Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction

Zhao He; Ya-Nan Zhu; Suhao Qiu; Xiaoqun Zhang; Yuan Feng

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arXiv · 2107.11947

Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction

Zhao He·Ya-Nan Zhu·Suhao Qiu·Xiaoqun Zhang·Yuan Feng

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Abstract

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.

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Zhao He, Ya-Nan Zhu, Suhao Qiu, Xiaoqun Zhang, Yuan Feng. 2021-07-26. Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction. https://arxiv.org/abs/2107.11947

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