# Comparison of respiratory-gated and breath‑hold accelerated T2-weighted sequences for liver MRI with deep learning reconstruction

## TL;DR

This study compares deep learning-based liver MRI techniques using respiratory-gated and breath-hold methods, finding similar image quality to traditional approaches and showing how breathing patterns can guide personalized imaging.

## Contribution

The paper introduces a novel comparison of deep learning-based respiratory-gated and breath-hold T2WI for liver MRI, demonstrating their diagnostic quality and the use of respiratory metrics for personalization.

## Key findings

- Respiratory-gated DL-T2WI showed higher overall image quality than breath-hold DL-T2WI.
- Respiratory metrics predicted image quality with high accuracy (AUROC of 0.836 for ARMS-T2WI).
- Respiratory-gated DL-T2WI benefits patients with high respiratory amplitude variability.

## Abstract

T2-weighted imaging (T2WI) of the liver suffers from prolonged scan times and respiratory motion artifacts. Deep learning (DL)-based reconstruction can accelerate acquisition while maintaining diagnostic quality. We compared respiratory-gated (RG) and breath-hold (BH) DL-T2WI to radial k-space sampling acquisition and reconstruction with motion suppression (ARMS)-T2WI and evaluated how respiratory characteristics affect image quality.

We prospectively enrolled 120 participants who underwent 3-T RG DL-, BH DL-, and ARMS-T2WI. Three radiologists evaluated image quality and lesion conspicuity using a 5-point scale. Respiratory characteristics were extracted from breathing curves.

All sequences showed comparable lesion-to-liver contrast ratios (p = 0.139), detection rates (p = 0.106), and lesion conspicuity scores (p = 0.990). RG DL-T2WI showed higher overall image quality compared to BH DL-T2WI (p = 0.027), and similar scores to ARMS-T2WI (p = 0.106). A respiratory score calculated using four parameters predicted ARMS-T2WI image quality with an area under the receiver operating characteristic curve (AUROC) of 0.836 (95% confidence interval 0.638–0.968) in the validation set. For RG DL-T2WI, a respiratory score using seven parameters achieved an AUROC of 0.831 (0.652–0.967) in the validation set. Standard deviation of the respiratory amplitude (SDamp) was an independent factor for BH DL-T2WI image quality (validation set, odds ratio 0.297, p = 0.049). For patients with high SDamp, RG DL-T2WI provided better image quality compared to BH DL-T2WI (68.6% versus 14.3%, p < 0.001).

Both RG and BH DL-T2WI offer image quality comparable to ARMS-T2WI. Respiratory metrics derived from breathing curves may facilitate personalized liver imaging.

Both respiratory-gated and breath-hold T2WI with deep learning reconstruction showed comparable image quality to T2WI based on radial k-space sampling strategies. Respiratory parameters enable personalized magnetic resonance liver imaging workflows.

Respiratory-gated and breath-hold deep learning T2WI exhibited satisfactory image quality.Respiratory curve traits variably impact T2WI quality, guiding personalized imaging workflows.‌Respiratory-gated deep learning-reconstructed T2WI benefits patients with breath-holding difficulties in liver MRI.

Respiratory-gated and breath-hold deep learning T2WI exhibited satisfactory image quality.

Respiratory curve traits variably impact T2WI quality, guiding personalized imaging workflows.‌

Respiratory-gated deep learning-reconstructed T2WI benefits patients with breath-holding difficulties in liver MRI.