发表机构
University of Bern（伯尔尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结合多尺度特征提取与卷积块注意力模块（CBAM）的深度学习超分辨率框架，用于提升颈动脉4D血流MRI的质量，可降低RMSE并改善复杂血流模式重建，扩展其临床实用性。
AI 中文摘要
四维（4D）血流磁共振成像（MRI）是一种强大的非侵入性技术，可用于在体可视化和量化复杂的血流模式。尽管其具有临床应用前景，但其更广泛的应用受到低空间分辨率和噪声敏感性的限制，这些问题会影响对壁面剪切应力、压力梯度和湍动能等关键血流动力学生物标志物的准确评估。为克服这些挑战，我们提出一种基于深度学习的超分辨率框架，该框架整合多尺度特征提取和注意力机制以提升4D血流MRI数据的质量。该模型在包含120例患者、240条狭窄颈动脉的数据集上进行训练，高分辨率真值数据通过基于分割血管几何结构和生理真实边界条件的患者特异性计算流体动力学（CFD）模拟生成，所得速度场作为监督学习的目标。所提出的架构使用卷积块注意力模块（CBAM）引导网络关注临床相关的空间特征，并抑制低分辨率输入中的噪声。定量结果显示，与不含注意力机制的基线模型相比，该注意力引导模型显著降低了均方根误差（RMSE），定性速度轮廓分析也证实其对复杂血流模式的重建效果得到改善。这些发现凸显了该模型在噪声条件下恢复高保真流场的能力，并支持利用深度学习扩展4D血流MRI在非侵入性血流动力学评估中的临床实用性。
英文摘要
Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizing and quantifying complex blood flow patterns in vivo. Despite its clinical promise, broader adoption is limited by low spatial resolution and sensitivity to noise, which restrict accurate assessment of critical hemodynamic biomarkers such as wall shear stress, pressure gradients, and turbulent kinetic energy. To overcome these challenges, we propose a deep learning-based super-resolution framework that integrates multi-scale feature extraction and attention mechanisms to enhance the quality of 4D flow MRI data. The model was trained on a dataset of 120 patients with 240 stenosed carotid arteries. High-resolution ground truth data were generated using patient-specific computational fluid dynamics (CFD) simulations based on segmented vascular geometries and physiologically realistic boundary conditions, and the resulting velocity fields served as targets for supervised learning. The proposed architecture uses convolutional block attention modules (CBAM) to guide the network toward clinically relevant spatial features and to suppress noise in low-resolution inputs. Quantitative results show that the attention-guided model substantially reduces the root mean square error (RMSE) compared with a baseline model without attention, and qualitative velocity contour analysis confirms improved reconstruction of intricate flow patterns. These findings highlight the capacity of the model to restore high-fidelity flow fields under noisy conditions and support the use of deep learning to extend the clinical utility of 4D flow MRI for non-invasive hemodynamic assessment.
