用神经网络同时去噪和解包裹，提升4D流MRI的准确性与可靠性。
Unsupervised 4D Flow MRI Velocity Enhancement and Unwrapping Using Divergence-Free Neural Networks
- 通过旋度参数化速度场，天然满足质量守恒，无需调参。
- 在合成数据上速度误差降低11%，包裹残余像素减少72%。
- 适合心血管影像分析，尤其处理噪声与相位混叠并存场景。
本文提出一种无监督的发散与混叠自由神经网络（DAF-FlowNet），用于4D流MRI中同时增强噪声速度场并校正相位包裹伪影。该方法将速度场参数化为矢量势的旋度，从构造上保证质量守恒，避免显式发散惩罚项调参。采用余弦数据一致性损失，可从包裹相位图像中联合实现去噪与解包裹。在基于计算流体力学生成的主动脉4D流MRI合成数据上，DAF-FlowNet性能优于现有方法：速度归一化均方根误差降低最多11%，方向误差降低11%，发散值降低44%（在不同噪声水平下对比最优替代方法）。在解包裹任务中，于峰值速度/编码比1.4和2.1时，残余包裹体素分别降至0.18%和5.2%，较最优方法减少72%和18%。当噪声与混叠共存时，单阶段框架优于当前最优的串行流程（速度误差降低15%，方向误差降低11%，发散降低28%）。在10例肥厚型心肌病患者数据上，该方法保留了精细流动结构，纠正了混叠区域，并通过主动脉与肺动脉的质量守恒分析改善了平面间流动一致性，符合4D流MRI共识指南建议。结果表明，DAF-FlowNet是一种统一速度增强与相位解包裹的可靠框架。
原文摘要 · Abstract (English)
This work introduces an unsupervised Divergence and Aliasing-Free neural network (DAF-FlowNet) for 4D Flow Magnetic Resonance Imaging (4D Flow MRI) that jointly enhances noisy velocity fields and corrects phase wrapping artifacts. DAF-FlowNet parameterizes velocities as the curl of a vector potential, enforcing mass conservation by construction and avoiding explicit divergence-penalty tuning. A cosine data-consistency loss enables simultaneous denoising and unwrapping from wrapped phase images. On synthetic aortic 4D Flow MRI generated from computational fluid dynamics, DAF-FlowNet achieved lower errors than existing techniques (up to 11% lower velocity normalized root mean square error, 11% lower directional error, and 44% lower divergence relative to the best-performing alternative across noise levels), with robustness to moderate segmentation perturbations. For unwrapping, at peak velocity/velocity-encoding ratios of 1.4 and 2.1, DAF-FlowNet achieved 0.18% and 5.2% residual wrapped voxels, representing reductions of 72% and 18% relative to the best alternative method, respectively. In scenarios with both noise and aliasing, the proposed single-stage formulation outperformed a state-of-the-art sequential pipeline (up to 15% lower velocity normalized root mean square error, 11% lower directional error, and 28% lower divergence). Across 10 hypertrophic cardiomyopathy patient datasets, DAF-FlowNet preserved fine-scale flow features, corrected aliased regions, and improved internal flow consistency, as indicated by reduced inter-plane flow bias in aortic and pulmonary mass-conservation analyses recommended by the 4D Flow MRI consensus guidelines. These results support DAF-FlowNet as a framework that unifies velocity enhancement and phase unwrapping to improve the reliability of cardiovascular 4D Flow MRI.
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