PROJECT SUMMARY Arterial spin labeled (ASL) perfusion MRI provides noninvasive quantification of tissue blood flow in physiological units of ml/100g/min using magnetic labeling of blood water as an endogenous diffusible flow tracer and is one of the few MRI parameters whose biological basis is known. ASL MRI has primarily been used in the brain to measure cerebral blood flow (CBF), a key physiological parameter that serves a biomarker of cerebrovascular integrity and regional brain function with a broad range of applications in basic and clinical neuroscience research and in clinical care. ASL MRI was originally conceived by our laboratory at the University of Pennsylvania, and we have been responsible for demonstrating many of its technical advances and applications in biomedical research. Although ASL MRI has been translated to clinical use, commercial ASL MRI technologies have failed to keep up with research progress. This Academic Industrial Partnership in response to PAR-18-530 provides dedicated resources to develop, disseminate, and maintain state-of-the-art ASL MRI acquisition and processing technologies for clinical research on the Siemens MRI platform, which represents the most widely used MRI system in neuroscience. An Academic Industrial Partnership is needed because market forces for commercial MRI technologies have been insufficient to drive the development of state-of-the-art ASL MRI capabilities, yet close collaboration between academia and industry are required to deliver a streamlined and capability to users. The proposed supplemental research in response to NOT-AG-23-032 and specifically focuses on optimizing ASL MRI for use in Alzheimer's disease related dementia (ADRD) populations. While a major innovation will be the delivery of an ASL MRI software package featuring state-of-the-art approaches to maximize sensitivity, spatial and temporal resolution, and robustness to artifacts to meet evolving research and clinical requirements for noninvasive quantification of regional cerebral blood flow, next-generation approaches leveraging deep machine learning are also proposed to achieve higher spatial and temporal resolution, faster online image reconstructions, and improved robustness to artifacts than are currently possible. The proposed supplemental research leverages the interdisciplinary expertise of the investigative team to provide a reliable, reproducible, flexible and user friendly technology for quantifying a key parameter of brain health and function in ADRD research. The feasibility of the proposed work is supported by our preliminary data and track record of ASL MRI technology development and dissemination.