Brain cancer screening utilizing MRI suffers from a bottleneck by requiring 3D acquisitions before and after contrast injection. We designed a novel two-stage deep learning reconstruction pipeline to accelerate 3D brain MRI over conventional 2-fold parallel imaging accelerations used in clinical practice. Using modular deep neural networks, we removed the dependence on one all-encompassing network. We successfully dealiased brain images at higher accelerations and with structural fidelity in lesions superior to conventional clinical imaging. Our method was validated on pathologies unseen during training using qualitative evaluation from an expert neuroradiologist and achieved comparable scores to conventional 2-fold clinical accelerations.