Concurrent hyperthermia estimation schemes based on extended kalman filtering and reduced-order modelling

J. K. Potocki, H. S. Tharp

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.

Original languageEnglish (US)
Pages (from-to)849-865
Number of pages17
JournalInternational Journal of Hyperthermia
Issue number6
StatePublished - 1993


  • Blood perfusion
  • EKF
  • Estimation
  • Hyperthermia
  • Model reduction

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)
  • Cancer Research


Dive into the research topics of 'Concurrent hyperthermia estimation schemes based on extended kalman filtering and reduced-order modelling'. Together they form a unique fingerprint.

Cite this