BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks

Abstract

Arterial spin labeling (ASL) perfusion MRI and blood-oxygen-level-dependent (BOLD) fMRI provide complementary information for assessing brain functions. ASL is quantitative, insensitive to low-frequency drift but has lower signal-to-noise-ratio (SNR) and lower temporal resolution than BOLD. However, there still lacks a way to fuse the benefits provided by both of them. When only one modality is available, it is also desirable to have a technique that can extract the other modality from the one being acquired. The purpose of this study was to develop such a technique that can combine the advantages of BOLD fMRI and ASL MRI, i.e., to quantify cerebral blood flow (CBF) like ASL MRI but with high SNR and temporal resolution as in BOLD fMRI. We pursued this goal using a new deep learning-based algorithm to extract CBF directly from BOLD fMRI. Using a relatively large dataset containing dual-echo ASL and BOLD images, we built a wide residual learning based convolutional neural network to predict CBF from BOLD fMRI. We dubbed this technique as a BOA-Net (BOLD to ASL networks). Our testing results demonstrated that ASL CBF can be reliably predicted from BOLD fMRI with comparable image quality and higher SNR. We also evaluated BOA-Net with different deep learning networks.