Abstract : Real-time magnetic resonance (MR) thermometry provides continuous temperature mapping inside the human body and is therefore a promising tool to monitor and control interventional therapies based on thermal ablation. Temperature information must be mapped to a reference position of observed organs in order to allow thermal dose computation, as the history of temperature is required for each pixel. Motion compensated MR-thermometry for thermotherapy has to cope with radio-frequency (RF) artifacts and relaxation-time changes of the monitored tissue. While purely optical-flow-based realignment may lead to temperature map computation errors for the case of local or global intensity changes, principal component analysis based realignment results in accurately registered temperature maps. The motion estimation process described in this paper consists of two steps : a parameterized flow models is initially computed using a principal component analysis during a preparative learning step; during the intervention, motion is characterized with a small set of parameters using a least square solver.