Identification of tissue optical properties during thermal laser‐tissue interactions: An ensemble Kalman filter‐based approach

Abstract

In this article, we propose a computational framework to estimate the physical properties that govern the thermal response of laser-irradiated tissue. We focus in particular on two quantities, the absorption and scattering coefficients, which describe the optical absorption of light in the tissue and whose knowledge is vital to correctly plan medical laser treatments. To perform the estimation, we utilize an implementation of the ensemble Kalman filter (EnKF), a type of Bayesian filtering algorithm for data assimilation. Unlike prior approaches, in this work, we estimate the tissue optical properties based on observations of the tissue thermal response to laser irradiation. This method has the potential for straightforward implementation in a clinical setup, as it would only require a simple thermal sensor, for example, a miniaturized infrared camera. Because the optical properties of tissue can undergo shifts during laser exposure, we employ a variant of EnKF capable of tracking time-varying parameters. Through simulated experimental studies, we demonstrate the ability of the proposed technique to identify the tissue optical properties and track their dynamic changes during laser exposure, while simultaneously tracking changes in the tissue temperature at locations beneath the surface. We further demonstrate the framework's capability in estimating additional unknown tissue properties (i.e., the volumetric heat capacity and thermal conductivity) along with the optical properties of interest.

When the End Effector Is a Laser: A Review of Robotics in Laser Surgery

“…Arnold and Fichera have demonstrated, in simulation, that it is possible to identify the absorption and scattering coefficients of tissue by measuring the temperature change created by a known set of laser inputs. [111] The authors simulate the thermal evolution of tissue using Equation (8) while it is being exposed to repeated laser pulses. Using a variant of the ensemble Kalman filter, they are able to track time-varying optical properties and temperature beneath the tissue surface based on the surface temperature.…”

State and parameter estimation for retinal laser treatment

“…Very recently, some related work has been presented in [16], where the absorption coefficient in a homogeneous tissue is estimated via an ensemble Kalman filter (EnKF). In contrast, in this work we consider an inhomogeneous tissue with different absorption coefficients; furthermore, since we aim for (computationally demanding) model predictive control, state and parameter estimation has to be real-time capable and hence cannot be done using a large-scale full order model as in [16]. Instead, we show that EKF and MHE schemes can be developed based on a suitably defined reduced order model.…”

When Artificial Parameter Evolution Gets Real: Particle Filtering for Time-Varying Parameter Estimation in Deterministic Dynamical Systems

“…While many approaches focus on estimating constant (or static) parameters, a subset of these problems includes parameters that are known to vary with time but have unknown dynamics that often cannot be directly observed. Examples include the seasonal transmission parameter in modeling the spread of infectious diseases [1,2], the input stimuli in modeling neuron dynamics [3,4], and the tissue optical properties in modeling thermal laser-tissue interactions [5]. The main challenge in estimating time-varying parameters lies in accurately accounting for their time evolution without information regarding their temporal dynamics.…”