Property Estimation of Thin Cortical Bone from Clinical QCT: An Analysis by Synthesis Approach : Going beyond Nyquist

This dissertation addresses the challenge of assessing the thickness and density of thin cortical bone. Due to the limited spatial resolution of Quantitative Computed Tomography (QCT) the Nyquist-Shannon sampling theorem is violated, making direct reconstruction impossible.

To overcome this, a probabilistic Analysis by Synthesis (AbS) framework is proposed that exploits the geometry of the bone and the anisotropy of the CT point spread function. By modeling uncertainties through a bone and measurement model, the AbS optimization is framed as a maximum a posteriori problem, using a Monte Carlo expectation maximization strategy to infer the bone parameters.

The framework is validated on high-resolution µ-CT scans of real vertebrae, demonstrating its ability to accurately estimate cortical bone thickness and density. While there are limitations in mapping the spatial distribution of these parameters, the results are promising and further research offers the potential to refine and improve the method.

Logo KCSS

Rechte

Nutzung und Vervielfältigung:

Keine Lizenz. Es gelten die Bestimmungen des deutschen Urheberrechts (UrhG).

Bitte beachten Sie, dass einzelne Bestandteile der Publikation anderweitigen Lizenz- bzw. urheberrechtlichen Bedingungen unterliegen können.

Zitieren

Zitierform:
Reinhold, S.P., 2024. Property Estimation of Thin Cortical Bone from Clinical QCT: An Analysis by Synthesis Approach: Going beyond Nyquist. Kiel Computer Science Series. Department of Computer Science, Kiel. https://doi.org/10.21941/kcss/2024/5
Zitierform konnte nicht geladen werden.