Combination of principal component analysis and optical-flow motion compensation for improved cardiac MR thermometry.

The use of magnetic resonance (MR) thermometry for the monitoring of thermal ablation is rapidly expanding. However, this technique remains challenging for the monitoring of the treatment of cardiac arrhythmia by radiofrequency ablation due to the heart displacement with respiration and contraction. Recent studies have addressed this problem by compensating in-plane motion in real-time with optical-flow based tracking technique. However, these algorithms are sensitive to local variation of signal intensity on magnitude images associated with tissue heating. In this study, an optical-flow algorithm was combined with a principal component analysis method to reduce the impact of such effects. The proposed method was integrated to a fully automatic cardiac MR thermometry pipeline, compatible with a future clinical workflow. It was evaluated on nine healthy volunteers under free breathing conditions, on a phantom and in vivo on the left ventricle of a sheep. The results showed that local intensity changes in magnitude images had lower impact on motion estimation with the proposed method. Using this strategy, the temperature mapping accuracy was significantly improved.