UMPIRE-Net：用于加速MRI的展开式幅度-相位正则化网络
UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
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- University of Minnesota（明尼苏达大学）
- Center for Magnetic Resonance Research（磁共振研究中心）
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中文总结 AI 辅助
针对加速MRI的不适定逆问题，提出将幅度与相位解耦正则化的UMPIRE-Net，在部分傅里叶成像场景下提升了重建质量，生成更清晰图像并减少伪影。
中文摘要 AI 辅助
从欠采样的k空间测量值重建MRI是一个不适定的逆问题。物理驱动的深度学习（PD-DL）方法通过将MRI前向模型与算法展开框架内的学习型图像正则化相结合，在该任务中表现出强大性能。然而，大多数现有PD-DL方法直接重建复值图像，从而在单一学习表示中隐式耦合幅度与相位。在准确的相位建模起重要作用的重建场景中，这种耦合正则化可能并非最优，例如部分傅里叶（PF）成像，其中省略的非对称k空间测量值的恢复依赖于底层图像相位。在这类场景中，将幅度和相位显式建模为独立组件，可减少对外部估计或预定义相位信息的依赖。为此，我们提出UMPIRE-Net（展开式幅度-相位正则化网络，Unrolled Magnitude-Phase In REgularization Network），这是一种PD-DL方法，它为幅度和相位组件引入独立的学习型正则化器，同时采用新颖的数据保真度公式以确保测量一致性。我们在不同数据集和加速因子下，针对带PF的加速MRI评估UMPIRE-Net。实验结果表明，与传统复值PD-DL基线相比，我们提出的方法提升了重建质量，产生更清晰的图像并减少了伪影。代码可在以下网址获取：this https URL
英文摘要
MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on the underlying image phase. In such scenarios, explicit modeling of magnitude and phase as separate components may reduce the reliance on externally estimated or predefined phase information. To this end, we propose UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency. We evaluate UMPIRE-Net for accelerated MRI with PF across different datasets and acceleration factors. Experimental results demonstrate that our proposed method improves reconstruction quality compared with a conventional complex-valued PD-DL baseline, yielding sharper images and reduced artifacts. Code available at: https://github.com/MahdiSaberii/UMPIRE-Net
