k-t SPARKS: Dynamic Parallel MRI Exploiting Sparse Kalman Smoother

Dynamic parallel magnetic resonance imaging (PMRI) has been widely used in a variety of fast imaging applications to accelerate the data acquisition without any compromise of the spatial-temporal resolution. An accurate calibration is the key for successful dynamic PMRI. However, the calibration quality typically decreases with both small amount of calibrating signals and motion-induced temporally varying coil sensitivity. In this work, we propose a new, dynamic PMRI exploiting sparse Kalman smoother (k-t SPARKS) for robust calibration and reconstruction in the presence of time-varying coil sensitivity, in which the proposed method incorporates the Kalman smoother calibration and the sparse signal recovery into a single optimization problem, leading to joint estimation of time-varying convolution kernel and full k-space. Simulation and experiments were performed using both the proposed and conventional methods in the free-breathing cardiac cine applications for comparison.

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