Achim Schweikard

Relationship between NIR laser power and the human forehead tissue backscattering image features

A novel markerless near infrared (NIR) laser-based head tracking system was recently proposed to resolve patients head motion problem during cranial radiotherapy. Although previous research showed that we could track the patients head position with the sub-millimetre range accuracy, the tracking performance strongly relied on the accuracy of the tissue thickness estimation. We noticed the factors that inuence the ROI features extracted from the backscattering images were the NIR laser power fluctuation and inconsistency. Therefore, the propose of this paper was to investigate the relationship between these parameters and determine a laser power independent feature transformation. We set up our head tracking system to project the pulsed NIR laser beam onto a single point on subjects forehead and observed the changes of the 5-ROI feature values on the different laser power level. The scatter plots between each ROI feature values and the laser power showed distinctive straight lines with similar slopes while applying linear regression to each scatter plot indicated that the slope of each ROI feature was also in the same range. According to the results, we could transform the data by subtracting the feature value of each ROI from their average slope value and the laser power. This new feature is laser power noise tolerance and could be used to enhance the tissue thickness estimation accuracy.

Feasibility test of line sensors for optical tissue thickness estimation

Purpose Line sensors are cheap, fast and have high quantum effciencies. Here, we investigate whether these sensors can replace an area image sensor for the purpose of tissue thickness measurements. Material and Methods As part of a subject study high dynamic range (HDR) images of three subjects were acquired with an area image sensor. To simulate a line sensor as realistic as possible single or multiple lines were extracted from these HDR images. Thereby, horizontally extracted lines correspond to a parallel orientation of the line sensor relative to the incident angle of a laser beam. Vertically extracted lines correspond to an orthogonal orientation. Then, optical features were determined and converted into a tissue thickness using a machine learning algorithm. Results For the tested subjects the worst root mean square error (RMSE) of the learning process was 0:385 mm. The best RMSE was 0:222 mm. For all subjects, the mean RMSE and the standard deviation of RMSE values decreases with a larger number of extracted lines. The orientation of the line sensor turned out to be important for the RMSE. Vertically oriented line sensors achieve lower RMSEs than horizontally oriented sensors because of the influence of the incident angle. Furthermore, the head-pose of the subject seems to be important for the accuracy. Conclusion Line sensors deliver comparable results to previously analysed area image sensors. Nevertheless, the scattering of the values is higher and the size and orientation of the sensor and the head-pose have an influence on the RMSE of the learning process. Therefore, line sensors are feasible for tissue thickness estimation but they are a trade-off between accuracy and speed.