Automated Fiducial Localization in CT Images Based on Surface Processing and Geometrical Prior Knowledge for Radiotherapy Applications

Automated Fiducial Localization in CT Images Based on Surface Processing and Geometrical Prior Knowledge for Radiotherapy Applications

Abstract— We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge.< Final Year Projects > The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3 mm and with a true positive rate of 90.1%, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.