Analysis of feature stability for laser-based determination of tissue thickness

Localisation of the cranium is necessary for accurate stereotactic radiotherapy of malign lesions in the brain. This is achieved by immobilizing the patient’s head (typically by using thermoplastic masks, bite blocks or combinations thereof) and X-ray imaging to determine the actual position of the patient with respect to the treatment device. In previous work we have developed a novel method for marker-less and non-invasive tracking of the skull using a combination of laser-based surface triangulation and the analysis of backscattered feature patterns of a tightly collimated NIR laser beam scanned over the patient’s forehead. An HDR camera is coupled into the beam path of the laser scanning system to acquire one image per projected laser point. We have demonstrated that this setup is capable of accurately determining the tissue thickness for each triangulation point and consequently allows detecting the surface of the cranial bone with sub-millimetre accuracy. Typical clinical settings (treatment times of 15-90min) require feature stability over time, since the determination of tissue thickness is achieved by machine learning methods trained on initial feature scans. We have collected initial scans of the forehead as well as long-term backscatter data (20 images per seconds over 30min) from five subjects and extracted the relevant tissue features from the image streams. Based on the knowledge of the relationship between the tissue feature values and the tissue thickness, the analysis of the long-term data showed that the noise level is low enough to allow robust discrimination of tissue thicknesses of 0.5mm.