Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI

TL;DR

This paper introduces Cine-DL, a deep learning framework for high-quality free-breathing cardiac MRI reconstruction, addressing motion artifacts and improving clinical feasibility.

Contribution

Cine-DL combines targeted k-space preprocessing with a model-based deep reconstruction, including novel streak-optimized coil compression, for robust free-breathing cardiac MRI.

Findings

Cine-DL outperforms established methods like k-t SENSE and iGRASP.

It improves quantitative metrics and visual fidelity in free-breathing cardiac MRI.

Demonstrated successful clinical deployment on patient data.

Abstract

Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infeasible, particularly for pediatric and other noncompliant patients who cannot reliably hold their breath. Free-breathing radial acquisitions can alleviate these limitations, but robust reconstruction at high acceleration remains challenging due to prominent streak artifacts. To address these limitations, we propose Cine-DL, a clinically oriented framework that couples targeted k-space preprocessing with fast, model-based deep reconstruction. In this pipeline, raw free-breathing radial data undergo retrospective cardiac binning and respiratory gating to resolve cardiac phases and discard motion-corrupted spokes. We then introduce Streak Optimized Coil Compression (SOC), which explicitly preserves cardiac signals while suppressing peripheral interference that typically drives the streak artifacts. The resulting 2D+t cine series is reconstructed with an unrolled network that alternates a ResNet proximal operator with physics-based data consistency updates solved via conjugate gradient. We further employ a memory-efficient training strategy that reduces peak memory usage. We evaluate Cine-DL on free-breathing volunteer data against established baselines (k-t SENSE and iGRASP) and demonstrate clinical translation via hospital deployment on newly acquired patient data. Our experiments show that Cine-DL consistently improves quantitative metrics and visual fidelity, supporting a practical route toward routine, time-sensitive clinical adoption of free-breathing cine MRI.