Keywords: Machine Learning/Artificial Intelligence, BrainWhile gadolinium-based contrast agents are necessary to generate a quantitative mapping of brain metabolism, they are invasive with unclear long-term side-effects. As such, convolutional neural networks (CNNs) have been explored as a method to generate artificial cerebral blood volume (aCBV) maps from T1W structural MRI scans. However, prior implementations process MRI in 2D slices, severely limiting output resolution, production time, and utility. In this study, we propose a 3D CNN-Transformer hybrid aCBV generation tool that outperforms both 2D and 3D implementations of the prior state-of-the-art model (PSNR: 29.46, P.R.: 0.836, SSIM: 0.875, S.R.: 0.681).
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