B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI

TL;DR

This paper introduces BJORK, a joint optimization method for MRI that combines trajectory design and reconstruction using B-spline parameterization and neural networks, leading to significantly improved image quality at high acceleration factors.

Contribution

It proposes a novel joint optimization framework for MRI trajectory and reconstruction, utilizing B-spline parameterization and multi-scale optimization to enhance image quality.

Findings

Learned trajectories outperform previous methods in simulations and in-vivo tests.

The method improves image quality at 10x acceleration with neural network and compressed sensing reconstructions.

Trajectory optimization reduces artifacts and enhances reconstruction fidelity.

Abstract

Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in-vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10 times acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.