Low-rank and framelet based sparsity decomposition for interventional MRI reconstruction
IEEE Transactions on Biomedical Engineering, 2022•ieeexplore.ieee.org
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 real-time i-MRI. 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 …
reconstruction methods for dynamic MR imaging are mostly retrospective that may not be
suitable for real-time i-MRI. 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 …
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 real-time i-MRI. 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 algorithms, the spatial sparsity of both the low-rank and sparsity components was used. 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.
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