What does this research mean for the field?

Respiratory-gated deep learning-reconstructed T2-weighted imaging improves image quality for liver MRI in patients who have difficulty with breath-holding. Novelty: ClaimNovelty.CONFIRMATORY. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to compare respiratory-gated and breath-hold T2-weighted sequences in liver MRI using deep learning reconstruction.

February 25, 2026Open Access

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

Key Points

This research aims to compare respiratory-gated and breath-hold T2-weighted sequences in liver MRI using deep learning reconstruction.
Used T2-weighted imaging (T2WI) sequences under respiratory-gated and breath-hold conditions.
Applied deep learning reconstruction techniques to enhance image quality.
Assessed the impact of respiratory curve traits on MRI quality.
Both imaging techniques resulted in satisfactory image quality.
Respiratory curve characteristics varied the quality of T2WI.
Respiratory-gated approaches were particularly beneficial for patients struggling with breath-holding.

Abstract

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.

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

Key Points

Abstract

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