POD-based recursive temperature estimation for MR-guided RF hyperthermia cancer treatment: a pilot study

R.W.M. Hendrikx, S. Curto, Bram de Jager, E. Maljaars, G.C. van Rhoon, M.M. Paulides, W.P.M.H. Heemels

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
6 Downloads (Pure)


In this paper, proper-orthogonal-decomposition (POD) reduced models of the body's heat response to radio-frequency hyperthermia cancer treatment are used for recursive temperature estimation. First, efficient low-dimensional models are obtained by projecting high-resolution finite-difference discretized models on low-dimensional subspaces spanned by empirical simulation modes. These models are then used in a Kalman filter to obtain recursive 3D temperature estimates from noise-susceptible magnetic resonance thermometry (MRT). The strategy is tested on an experimental setup containing an anthropomorphic phantom. It is found that recursive estimation reduces the mean absolute temperature error for the phantom experiment by 38% when compared to MRT and may be a valuable addition to MRT, most notably in the case where high quality thermometry is temporally interleaved with thermometry of degraded quality.

Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781538613955
Publication statusPublished - 18 Jan 2019
Event57th IEEE Conference on Decision and Control (CDC2018) - Miami, United States
Duration: 17 Dec 201819 Dec 2018
Conference number: 57


Conference57th IEEE Conference on Decision and Control (CDC2018)
Abbreviated titleCDC 2018
Country/TerritoryUnited States


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