KEYWORDS: Bone, Radiomics, Data modeling, 3D microstructuring, 3D modeling, Computed tomography, Random forests, Modeling, Mahalanobis distance, Image resolution
We present a method to generate random synthetic trabecular bone microstructures sufficiently diverse for modeling a dataset of human femur bones. We further demonstrate that using a random forest regressor, we can also generate synthetic bones with prespecified microstructure metric values. This tunability allows for the user to generate synthetic datasets with arbitrary distributions of microstructure metrics that can be useful for modeling trabecular bone in other anatomical sites or disease states. Virtual imaging studies can be applied to simulate high resolution CT image data and used for developing new texture-based models for the evaluation of bone health.
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