Parameter estimation and model reduction for model predictive control in retinal laser treatment

verfasst von

Manuel Schaller, Mitsuru Wilson, Viktoria Kleyman, Mario Mordmüller, Ralf Brinkmann, Matthias A. Müller, Karl Worthmann

Abstract

Laser photocoagulation is one of the most frequently used treatment approaches for retinal diseases such as diabetic retinopathy and macular edema. The use of model-based control, such as Model Predictive Control (MPC), enhances a safe and effective treatment by guaranteeing temperature bounds. In general, real-time requirements for model-based control designs are not met since the temperature distribution in the eye fundus is governed by a heat equation with a nonlinear parameter dependency. This issue is circumvented by representing the model by a lower-dimensional system which well-approximates the original model, including the parametric dependency. We combine a global-basis approach with the discrete empirical interpolation method, tailor its hyperparameters to laser photocoagulation, and show its superiority in comparison to a recently proposed method based on Taylor-series approximation. Its effectiveness is measured in computation time for MPC. We further present a case study to estimate the range of absorption parameters in porcine eyes, and by means of a theoretical and numerical sensitivity analysis we show that the sensitivity of the temperature increase is higher with respect to the absorption coefficient of the retinal pigment epithelium (RPE) than of the choroid's.

Organisationseinheit(en)

Institut für Regelungstechnik

Externe Organisation(en)

Technische Universität Ilmenau
Universität zu Lübeck
Medizinisches Laserzentrum Lübeck GmbH

Typ

Artikel

Journal

Control engineering practice

Band

128

ISSN

0967-0661

Publikationsdatum

11.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Angewandte Informatik, Elektrotechnik und Elektronik, Angewandte Mathematik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1016/j.conengprac.2022.105320 (Zugang: Geschlossen)