An in silico generative trabecular bone model for radiomic analysis of bone health

Qian Cao; Gengxin Shi; Andrew Wang; Sriharsha Marupudi; Ravi Samala; Wojciech Zbijewski; Nicholas Petrick

doi:10.1117/12.3009699

3 April 2024 An in silico generative trabecular bone model for radiomic analysis of bone health

Qian Cao, Gengxin Shi, Andrew Wang, Sriharsha Marupudi, Ravi Samala, Wojciech Zbijewski, Nicholas Petrick

Author Affiliations +

Proceedings Volume 12930, Medical Imaging 2024: Clinical and Biomedical Imaging; 1293005 (2024) https://doi.org/10.1117/12.3009699
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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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Citation Download Citation

Qian Cao, Gengxin Shi, Andrew Wang, Sriharsha Marupudi, Ravi Samala, Wojciech Zbijewski, and Nicholas Petrick "An in silico generative trabecular bone model for radiomic analysis of bone health", Proc. SPIE 12930, Medical Imaging 2024: Clinical and Biomedical Imaging, 1293005 (3 April 2024); https://doi.org/10.1117/12.3009699

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