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Individualisierte Risikoabschätzung von osteoporotischen Wirbelkörperfrakturen mittels Quantitativer CT im Niedrigdosisbereich und in CT-Untersuchungen aus der klinischen Routine

Fachliche Zuordnung Nuklearmedizin, Strahlentherapie, Strahlenbiologie
Förderung Förderung von 2019 bis 2024
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 432290010
 
Erstellungsjahr 2024

Zusammenfassung der Projektergebnisse

Osteoporosis is defined as a skeletal disorder characterized by compromised bone strength predisposing an individual to an increased risk for fracture. Osteoporotic fractures, particularly at the spine and hip, are associated with a reduced quality of life and an increased morbidity and mortality. Thus, osteoporosis is classified as a public health problem in our ageing society. Dual Energy X-ray Absorptiometry (DXA)-based Bone Mineral Density (BMD) measurements and corresponding T-scores are limited in their prediction of fracture risk as BMD/T-score of subjects with versus without osteoporotic fractures overlap. Vertebral BMD assessment and Finite Element Modelling (FEM) in Multi-Detector Computed Tomography (MDCT) exams performed at least as well as DXA-based T-score to predict incident vertebral fractures . The purpose of our research project was twofold: Firstly, we developed iterative image reconstruction techniques and advanced CT acquisition models (e.g. virtual sparse sampling CT and dual-layer spectral CT) which allowed BMD, bone texture analysis, and FEM in ultra-low dose images to predict vertebral-specific fracture risk. Our study results suggested that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to adequately predict FEM-based vertebral bone strength as compared to a standard clinical routine MDCT protocol. Furthermore, our findings indicated that the sparse sampling-based method performs better than the tube current-reduction method in generating images required for FEM-based bone strength prediction models and clinical assessment of reading spinal pathologies. We also demonstrated that spectral-detector based x-ray absorptiometry (SDXA) can differentiate patients with versus without osteoporotic fractures. Thus, SDXA could be a useful tool for opportunistic osteoporosis screening. Furthermore, we investigated the diagnostic accuracy of iodine-corrected vertebral BMD measurements derived from non-dedicated contrast-enhanced phantomless dual-layer spectral CT (DLCT) examinations. Converted BMD derived from contrast-enhanced DLCT examinations and adjusted for individual vessel iodine concentrations showed a high agreement with non-enhanced DLCT-BMD, suggesting that opportunistic BMD measurements are feasible in non-dedicated contrast-enhanced DLCT examinations. Secondly, we developed a fully automated pipeline to use non-dedicated clinical routine MDCT exams for opportunistic osteoporosis screening. A fully automated framework has been developed for MDCT images of the spine by the research group. The framework includes fully automated labelling and segmentation of vertebrae using a convolutional neural network, extraction of trabecular and integral volumetric bone mineral density (vBMD), and MDCT-based areal BMD (aBMD) using asynchronous calibration. BMD measurements using this automated framework showed good agreement with standard QCT-based BMD measurements. Furthermore, opportunistic assessment of vBMD, trabecular bone texture features, and FEM-based vertebral failure load using the framework yields substantial reproducibility. All measures performed significantly better as predictors for vertebral fractures compared to DXA. We established vBMD threshold values at different spinal levels, derived from clinical routine MDCT for the prediction of incident vertebral fractures. No significant difference between vertebral levels was observed and was highest at the thoracolumbar junction. Lastly, we investigated the performance of BMD measurements based on our automated framework in vertebral osteoporotic fracture prediction as compared to fracture prevalence-based prediction models. Vertebral fracture prediction based on automatically extracted vBMD outperformed prediction models based on vertebral fracture status and count. Our studies underline the feasibility and importance of ultra-low dose CT imaging for bone strength prediction in the context of osteoporosis and our developed convolutional neural network framework allows reproducible opportunistic osteoporosis screening in clinical routine MDCT data.

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