# An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

## TL;DR

This paper introduces a model-based method for automatic cortical surface identification in clinical QCT scans, improving accuracy in osteoporosis assessment despite limited spatial resolution.

## Contribution

It develops a statistical bone model and an optimization approach to accurately locate the vertebral cortex in QCT images, demonstrating effectiveness both ex-vivo and in-vivo.

## Key findings

- Accurately identified cortical surface ex-vivo using phantom and cadaveric data.
- Demonstrated in-vivo applicability with manual surface comparison.
- Improved cortical detection despite QCT's limited resolution.

## Abstract

Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution \mu CT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.