Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction
Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction
复制标题
用于介入性 MRI 重建的低秩和基于框架的稀疏分解
DOI：
10.1109/tbme.2022.3142129
复制
发表时间：
2022-07-01
影响因子：
4.6
通讯作者：
Feng, Yuan
中科院分区：
文献类型：
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作者：
He, Zhao;Zhu, Ya-Nan;Feng, Yuan
目的：介入性MRI（Interventional MRI，i-MRI）是磁共振图像引导治疗的关键。目前用于动态MR成像的图像重建方法大多是回顾性的，可能不适合实时i-MRI。因此，i-MRI需要一种算法来重建图像，而不需要动态成像中的时间模式。研究方法：我们提出了一种低秩和稀疏（LS）分解算法与小框架变换重建的介入功能与高的时间分辨率。与现有的基于LS的算法不同，该算法同时利用了低秩分量和稀疏分量的空间稀疏性。我们还使用了原始对偶不动点（PDFP）方法优化的目标函数，以避免解决子问题。采用明胶和脑模型进行干预实验进行验证。结果：最小二乘分解结合framelet变换和PDFP可以提供最好的重建性能相比，没有。满意的重建结果得到了只有10个径向辐条的时间分辨率为60 ms。结论和意义：所提出的方法具有潜力的i-MRI在许多不同的应用场景。
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.