An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT

TL;DR

This paper introduces a novel analysis-by-synthesis method for accurately modeling cortical bone thickness from clinical QCT scans, significantly improving over standard methods and closely matching high-resolution gold standards.

Contribution

The paper presents a fully automatic, model-based analysis-by-synthesis approach that accurately estimates cortical bone thickness distribution from clinical QCT, overcoming resolution limitations.

Findings

Standard QCT overestimates cortical thickness by 560%.

Proposed method achieves $r^2=0.98$ correlation with gold standard.

Method reduces root mean square error below 10%.

Abstract

Osteoporosis is a skeletal disorder that leads to increased fracture risk due to decreased strength of cortical and trabecular bone. Even with state-of-the-art non-invasive assessment methods there is still a high underdiagnosis rate. Quantitative computed tomography (QCT) permits the selective analysis of cortical bone, however the low spatial resolution of clinical QCT leads to an overestimation of the thickness of cortical bone (Ct.Th) and bone strength. We propose a novel, model based, fully automatic image analysis method that allows accurate spatial modeling of the thickness distribution of cortical bone from clinical QCT. In an analysis-by-synthesis (AbS) fashion a stochastic scan is synthesized from a probabilistic bone model, the optimal model parameters are estimated using a maximum a-posteriori approach. By exploiting the different characteristics of in-plane and out-of-plane point spread functions of CT scanners the proposed method is able assess the spatial distribution of cortical thickness. The method was evaluated on eleven cadaveric human vertebrae, scanned by clinical QCT and analyzed using standard methods and AbS, both compared to high resolution peripheral QCT (HR-pQCT) as gold standard. While standard QCT based measurements overestimated Ct.Th. by 560% and did not show significant correlation with the gold standard ($r^2 = 0.20,\, p = 0.169$) the proposed method eliminated the overestimation and showed a significant tight correlation with the gold standard ($r^2 = 0.98,\, p < 0.0001$) a root mean square error below 10%.