Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements.

Area of Science:

• Biomedical Engineering
• Computational Physics
• Medical Imaging

Background:

• Accurate monitoring of tissue temperature during laser irradiation is crucial for effective and safe laser therapy.
• Existing methods often struggle with the complexity of heat transfer in biological tissues and limited sensor data.

Purpose of the Study:

• To develop and validate a data assimilation Bayesian framework for reconstructing spatiotemporal tissue temperature profiles during laser irradiation.
• To compare state-estimation with joint-estimation approaches for temperature and parameter correction.

Main Methods:

• Implementation of a Bayesian framework integrating a physical heat transfer model with sparse temperature measurements.
• Utilizing an Unscented Kalman Filter for data assimilation.
• Comparison of standard state-estimation with a joint-estimation approach that corrects both temperature and model parameters (thermal diffusivity, laser properties).

Main Results:

• Joint-estimation achieved accurate temperature distribution estimates with maximal errors of 1.5°C (1D synthetic/liver) and 2°C (2D phantom).
• The method provides strategies for optimizing sensor placement, suggesting non-symmetrical placement for two sensors yields optimal results.
• Joint-estimation significantly enhanced the predictive accuracy of the physical model.

Conclusions:

• The joint-estimation approach within a Bayesian data assimilation framework effectively reconstructs tissue temperature during laser irradiation.
• This framework offers improved predictive capabilities for physical models used in laser therapy.
• The study highlights the potential of data assimilation for advancing laser therapy monitoring and optimization.