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During interventional procedures, clinicians require immediate imaging feedback. The requirement can be fulfilled by the online reconstruction of dynamic magnetic resonance imaging (dMRI), where the current frame is reconstructed using only previous data. Unfortunately, existing online reconstruction algorithms fall short in terms of reconstruction quality and time delay, failing to meet the demands of modern interventional treatments. Addressing this issue, we introduce a more effective and efficient online reconstruction algorithm. To expedite the reconstruction process, our approach employs the concept of subspace tracking. Initially, each dMRI frame is interpreted as a subspace component and an error component, with the subspace component posited to reside on a Grassmannian manifold of one-dimensional subspace. Then, the current frame's subspace component is updated along the geodesic, drawing on the prior frame's reconstruction result, while the error component is updated following the gradient direction. Since the update for each frame is completed in a single step without iteration, the proposed algorithm can achieve rapid reconstruction. To enhance the quality, we collect the first five frames of under sampled Cartesian k-space data. Using the low-rank plus sparse reconstruction method for offline processing, we generate high-quality initial estimates of subspace components. This step substantially improves the overall reconstruction quality. Our comparative study against various online reconstruction methods at different acceleration rates on an open cardiac cine dataset demonstrates that our algorithm outperforms others in achieving high spatial-temporal resolution reconstruction with minor delay.
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