State Estimation of Time-Varying MRI with Radial Golden Angle Sampling

We propose a state estimation approach to time-varying magnetic resonance imaging utilizing a priori information. In state estimation, the time-dependent image reconstruction problem is modeled by separate state evolution and observation models. In our method, we compute the state estimates by using the Kalman filter and steady-state Kalman smoother utilizing a data-driven estimate for the process noise covariance matrix, constructed from conventional sliding window estimates. The proposed approach is evaluated using radially golden angle sampled simulated and experimental small animal data from a rat brain. In our method, the state estimates are updated after each new spoke of radial data becomes available, leading to faster frame rate compared with the conventional approaches. The results are compared with the estimates with the sliding window method. The results show that the state estimation approach with the data-driven process noise covariance can improve both spatial and temporal resolution.

Automated summary

We propose a state estimation approach for time-varying magnetic resonance imaging using a priori information. The method models the time-dependent image reconstruction problem with separate state evolution and observation models and utilizes the Kalman filter and steady-state Kalman smoother to compute state estimates. The results show that the proposed approach with data-driven process noise covariance can improve both spatial and temporal resolution.