16 / 2021-09-14 09:20:16

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

interventional MRI,image reconstruction,low-rank and sparsity decomposition

Purpose: Intervention MRI (i-MRI) plays an important role in MRI-guided surgery. Fast data acquisition and image reconstruction are necessary for i-MRI to monitor the interventional process. However, conventional fast imaging methods reconstruct images in a retrospective way 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 decomposition (LS) with framelet transform to reconstruct interventional images 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 proposed model to avoid solving sub-problems. Interventional experiments with gelatin and brain phantoms were carried out for validation.

Results: The LS decomposition with Framelet transform and PDFP (LSFP) 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: In this study, we proposed an LSFP algorithm for i-MRI reconstruction. Results showed the LSFP algorithm had better performance than other retrospective reconstruction methods. The improved temporal resolution demonstrates the potential of the proposed method for a variety of real-time i-MRI scenarios.