Purpose: To develop a protocol to non-invasively measure and map fat fraction, fat/(fat + water), as a function of age in the adult thymus for future studies monitoring the effects of interventions aimed at promoting thymic rejuvenation and preservation of immunity in older adults. Materials and methods: Three-dimensional spoiled gradient echo 3T MRI with 3-point Dixon fat-water separation was performed at full inspiration for thymus conspicuity in 36 volunteers 19 to 56 years old. Reproducible breath-holding was facilitated by real-time pressure recording external to the console. The MRI method was validated against localized spectroscopy in vivo, with ECG triggering to compensate for stretching during the cardiac cycle. Fat fractions were corrected for T-1 and T-2 bias using relaxation times measured using inversion recovery-prepared PRESS with incremented echo time. Results: In thymus at 3 T, T-1water = 978 +/- 75 ms, T-1fat = 323 +/- 37 ms, T-2water = 43.4 +/- 9.7 ms and T-2fat = 52.1 +/- 7.6 ms were measured. Mean T-1-corrected MRI fat fractions varied from 0.2 to 0.8 and were positively correlated with age, weight and body mass index (BMI). In subjects with matching MRI and MRS fat fraction measurements, the difference between these measurements exhibited a mean of -0.008 with a 95% confidence interval of (0.123, - 0.138). Conclusions: 3-point Dixon MRI of the thymus with T-1 bias correction produces quantitative fat fraction maps that correlate with T-2-corrected MRS measurements and show age trends consistent with thymic involution.

In a wide variety of biomedical and clinical research studies, sample statistics from diagnostic marker measurements are presented as a means of distinguishing between two populations, such as with and without disease. Intuitively, a larger difference between themean values of a marker for the two populations, and a smaller spread of values within each population, should lead to more reliable classification rules based on this marker. We formalize this intuitive notion by deriving practical, new, closed-form expressions for the sensitivity and specificity of three different discriminant tests defined in terms of the sample means and standard deviations of diagnosticmarkermeasurements. The three discriminant tests evaluated are based, respectively, on the Euclidean distance and theMahalanobis distance between means, and a likelihood ratio analysis. Expressions for the effects of measurement error are also presented. Our final expressions assume that the diagnostic markers follow independent normal distributions for the two populations, although it will be clear that other known distributions may be similarly analyzed. We then discuss applications drawn from the medical literature, although the formalism is clearly not restricted to that application.

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter‐weighted images.

ABSTRACT This work evaluates the ability of quantitative MRI to discriminate between normal and pathological human osteochondral plugs characterized by the Osteoarthritis Research Society International (OARSI) histological system. Normal and osteoarthritic human osteochondral plugs were scored using the OARSI histological system and imaged at 3 T using MRI sequences producing T1 and T2 contrast and measuring T 1 , T 2 , and T 2 * relaxation times, magnetization transfer, and diffusion. The classification accuracies of quantitative MRI parameters and corresponding weighted image intensities were evaluated. Classification models based on the Mahalanobis distance metric for each MRI measurement were trained and validated using leave‐one‐out cross‐validation with plugs grouped according to OARSI histological grade and score. MRI measurements used for classification were performed using a region‐of‐interest analysis which included superficial, deep, and full‐thickness cartilage. The best classifiers based on OARSI grade and score were T 1 ‐ and T 2 ‐weighted image intensities, which yielded accuracies of 0.68 and 0.75, respectively. Classification accuracies using OARSI score‐based group membership were generally higher when compared with grade‐based group membership. MRI‐based classification—either using quantitative MRI parameters or weighted image intensities—is able to detect early osteoarthritic tissue changes as classified by the OARSI histological system. These findings suggest the benefit of incorporating quantitative MRI acquisitions in a comprehensive clinical evaluation of OA. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 33:640–650, 2015.

PurposePrevious work has evaluated the quality of different analytic methods for extracting relaxation times from magnitude imaging data exhibiting Rician noise. However, biexponential analysis of relaxation in tissue, including cartilage, and materials is of increasing interest. We, therefore, analyzed biexponential transverse relaxation decay in the presence of Rician noise and assessed the accuracy and precision of several approaches to determining component fractions and apparent transverse relaxation times.Theory and MethodsComparisons of four different voxel‐by‐voxel fitting methods were performed using Monte Carlo simulations, and phantom and ex vivo bovine nasal cartilage (BNC) experiments. In each case, preclinical and clinical imaging field strengths of 7 Tesla (T) and 3T, respectively, and parameters, were investigated across a range of signal‐to‐noise ratios (SNR). Results were compared with Cramér‐Rao lower bound calculations.ResultsAs expected, at high SNR, all methods performed well. At lower SNR, fits explicitly incorporating the analytic form of the Rician noise maintained performance. The much more efficient correction scheme of Gudbjartsson and Patz performed almost as well in many cases. Ex vivo experiments on phantoms and BNC were consistent with simulation results.ConclusionExplicit incorporation of Rician noise greatly improves accuracy and precision in the analysis of biexponential transverse decay data. Magn Reson Med 73:352–366, 2015. © 2014 Wiley Periodicals, Inc.

Vanessa A. Lukas, Kenneth W. Fishbein, Ping-Chang Lin, Michael Schär, Corey P. Neu, Richard G. Spencer, and David A. Reiter Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States, Department of Radiology, Howard University College of Medicine, Washington, District of Columbia, United States, Philips Healthcare, Highland Heights, Ohio, United States, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, United States, Clinical Research Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States