Accelerating KalmanNet using POD: Real-time 3D MR-thermometry for monitoring microwave ablation procedures

Motivation: Noise is a challenge for real-time MR-thermometry. The Extended Kalman Filter (EKF) has been shown to reduce noise successfully in temperature maps. Goal(s): We modified KalmanNet, a recurrent neural network emulating an EKF, using a proper orthogonal decomposition (POD) to reduce computational cost. Approach: POD-KalmanNet was applied to microwave ablations of 14 bioprotein phantoms. Mean squared errors (MSE) and Sørensen-Dice-Coefficient (DSC) were compared between noisy and filtered data. Results: Results show a highly significant reduction of MSE (p < 0.001) and a significant increase of DSC (p < 0.05) for filtered images compared to noisy images. POD-KalmanNet could enable real-time filtering of MR-Thermometry. Impact: KalmanNet was modified using proper orthogonal decomposition. This modification allows lower memory usage and inference times. It makes the application of KalmanNet to 3D temperature maps practically feasible. Reducing noise in 3D temperature maps can improve outcomes of thermoablation procedures.