Continuous Spatio-Temporal Representation with Implicit Neural Representation and Neural Ordinary Differential Equation in DSC-MRI

Motivation: Dynamic Susceptibility Contrast MRI (DSC-MRI) aids in diagnosing cerebrovascular conditions, but simultaneously achieving high spatial and temporal resolutions is challenging, limiting the capture of detailed perfusion dynamics. Goal(s): To develop a deep learning framework for spatio-temporal super-resolution in DSC-MRI to enhance the capture of perfusion dynamics. Approach: Our proposed model utilizing bi-directional Neural ODE, feature extraction, and a local implicit image function to improve DSC-MRI images and address spatial and temporal resolution constraints. Results: The reconstructed results outperform other methods, with enhanced NMSR, PSNR, and SSIM metrics, providing visual confirmation of accurate MR signal approximation and perfusion parameter calculation. Impact: The spatiotemporal super-resolution of DSC-MRI with deep learning allows for more accurate assessment of perfused tissue dynamics and tumor habitat, as well as more freedom in choosing acquisition weights between spatial and temporal during MRI acquisition.