Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower Grade Gliomas Using 3D Fully Convolutional Neural Networks

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MRI generated from precontrast MR images in patients with brain gliomas. In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast series in 400 patients (mean age 57 years; 239 men; from 2015–2020), including 332 with glioblastoma and 68 with lower-grade gliomas.