MRI-based mapping of oxygen extraction fraction with QSM and qBOLD is a non-invasive diagnostic tool with many possible applications. But current reconstruction methods based on quasi-Newton (QN) methods are very dependent on accurate parameter initialization. Artificial Neural Networks showed a lot of potential in our previous works. Using a Convolutional Neural Network improves the reconstruction, since neighboring voxels can provide additional information. Using a GESFIDE sequence to sample the qBOLD signal instead of a standard mGRE that samples only the FID, improves the reconstruction accuracy of R2, Y and χnb a lot.