# 4D Flow MRI at 0.6 T—Self-Gating Versus Camera-Based Respiratory Binning

## TL;DR

This study compares two methods for reducing motion in 4D MRI scans at low magnetic field strength, finding they produce similar results.

## Contribution

The study evaluates the effectiveness of self-gating versus camera-based binning for 4D flow MRI at 0.6 Tesla.

## Key findings

- Self-gating and camera-based binning showed strong agreement with cross-correlation coefficients of ~0.87.
- Velocity analysis revealed high concordance (R2 = 0.99) between the two methods.
- Camera-based binning is independent of MR image SNR, making it a viable alternative at low field strengths.

## Abstract

Four-dimensional (4D) flow MRI enables the comprehensive assessment of cardiovascular hemodynamics. To compensate for respiratory motion, self-gating strategies are typically used and perform reliably at clinical field strengths. With the recent push towards field strengths below 1 Tesla, these strategies need to be re-evaluated given the reduced signal-to-noise ratio (SNR). Camera-based, contactless respiratory monitoring offers an attractive alternative to self-gating, as it is unaffected by imaging. This study compared respiratory self-gating (SG) and camera-based (VE) binning for phase-contrast gradient-echo (PC-GRE) 4D flow MRI at 0.6 T. Data were acquired from twenty healthy subjects (age: 32.8 ± 12.6 years) using a pseudo-spiral undersampled Cartesian four-point velocity encoding scheme. Reconstructions were performed with FlowMRI-Net for the end-expiratory state using either SG or VE binning. SG and VE showed strong agreement, with cross-correlation coefficients of ~0.87, accuracies of ~0.87, and F1-scores of ~0.9. Velocity analysis revealed high concordance (R2 = 0.99; RMSE = 3.9 cm/s), with mean differences in peak velocity of 1.25 ± 2.36 cm/s. In this feasibility study, respiratory self-gating and camera-based binning yielded similar hemodynamic parameters from PC-GRE 4D flow MRI at 0.6 T, with the camera-based approach being independent of MR image SNR.