Authors:

Sidh Jain

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About Paper:

High-resolution peripheral quantitative computed tomography (HR pQCT) time-lapse imaging is emerging as a non-invasive method for assessing bone remodeling by capturing localized bone turnover, including formation and resorption, over time. However, reproducibility and parameter standardization are essential for broad adoption. In this project, we focused on implementing and validating a robust HR-pQCT image processing pipeline. Using Python-based scripts, we successfully reproduced the time-lapse workflow, which included image registration to align 3D scans across timepoints, density thresholding to segment bone based on intensity values, and morphological filtering to refine and isolate meaningful remodeling regions. We confirmed the integrity of the pipeline by minimizing errors in same-day scan comparisons, consistent with the original study's findings. Key optimizations included the use of grayscale input images to preserve structural detail, 3D registration for precise alignment, and Gaussian smoothing to reduce noise and enhance consistency in detection. With the pipeline now functional, the next phase involves applying this method to diverse research datasets, including studies on osteogenesis imperfecta and other bone pathologies. Ultimately, this work contributes to the development of a reliable, non-invasive biomarker for bone remodeling in both clinical and research settings. Keywords: HR-pQCT; Bone Remodeling; Time-Lapse Imaging; Image Registration; Non-Invasive Biomarkers

Source:

Purdue University / 2025

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Co-authors:

Sidh Jain