Adaptive rejection of outliers for robust motion compensation in cardiac MR-thermometry

New Magnetic Resonance (MR) imaging applications include real time monitoring of temperature changes during cardiac radiofrequency ablations. MR-thermometry requires online robust motion compensation to cope with the complex motion of the heart resulting from respiratory activity and cardiac contraction (potentially in presence of arrhythmia), together with the presence of noise in MR images. We propose a method to adaptively and automatically tune parameters of motion compensation algorithms that use robustness function. The core of the method is the estimation of the probability density function (pdf) of the error for each pixel in a reference frame using the Rician noise pdf model in MRI. Then parameter map is derived from estimated pdf. The proposed method leads to better results than using a fixed control parameter of the robustness function, which should facilitate the use of such methods for clinical purpose.