Assessment of proximal femur microarchitecture using ultra-high field MRI at 7 Tesla.

PURPOSE: The purpose of this study was to investigate bone microarchitecture of cadaveric proximal femurs using ultra-high field (UHF) 7-Tesla magnetic resonance imaging (MRI) and to compare the corresponding metrics with failure load assessed during mechanical compression test and areal bone mineral density (ABMD) measured using dual-energy X-ray absorptiometry. MATERIALS AND METHODS: ABMD of ten proximal femurs from five cadavers (5 women; mean age=86.2±3.8 (SD) years; range: 82.5-90 years) were investigated using dual-energy X-ray absorptiometry and the bone volume fraction, trabecular thickness, trabecular spacing, fractal dimension, Euler characteristics, connectivity density and degree of anisotropy of each femur was quantified using UHF MRI. The whole set of specimens underwent mechanical compression tests to failure. The inter-rater reliability of microarchitecture characterization was assessed with the intraclass correlation coefficient (ICC). Associations were searched using correlation tests and multiple regression analysis. RESULTS: The inter-rater reliability for bone microarchitecture parameters measurement was good with ICC ranging from 0.80 and 0.91. ABMD and the whole set of microarchitecture metrics but connectivity density significantly correlated with failure load. Microarchitecture metrics correlated to each other but did not correlate with ABMD. Multiple regression analysis disclosed that the combination of microarchitecture metrics and ABMD improved the association with failure load. CONCLUSION: Femur bone microarchitecture metrics quantified using UHF MRI significantly correlated with biomechanical parameters. The multimodal assessment of ABMD and trabecular bone microarchitecture using UHF MRI provides more information about fracture risk of femoral bone and might be of interest for future investigations of patients with undetected osteoporosis.

[Approaches to segment multiple-sclerosis lesions on conventional brain MRI]. / Revue des approches de segmentation des lésions de sclérose en plaques dans les séquences conventionnelles IRM.

Different sets of criteria are currently used for the diagnosis of multiple sclerosis (MS). Some are based on clinical features, while others are related to imaging findings. Among the image processing systems, specific criteria include spatial dissemination of lesions in one image or their temporal dissemination in images acquired at different time points. In addition, the evolution of the lesion load can be used to evaluate treatment efficiency in MS clinical research. Consequently, obtaining a precise segmentation of the MS lesion appears to be crucial. In the literature, a number of semi-automated or completely automated approaches have been proposed enabling a reduction of the inter- and intra-expert variability for manual delineations. A comprehensive state-of-the-art classification of the most representative systems is presented here.