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

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