  Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy.
Current image reconstruction methods for dynamic MR imaging are mostly
retrospective that may not be suitable for i-MRI in real-time. Therefore, an
algorithm to reconstruct images without a temporal pattern as in dynamic
imaging is needed for i-MRI. Methods: We proposed a low-rank and sparsity (LS)
decomposition algorithm with framelet transform to reconstruct the
interventional feature with a high temporal resolution. Different from the
existing LS based algorithm, we utilized the spatial sparsity of both the
low-rank and sparsity components. We also used a primal dual fixed point (PDFP)
method for optimization of the objective function to avoid solving
sub-problems. Intervention experiments with gelatin and brain phantoms were
carried out for validation. Results: The LS decomposition with framelet
transform and PDFP could provide the best reconstruction performance compared
with those without. Satisfying reconstruction results were obtained with only
10 radial spokes for a temporal resolution of 60 ms. Conclusion and
Significance: The proposed method has the potential for i-MRI in many different
application scenarios.
