Mr. Sriharsha Marupudi Profile

Sriharsha Marupudi

at US Food and Drug Administration

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Publications (3)

Proceedings Article | 3 April 2024 Poster

Evaluation of a CdTe photon-counting detector in medical imaging: performance metrics and distortion correction approaches

Harsha Marupudi, Joseph Manus, Muhammad Ghani, Andrey Makeev, Stephen Glick, Bahaa Ghammraoui

Proceedings Volume 12925, 129252Z (2024) https://doi.org/10.1117/12.3008625

KEYWORDS: Surface plasmons, Photodetectors, Medical imaging, Distortion, X-rays, X-ray sources, X-ray imaging, X-ray detectors, X-ray computed tomography, Tungsten

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Proceedings Article | 3 April 2024 Presentation

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

Proceedings Volume 12930, 1293005 (2024) https://doi.org/10.1117/12.3009699

KEYWORDS: Bone, Radiomics, Data modeling, 3D microstructuring, 3D modeling, Computed tomography, Random forests, Modeling, Mahalanobis distance, Image resolution

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Proceedings Article | 5 May 2023 Presentation + Paper

Deep learning texture analysis for the assessment of trabecular bone stiffness in CT

Ran Yan, Qian Cao, Sriharsha Marupudi, Ravi Samala, Nicholas Petrick

Proceedings Volume 12468, 1246807 (2023) https://doi.org/10.1117/12.2654474

KEYWORDS: Bone, Radiomics, Education and training, 3D modeling, Performance modeling, Feature extraction, Image segmentation, Data modeling, Deep learning, X-ray computed tomography

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Evaluation of bone fracture risk is important for the diagnosis and treatment of osteoporosis. Bone stiffness is a major factor in determining overall bone strength and fracture risk. With recent improvements in the spatial resolution of CT systems, it is possible to visualize bone microstructure and extract texture features. It is hypothesized that bone texture can be used to improve the assessment of bone strength compared to using bone mineral density (BMD) alone. In this work, we develop image analysis models for bone stiffness estimation utilizing deep learning (DL) features, radiomics features, and gradient structure tensors (GSTs) to estimate trabecular bone stiffness for high-resolution CT. We base our analysis on a dataset containing micro-CT images of 70 individual lumbar vertebrae. Ten trabecular bone ROIs were extracted from each vertebral body and their bone structure was segmented. The mechanical stiffness of each ROI was estimated using micro-finite element (μFE) analysis. Blur and correlated noise derived from clinical high-resolution CT systems were then added to the trabecular bone ROIs to generate simulated high-resolution CT images. A 3D residual network (ResNet) was trained to extract DL features to predict μFE-derived bone stiffness from the simulated CT images. Radiomics and GST features of bone ROIs were also computed for the same task. The prediction results for DL, radiomics, and GST features combined showed the best performance with a root mean square error (RMSE) of 2.646 N/μm and an R2 of 0.881. The performance of DL features alone was better than using BMD alone or using radiomic features alone. Additionally, incorporating orientation information from GST into the models resulted in improved accuracy. We demonstrate that μFEestimated mechanical properties of lumbar vertebral trabecular bone can be inferred from high-resolution CT images and that a combination of DL, radiomic, and GST features provides the highest prediction performance.

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