Quantitative 3D imaging of cerebral blood flow during stroke

Quantitative imaging of cerebral blood flow (CBF) plays a crucial role in preclinical stroke research, enabling the monitoring of treatment effectiveness and providing insights into vascular reorganization and its role in functional recovery. Many optical techniques have been developed to map blood flow due to the high spatial resolution of optical microscopy techniques, but all suffer from limitations. Laser speckle contrast imaging (LSCI) has become a widely used technique due to its simplicity and ability to visualize blood flow in real time without contrast agents. However, LSCI is limited to capturing relative blood flow changes within individual subjects and lacks the ability to compare CBF across subjects or to track longitudinal changes. Additionally, current LSCI methods do not offer depth-resolved imaging. These limitations arise from the inability of LSCI and related technique to capture the full characteristics of the dynamic light scattering signal. Furthermore, the complexity of the three-dimensional vascular network and the heterogeneity of blood flow through that network have prevented quantitative interpretation of measured images. In this proposal we will develop two new advances to laser speckle imaging to enable quantitative blood flow imaging with depth discrimination. First, we will develop a new approach to laser speckle imaging using within exposure laser intensity modulation that will allow direct, full field imaging of the intensity autocorrelation function without any assumptions about the shape or form of the function. This method does not require complex instrumentation or high frame rate cameras. Second, we will create a computational framework to extend the capabilities of surface-based laser speckle imaging to three dimensions and enable depth-resolved blood flow imaging. We will then use these techniques to image chronic cerebral blood flow alterations and vascular remodeling following stroke. The results of this work will provide a new way of imaging blood flow that is free from any assumptions about the form of the autocorrelation functions. Although we focus on cerebral blood flow imaging in mice, our new methodology will be useful in other tissues and eventually in clinical applications as it will serve as a general platform for blood flow imaging. Project Number: 1R01NS144424-01A1 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Neurological Disorders and Stroke (NINDS) | Principal Investigator: Andrew Dunn | Institution: UNIVERSITY OF TEXAS AT AUSTIN, AUSTIN, TX | Award Amount: $541,700 | Activity Code: R01 | Study Section: Imaging Technology Development Study Section[ITD] View on NIH RePORTER: https://reporter.nih.gov/project-details/11367707