Model-based determination of the synchronization delay between MRI and trajectory data

TL;DR

This paper introduces a fast, model-based algorithm that automatically synchronizes MRI data with trajectory data in real time, significantly improving image quality by minimizing artifacts due to delay mismatches.

Contribution

The authors developed a novel iterative convex optimization method for automatic synchronization of MRI and trajectory data using only the data itself.

Findings

Minimized artifacts in in vivo MRI scans at 7T within 30 seconds.

Achieved synchronization accuracy within tens of nanoseconds in simulations.

Applicable to spiral and EPI MRI acquisitions with real-time performance.

Abstract

Purpose: Real time monitoring of dynamic magnetic fields has recently become a commercially available option for measuring MRI k-space trajectories and magnetic fields induced by eddy currents in real time. However, for accurate image reconstructions, sub-microsecond synchronization between the MRI and trajectory data is required. In this work, we introduce a new model-based algorithm to automatically perform this synchronization using only the MRI and trajectory data. Methods: The algorithm works by enforcing consistency between the MRI data, trajectory data, and receiver sensitivity profiles by iteratively alternating between convex optimizations for (a) the image and (b) the synchronization delay. A healthy human subject was scanned at 7T using a transmit-receive coil with integrated field probes using both single shot spiral and echo-planar imaging (EPI), and reconstructions with various synchronization delays were compared to the result of the proposed algorithm. The accuracy of the algorithm was also investigated using simulations, where the acquisition delays for simulated acquisitions were determined using the proposed algorithm and compared to the known ground truth. Results: In the in vivo scans, the proposed algorithm minimized artefacts related to synchronization delay for both spiral and EPI acquisitions, and the computation time required was less than 30 seconds. The simulations demonstrated accuracy to within tens of nanoseconds. Conclusion: The proposed algorithm can automatically determine synchronization delays between MRI data and trajectories measured using a field probe system.