Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI
This work addresses the need for more precise cerebrovascular diagnostics in medical imaging, representing an incremental improvement with a novel hybrid method.
The paper tackled the problem of low spatiotemporal resolution and noise in magnetic resonance flow imaging for aneurysm hemodynamic analysis by proposing the Localized Fourier Neural Operator (LoFNO), which achieved superior velocity and wall shear stress predictions compared to interpolation and other deep learning methods.
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.