (1) clinical parameters and (2) WM metabolite concentrations. Regression analyses were performed with brain parenchyma fraction and MSSS. Results: NAWM in the MRIneg group was significantly different from NAWM in the MRIpos group and NWM. In addition, R1 and R2 of NAWM in the MRIpos group correlated negatively with EDSS and MSSS. DAWM was significantly different from NWM, but similar in the MS groups. N-acetyl aspartate correlated negatively with R1 and R2 in MRIneg. R2 of DAWM was associated with BPF. Conclusions: Changes in NAWM and DAWM are independent pathological entities in the disease. The correlation between qMRI and clinical status may shed new light on the clinicoradiological paradox.