Predicting cutaneous features for marker-less optical head-tracking in cranial radiotherapy

In cranial radiotherapy highly accurate tumor localization is required to spare healthy tissue while delivering sufficient dose to the target. Here, marker-less optical tracking can support immobilization devices and monitor head motion. However, recent studies have shown that the spatial registration of the optically generated point cloud to a reference is not robust enough. It may suffer from misalignments due to local similarities and changes along the deformable surface geometry. Irrespective from the registration algorithm, we propose to support the spatial information by tissue thickness patterns across the surface. These patterns are predicted by Gaussian Processes which process near infrared optical backscatter from the skin. We first demonstrate for a cohort of 30 volunteers that the tissue thickness can be determined with errors of less than 0.22mm on average. We found high prediction accuracy irrespective of gender, age or skin type. Second, we show that the robustness of a standard iterative closest point (ICP) algorithm can be improved when exploiting cutaneous patterns to identify point-to-point correspondences. On average, tissue thickness support outperformed the standard procedure for every subject and was capable of pushing 90% of all misalignments exceeding a registration error of 1mm below that threshold.