Automated quantification of three-dimensional subject motion to monitor image quality in high-resolution peripheral quantitative computed tomography

Abstract

Subject motion during acquisition of high-resolution peripheral quantitative computed tomography (HR-pQCT) results in image artifacts and interferes with quantification of bone architecture used to study bone-related diseases such as osteoporosis. We propose an automatic method to measure physical subject motion that frequently takes place during acquisition. Three measures derived from projection data are proposed to quantify motion artifacts: in-plane translation (εT) and in-plane rotation (εR) utilizing projection moments and longitudinal translation (εz) based on tracking projection profiles. Validation was performed using a phantom containing sections of distal human cadaver radii attached to a mechanical device to precisely control in-plane rotation and longitudinal translation that was intentionally performed during HR-pQCT data acquisition. Motion measured by the new automated technique was compared to the known applied motion, and related to percent errors in morphological parameters quantifying bone properties. It was determined that of the three proposed measures, εT best captured a quantified representation of image quality. εT linearly relates to true physical in-plane translational motion (r2 = 0.95, p<0.001) and is independent from longitudinal translational motion as well as the object being scanned. Additionally, εz captures large longitudinal movements and combines well with εT to fully characterize physical motion artifacts. The magnitude of εT corresponds to morphological parameter error and is an excellent basis to select high-quality images. Morphological parameter errors from these experiments confirmed our earlier computer simulations which showed that increased subject motion resulted in artificially higher trabecular number, and artificially lower bone mineral density and cortical thickness. The magnitude and, notably, the uncertainty of the morphological errors increased with increased physical motion, and this impedes a direct linear compensation of parameter errors. The automated method presented provides a basis for consistent and objective quality assurance for HR-pQCT scanning, and addresses an important challenge for this novel imaging modality that is rapidly becoming an important basis for assessment and monitoring of bone quality.