PROJECT ABSTRACT MRI is the gold standard for diagnosis, staging, and assessment of treatment response in pediatric brain tumors. Neuro-oncology MRI protocols often take more than 20 minutes, and this prolonged duration makes it difficult for pediatric subjects to keep motionless. Sedation is used to ensure immobility but has profound implications. For the patients, sedation carries risks of acute anesthetic complications and the potential for adverse neurocognitive effects in the long term. For the healthcare system, sedation increases the wait time for exams and triples the cost. Recent studies have raised concerns about the deposition of Gadolinium-based contrast agents (GBCAs) in the brain and body, regardless of renal function. Due to the long life span of children and the high burden of GBCA exposure in oncology patients, children with cancer constitute a particularly vulnerable population and are at risk for developing long-term adverse effects that might not become manifest in other populations. Quantitative MRI (qMRI) is a technique that can measure biophysical tissue parameters, such as T1 and T2, reproducibly across different MRI systems and provide standard contrast-weighting images synthesized from the measured parameter maps. However, the speed limitations of qMRI, the sensitivity to motion degradation, and partial volume effects have hindered its clinical adoption. This project aims to address unmet needs in pediatric tumor imaging by developing a rapid and motion- robust qMRI acquisition. This proposal aims (i) to develop cutting-edge technologies for rapid, motion-robust, and quantitative scans that reduce the need and the amount of sedation or rescan, thus mitigating the long-term cognitive effects of sedatives in children, (ii) to synthesize clinically desired full-dose post-contrast images from the measured biophysical tissue parameters acquired from a low-dose qMRI acquisition. The developed technology will be clinically translated and deployed in children with brain tumors. The developed techniques will be rigorously analyzed by (i) assessing the improvements in image quality of the motion-corrected sequences on non-sedated children and (ii) evaluating the diagnostic/clinical equivalency of the synthetic and post-contrast images on children with brain tumors. All developed technology will be disseminated by employing the customer-to-producer (C2P) software package and the framework for image reconstruction environments (FIRE) package, provided by Siemens Healthineers, for running the developed techniques on clinical scanners and helping effectively disseminate to other institutes.