Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function.

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal-Wallis test with a Dunn-Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

Gender interactions between vertebral bone mineral density and fat content in the elderly: Assessment using fat-water MRI.

BACKGROUND: Adipose tissue is closely related to bone mass, bone quality, and bone fractures, but the connection between fat and bone is complex and gender-related. Fat-water magnetic resonance imaging (MRI) and MR spectroscopy (MRS) are very useful tools for identifying tissue fat. PURPOSE: To assess gender interactions between bone mineral density (BMD), bone marrow fat, and body mass index (BMI) in the elderly using fat-water MRI and MRS. STUDY TYPE: Prospective/cohort. POPULATION: Sixty-six women and 38 men (mean age, 62.3 years; range, 50-75 years), Asian. FIELD STRENGTH: A 1.5T MR equipped with a body and spine array coil. STEAM MRS and T2 * Dixon were performed. ASSESSMENT: Vertebral bone marrow fat ratio (MFR), BMI, and BMD were measured. Correlations between these variables and differences in bone density in MFR were assessed between participants, divided into three groups based on bone density. STATISTICAL TESTS: Multiple regression; Pearson tests; analysis of covariance; analysis of variance. RESULTS: Multiple regression analysis identified gender, vertebral bone MFR, and BMI as significant predictors of vertebral BMD (P < 0.001). Among the women, vertebral BMD was negatively correlated with vertebral MFR (P = 0.011), but among the men, it was positively correlated with BMI (P = 0.048), although this relationship was confounded by age and MFR. Moreover, vertebral bone marrow fat and BMI were indeed statistically uncorrelated in the elderly (P = 0.357 in women; P = 0.961 in men). DATA CONCLUSION: We found gender interactions between fat and bone in the elderly. Higher bone marrow fat was correlated with lower trabecular BMD in older women but not in men. On the other hand, the positive correlation between BMI and BMD was more pronounced in men than in women. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1382-1389.

Reproducibility and Repeatability of Computer Tomography-based Measurement of Abdominal Subcutaneous and Visceral Adipose Tissues.

Excessive accumulation of abdominal adipose tissue is a widely recognized as a major feature of obesity, and it can be quantified by dual-energy x-ray absorptiometry (DXA). However, in a phantom study, the inter- and intra-instrument reliability of DXA remains unpredictable. Thus, we attempted to determine the precision of estimates from computer tomography-based measurements and analysis with AZE Virtual Place software. To determine the inter-rater reproducibility and intra-rater repeatability of adipose tissue area estimates, we used the automatic boundary-tracing function of the AZE Virtual Place to generate cross-sectional areas of subcutaneous and visceral adipose tissues from the abdomen of reconstructed CT images. The variability of inter-rater and intra-rater estimates expressed as the coefficient of variation ranged from 0.47% to 1.43% for subcutaneous adipose tissue and 1.08% to 2.20% for visceral adipose tissue; the optimal coefficient of variation of the fat rate calculation ranged from 0.55% to 1.13%, respectively. There was high and significant correlation between adipose tissue areas as estimated in 40 obese subjects by two raters or repeatedly on 20 obese subjects by either rater. This indicates excellent reproducibility and repeatability via a computer tomography-based measurement of abdominal subcutaneous and visceral adipose tissues.