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

Tobias Wissel, Ralf Bruder, Achim Schweikard, Floris Ernst, Tobias Wissel, Ralf Bruder, Achim Schweikard, Floris Ernst

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.

Keywords: (100.5010) Pattern recognition; (170.1610) Clinical applications; (170.3660) Light propagation in tissues; (170.6935) Tissue characterization; (280.0280) Remote sensing and sensors.

Figures

Fig. 1
Fig. 1
Comparison of a common spectroscopy and laser scanner setup. (a) Reflectance spectroscopy: one light-emitting and one sensing probe are placed directly on the skin surface. The light source emits multiple wavelengths and the spectrograph analyses the intensity spectrum of all the incoming light. (b) Laser scanner consisting of a laser light source and a high-resolution camera. Both are located at a minimum of 30 cm above the skin. The camera records the spatial pattern of the total diffuse reflection.
Fig. 2
Fig. 2
Soft tissue model and program flowchart of the MCML simulation software. (a) According to a Gaussian beam profile, photons are applied in a certain angle to an eight-layer skin model. Reflection (blue grid) and absorption (red grid) are recorded for simulation output. (b) Flowchart for simulating the interactions of a single photon. All blue elements correspond to own extensions to the standard MCML software developed by [21].
Fig. 3
Fig. 3
Image changes during the preprocessing steps. The image resulted from simulated laser beam of 45° incidence angle. The spot is shown from above (upper plots) and by the cross-sections along its main half-axes (bottom plots). (a) After PCA the output image contains a centered spot. The half-axes are aligned to the coordinate axes x (green) and y (red). (b) Spot parameters are obtained from a fitting a Gaussian to the cross-sections. After rescaling and interpolation a circular shape is regained. (c) The amplitude of the profile is scaled to one (optional). This is to compensate for different laser to object and camera distances that may influence the spot size in practice. (d) After a possible nonlinear rescaling using LUTs (not shown), features are extracted. Each feature is the accumulated intensity value from a concentric ROI (white circles or blue shaded regions, respectively).
Fig. 4
Fig. 4
Sketch of the entire system comprising preprocessing, feature extraction and regression elements denoted by different boxes.
Fig. 5
Fig. 5
Proportions of light reflected from individual skin layers for different wavelengths (400 nm–980 nm). A photon is assigned to a proportion if this proportion corresponded to the deepest tissue layer the photon had at least one interaction with. Only photons leaving the skin are recorded and their number displayed relative to the total number of reflected photons.
Fig. 6
Fig. 6
Diffuse reflection RD observed at the soft tissue surface. By labeling each photon the total diffuse reflection at each location (x, y) is decomposed into its components originating from all different tissue layers. The color-coded intensities are given relative to the total diffuse reflection at each location.
Fig. 7
Fig. 7
Reflected light from each layer relative to the total reflection R at each location plotted across the spot radius r. Due to the symmetry for an incident angle of 90°, the photon energy has been integrated across the azimuthal angle. The relative proportion of light from bone and fat increases with the radius.
Fig. 8
Fig. 8
Feature analysis. (a) Feature space for orthogonal irradiation sampled at 101 different values for the thickness of the subcutaneous fat (df∈[0,0.5]cm). (b) Concatenated feature spaces resulting for different incident angles after the fitting step. Each tick at the horizontal axis denotes a particular angle, whereas the space between two ticks contains samples for different thickness values df equivalent to the plot in (a). The red line denotes a threshold from which onwards the angle influence substantially increases. Note that lines for bin 1 and bin 2 run very close to each other. (c) Four look-up tables (LUTs) indicating the remaining influence for the given incident angles after the fitting step has been applied.

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