Keywords: Analysis/Processing, DSC & DCE Perfusion
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords