An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities

TL;DR

This paper introduces a calibration-free, optimal control-based deep learning framework for parallel MRI reconstruction that effectively combines multi-coil data and extracts features in both image and Fourier spaces, demonstrating superior performance.

Contribution

It presents a novel variational model cast as a structured discrete-time optimal control system, enabling efficient training and improved reconstruction without coil sensitivities.

Findings

Outperforms several state-of-the-art pMRI networks on real datasets.

Ensures local convergence through Lagrangian-based training.

Effectively learns multi-coil combination and regularization in image and k-space domains.

Abstract

Goal: This work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruction model is designed to learn a regularization that combines channels and extracts features by leveraging the information sharing among channels of multi-coil images. We propose to recover both magnitude and phase information by taking advantage of structured convolutional networks in image and Fourier spaces. Methods: We develop a novel variational model with a learnable objective function that integrates an adaptive multi-coil image combination operator and effective image regularization in the image and Fourier spaces. We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables. We demonstrate that the Lagrangian method for solving the control problem is equivalent to back-propagation, ensuring the local convergence of the training algorithm. Results: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. Conclusion: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. Significance: By learning multi-coil image combination operator and performing regularizations in both image domain and k-space domain, the proposed method achieves a highly efficient image reconstruction network for pMRI.