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

TL;DR

This paper introduces a novel low-rank and sparsity decomposition method with framelet transform and PDFP optimization for real-time interventional MRI reconstruction, achieving high temporal resolution with minimal data.

Contribution

It proposes a new LS decomposition algorithm incorporating spatial sparsity and framelet transform, optimized via PDFP, tailored for interventional MRI reconstruction.

Findings

Achieved high-quality reconstruction with only 10 radial spokes at 60 ms resolution.

Outperformed existing LS-based algorithms in reconstruction quality.

Validated with gelatin and brain phantom experiments.

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.