CONICET | Buscador de Institutos y Recursos Humanos

PurposeAn adequate understanding of bone structural properties is critical for predicting fragility conditions caused by diseases such as osteoporosis, and in gauging the success of fracture prevention treatments. In this work we aim to develop multi-resolution image analysis techniques to extrapolate high-resolution images predictive power to images taken in clinical conditions.MethodsWe performed multifractal analysis (MFA) on a set of 17 ex-vivo human vertebræ clinical CT scans. The vertebræ failure loads (FFailure) were experimentally measured. We combined Bone Mineral Density (BMD) with different multifractal dimensions, and BMD with multiresolution statistics (e.g., skewness, kurtosis) of MFA curves, to obtain linear models to predictFFailure. Furthermore we obtained short- and long-term precisions from simulated in-vivo scans, using a clinical CT scanner. Ground-truth data|high resolution images-was obtained with a High-Resolution Peripheral Quantitative Computed Tomography (HRpQCT) scanner.ResultsAt the same level of detail, BMD combined with traditional multifractal descriptors (Lipschitz-Holder exponents), and BMD with monofractal features showed similar prediction powers in predicting FFailure(87%, adj. R2). However, at different levels of details, the prediction power of BMD with multifractal features raises to 92% (adj. R2) of FFailure. Our main finding is that a simpler but slightly less accurate model, combining BMD and the skewness of the resultingmultifractal curves, predicts 90% (adj. R2) of FFailure.ConclusionsCompared to monofractal and standard bone measures, multifractal analysis captured key insights in the conditions leading to FFailure. Instead of raw multifractal descriptors, the statistics of multifractal curves can be used in several other contexts, facilitating further research.