Identification of the blood perfusion rate for laser-induced thermotherapy in the liver

Matthias Andres,Sebastian Blauth,Christian Leithäuser,Norbert Siedow

doi:10.1186/s13362-020-00085-1

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.

Highlights

Laser-induced interstitial thermotherapy (LITT) is a minimally invasive, local therapy used to destroy tumors through thermal ablation
While typically good measurements are available for many of the tissue parameters, a critical role is the determination of the blood perfusion rate that models the cooling effect induced by blood vessels
The idea proposed in this paper is to identify the perfusion rate in a short time period during the beginning of the treatment from magnetic resonance (MR) thermometry data

Summary

Introduction

Laser-induced interstitial thermotherapy (LITT) is a minimally invasive, local therapy used to destroy tumors through thermal ablation. Our method proceeds as follows: On the first interval, i.e., (0, τ1) we use Algorithm 1 in order to identify the blood perfusion rate ξ (1) and the resulting temperature distribution T(1) = T[ξ (1)] as well as damage function ω(1). These are used as initial conditions for the state equations for the identification in the subsequent interval (τ1, τ2). Doing so saves a lot of computational time and is a stepping stone for the use of the method in an online therapy-planning and -monitoring tool

Noisy model problem Let us now consider the case of noisy measurement data

Findings

Conclusions