PROJECT SUMMARY The goal of this work is to develop and evaluate a novel strategy for multi-contrast imaging and automatic lesion segmentation of gliomas that will significantly impact clinical imaging workflow and enable robust measures for monitoring therapeutic response. The resulting images, quantitative maps, and segmented regions will have 1mm isotropic resolution, full brain coverage, in 6 minutes scan time, facilitating their routine use in providing objective criteria for response assessment and revealing subtle changes in lesion growth over time that can be missed by visual assessment or measurements of cross-sectional tumor diameter. Besides generating quantitative T1, T2, and macro-molecular proton fraction maps along with conventional T2-, FLAIR-, and T1- weighted images, our automatic segmentation of regions that correspond to contrast-enhancing and T2- hyperinense lesions will be performed without the injection of a gadolinium-based contrast agent. Our strategy involves using a highly accelerated 3D MRI sequence with multiple inversion pulses to achieve whole brain, multi-contrast imaging by continuously acquiring data during incomplete inversion recovery with balanced steady state free precession. This unique approach overcomes the limitations inherent in conventional anatomical imaging that acquire data during a limited window and require full inversion recovery by incorporating dictionary searching that is used in the MR fingerprinting approach. Our recent studies demonstrate the potential of this sequence in imaging patients with brain tumors by taking advantage of the added contrasts to automatically segment the contrast-enhancing lesion, infiltrative tumor, and edema, as well as highlight the need for further refinement of parameters and evaluation in patients. Specifically, in Aim 1 we will develop and evaluate: 1) quantitative multi-parametric mapping based on a multiple-compartment model comprised of water and macromolecular proton pools and includes magnetization transfer effects; 2) a patient-specific automated tissue segmentation that utilizes quantitative tissue T1, T2, and macromolecular proton fraction mapping, 3) using motion compensation to improve tissue and lesion segmentation. The resulting quantitative maps, synthetic images, and tissue segmentations will be evaluated through comparison with their individual references in normal brain tissue and lesions. Aim 2 will then utilize the best set of parameters to evaluate the resulting segmented lesions in patients with enhancing high-grade gliomas. Volumes derived from the automatic segmentation of our multi-contrast scan pre- and post-injection of gadolinium will be compared to manually defined regions of interest in order to determine whether the pre-contrast injection multi-contrast scan can accurately: 1) delineate the contrast-enhancing lesion and 2) separate infiltrative tumor from edema.