3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI

TL;DR

This paper introduces 3D FLAT, a data-driven method for designing feasible 3D MRI acquisition trajectories that improve image quality and efficiency over traditional and 2D learned trajectories by leveraging deep learning and computational imaging principles.

Contribution

The paper presents a novel deep learning-based approach for designing physically feasible 3D MRI trajectories that enhance image quality and acquisition speed compared to existing methods.

Findings

3D FLAT outperforms standard trajectories in image quality for the same readout time.

Non-Cartesian 3D trajectories offer significant benefits over 2D slice-wise trajectories.

Experimental results validate the effectiveness of the learned trajectories.

Abstract

Magnetic Resonance Imaging (MRI) has long been considered to be among the gold standards of today's diagnostic imaging. The most significant drawback of MRI is long acquisition times, prohibiting its use in standard practice for some applications. Compressed sensing (CS) proposes to subsample the k-space (the Fourier domain dual to the physical space of spatial coordinates) leading to significantly accelerated acquisition. However, the benefit of compressed sensing has not been fully exploited; most of the sampling densities obtained through CS do not produce a trajectory that obeys the stringent constraints of the MRI machine imposed in practice. Inspired by recent success of deep learning based approaches for image reconstruction and ideas from computational imaging on learning-based design of imaging systems, we introduce 3D FLAT, a novel protocol for data-driven design of 3D non-Cartesian accelerated trajectories in MRI. Our proposal leverages the entire 3D k-space to simultaneously learn a physically feasible acquisition trajectory with a reconstruction method. Experimental results, performed as a proof-of-concept, suggest that 3D FLAT achieves higher image quality for a given readout time compared to standard trajectories such as radial, stack-of-stars, or 2D learned trajectories (trajectories that evolve only in the 2D plane while fully sampling along the third dimension). Furthermore, we demonstrate evidence supporting the significant benefit of performing MRI acquisitions using non-Cartesian 3D trajectories over 2D non-Cartesian trajectories acquired slice-wise.