3D Artificial Cerebral Blood Volume Generation from T1W Structural MRI

While 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).