用新模型提升脑动脉瘤MRI血流分辨率，直接预测破裂风险
Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI
- 结合几何先验与神经算子，实现不规则血管的精准建模
- 在临床数据上实现速度与壁剪切应力的超分辨率重建
- 适合医学影像分析、计算流体力学与神经介入领域研究者
血流动力学分析对预测动脉瘤破裂和指导治疗至关重要。虽然磁共振血流成像可提供时变三维血流速度测量，但其空间和时间分辨率低、信噪比差，限制了诊断价值。为此，我们提出局部傅里叶神经算子（LoFNO），一种新型3D架构，能够从临床影像数据中直接预测壁剪切应力（WSS），并同时提升时空分辨率。LoFNO引入拉普拉斯特征向量作为几何先验，增强对不规则、未见几何结构的结构感知能力，并采用增强型深度超分辨率网络（EDSR）层实现鲁棒的上采样。通过将几何先验与神经算子框架结合，LoFNO有效降噪并实现时空上采样，相比插值法和其他深度学习方法，在速度和壁剪切应力预测上表现更优，显著提升了脑血管诊断精度。
原文摘要 · Abstract (English)
Hemodynamic analysis is essential for predicting aneurysm rupture and guiding treatment. While magnetic resonance flow imaging enables time-resolved volumetric blood velocity measurements, its low spatiotemporal resolution and signal-to-noise ratio limit its diagnostic utility. To address this, we propose the Localized Fourier Neural Operator (LoFNO), a novel 3D architecture that enhances both spatial and temporal resolution with the ability to predict wall shear stress (WSS) directly from clinical imaging data. LoFNO integrates Laplacian eigenvectors as geometric priors for improved structural awareness on irregular, unseen geometries and employs an Enhanced Deep Super-Resolution Network (EDSR) layer for robust upsampling. By combining geometric priors with neural operator frameworks, LoFNO de-noises and spatiotemporally upsamples flow data, achieving superior velocity and WSS predictions compared to interpolation and alternative deep learning methods, enabling more precise cerebrovascular diagnostics.
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