Identification of the Blood Perfusion Rate for Laser-Induced Thermotherapy in the Liver

TL;DR

This paper presents a PDE-constrained optimization method to identify blood perfusion rates during liver laser-induced thermotherapy, enabling real-time treatment monitoring and prediction from MR thermometry data.

Contribution

It introduces a novel parameter identification approach for blood perfusion rates using MR data during LITT, enhancing treatment control.

Findings

Effective identification with synthetic data

Robustness to noise demonstrated

Potential for real-time treatment monitoring

Abstract

Using PDE-constrained optimization we introduce a parameter identification approach which can identify the blood perfusion rate from MR thermometry data obtained during the treatment with laser-induced thermotherapy (LITT). The blood perfusion rate, i.e., the cooling effect induced by blood vessels, can be identified during the first stage of the treatment. This information can then be used by a simulation to monitor and predict the ongoing treatment. The approach is tested with synthetic measurements with and without artificial noise as input data.