Real-time MRI can provide high soft-tissue contrast without ionizing radiation for interventional procedure guidance. To achieve accurate and low-latency tracking of target tissues for decision support and feedback control, this work proposes a motion prediction framework based on a multi-rate Kalman filter and real-time golden-angle radial MRI. The proposed framework leverages the unique sampling pattern of golden-angle radial acquisition to combine image-based with surrogate-based motion tracking. Initial results demonstrate that the proposed framework can achieve significantly reduced error in motion prediction and provide low-latency feedback for real-time MRI guided interventions.
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