Estimating soft tissue thickness from light-tissue interactions––a simulation study

Abstract

Immobilization and marker-based motion tracking in radiation therapy often cause decreased patient comfort. However, the more comfortable alternative of optical surface tracking is highly inaccurate due to missing point-to-point correspondences between subsequent point clouds as well as elastic deformation of soft tissue. In this study, we present a proof of concept for measuring subcutaneous features with a laser scanner setup focusing on the skin thickness as additional input for high accuracy optical surface tracking. Using Monte-Carlo simulations for multi-layered tissue, we show that informative features can be extracted from the simulated tissue reflection by integrating intensities within concentric ROIs around the laser spot center. Training a regression model with a simulated data set identifies patterns that allow for predicting skin thickness with a root mean square error of down to 18 µm. Different approaches to compensate for varying observation angles were shown to yield errors still below 90 µm. Finally, this initial study provides a very promising proof of concept and encourages research towards a practical prototype.

Deep convolutional neural network approach for forehead tissue thickness estimation

“…The results of simulations in [4] showed that the values in the region surrounding the spot also have a significant relation to the tissue thickness. Hence, this extreme difference in magnitude of the pixel values could degrade the quality of the information during the network training phase.…”

“…These previous learning methods, however, require the user, to manually define the learning features [3,4], which may not be good enough for this application. Although the GP algorithm has shown good performance, its complexity of limited the training on training sets with a large size.…”

Combined GPR data analysis technique for diagnostics of structures with thin near-surface layers

“…Next, we consider the real pulse signals of GPR. Fig.4 shows the pulse of the antenna block AB of the "Odyag" georadar (S) reflected from the metal sheet (a) and the signal (S) reflected from the road surface (b) together with the results of the Hilbert transform (1) and OTRI (2). As one can see, more than one maximum of the Hilbert transform (1) and several "sets" (R, r 1 , r 2 ) of the OTRI (2) roots of transform correspond to the main signal, even when reflecting from the metal sheet.…”

“…In many practical problems non-destructive testing of multilayer coatings that have thin nearsurface layers is needed [1,2]. By the term 'thin layers' we mean layers that thickness and other characteristic dimensions are commensurable or less than the resolving power of the diagnostic tool.…”

Data-Driven Learning for Calibrating Galvanometric Laser Scanners

“…They avoid constructing mathematical models being tied to a specific application. Along these lines, we apply supervised statistical learning to a device as used in [22] and [23] for motion tracking in radiotherapy [24]. In combination with a camera, this galvanometric deflection system uses two mirrors to scan the forehead with a single laser beam (Fig.…”