A single deep learning regression network was trained for unified reconstruction, distortion correction and denoising of T1, T2*, WM, GM and lesion probability maps from MRF-EPI acquisition. The network was trained with binary lesion masks and the WM, GM probability maps generated with SPM. The training T1 and T2* maps were reconstructed using dictionary matching. The relative deviation was 7.6% for the 5 output mask in the whole brain between the proposed deep learning network and the conventional processing. Dice coefficients were 0.85 for WM and GM and 0.67 for the lesions with a lesion detection rate of 0.83.