FlowMoDL：结合共轭梯度数据一致性的基于模型的深度学习用于高度加速的4D血流MRI重建

中文摘要 AI 辅助

我们提出FlowMoDL，一种用于高度加速4D血流MRI重建的展开式神经网络，可直接优化解剖结构幅度和相位衍生的速度精度。该模型基于MoDL框架，将学习到的(3+1)D时空去噪器与基于SENSE前向模型的共轭梯度数据一致性更新交替进行。一种新颖的双通路条件方案可调整去噪器特征和数据一致性权重，使单个模型能处理10倍至50倍的不同加速因子。为确保生理精度，网络采用深度监督复合损失函数进行训练，该损失函数明确惩罚速度幅度和角度误差，并通过课程学习计划稳定训练过程。我们在多中心CMRx4DFlow数据集上对FlowMoDL进行评估，对比经典方法和深度学习基线方法（CG-SENSE、MoDL、FlowVN和FlowMRI-Net）。FlowMoDL的关键优势在于其卓越的梯度步骤效率：在梯度步骤数量有限且预算相当的情况下，同类竞争的血流专用网络会显著退化，而FlowMoDL能稳健收敛，在所有加速因子下，幅度SSIM、nRMSE、相对速度误差和角度误差均严格优于所有竞争对手，成功恢复清晰的结构细节和时间连贯的速度场。

英文摘要

We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude and phase-derived velocity accuracy. Building on the MoDL framework, FlowMoDL alternates a learned (3+1)D spatiotemporal denoiser with conjugate-gradient data-consistency updates based on the SENSE forward model. A novel dual-pathway conditioning scheme adapts the denoiser features and data-consistency weighting, enabling a single model to handle varying acceleration factors ($10\times$ to $50\times$). To ensure physiological accuracy, the network is trained using a deep-supervision composite loss that explicitly penalizes velocity magnitude and angular errors, stabilized by a curriculum schedule. We evaluate FlowMoDL on the multi-center CMRx4DFlow dataset against classical and deep-learning baselines (CG-SENSE, MoDL, FlowVN, and FlowMRI-Net). A key advantage of FlowMoDL is its superior gradient step efficiency. When evaluated under an equivalent, limited budget of gradient steps, competing flow-specific networks degrade significantly. In contrast, FlowMoDL robustly converges and strictly outperforms all competitors across all acceleration factors in magnitude SSIM, nRMSE, relative velocity error, and angular error, successfully recovering sharp structural details and temporally coherent velocity fields.

发表机构

• Leibniz University Hannover（莱布尼茨汉诺威大学）
• Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）
• University Medical Center of the Johannes Gutenberg University Mainz（美因茨约翰内斯·古腾堡大学医学中心）
• L3S（L3S研究所）
• CAIMED（CAIMED机构）
• Institute of Information Processing (tnt)（信息处理研究所（tnt））
• Institute of Medical Biostatistics, Epidemiology and Informatics（医学生物统计学、流行病学与信息学研究所）

机构由 AI 辅助整理，请以论文原文为准。