Abstract
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 language | English |
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Title of host publication | 2018 IEEE Conference on Decision and Control, CDC 2018 |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 5201-5208 |
Number of pages | 8 |
ISBN (Electronic) | 9781538613955 |
DOIs | |
Publication status | Published - 18 Jan 2019 |
Event | 57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018 Conference number: 57 |
Conference
Conference | 57th IEEE Conference on Decision and Control, CDC 2018 |
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Abbreviated title | CDC 2018 |
Country/Territory | United States |
City | Miami |
Period | 17/12/18 → 19/12/18 |
Funding
This research has been made possible by the Dutch Cancer Society and the Netherlands Organisation for Scientific Research (NWO) as part of their joint Partnership Programme “Technology for Oncology”. This project is partially financed by the PPP Allowance made available by Top Sector Life Sciences & Health. This research is supported by Pyrexar Medical.