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Quantification of Low-Frequency Oscillations in Cerebrospinal Fluid Dynamics with Rapidly Acquired Tagging MRI and Learning-based Spatiotemporal Pattern Recognition

Award Number: R21EB040046
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/15/2026
PERIOD OF PERFORMANCE END DATE: 06/30/2029

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Quantification of Low-Frequency Oscillations in Cerebrospinal Fluid Dynamics with Rapidly Acquired Tagging MRI and Learning-based Spatiotemporal Pattern Recognition - PROJECT SUMMARY The goal of this project is to develop and validate a noninvasive imaging framework that integrates fast tagging MRI with deep-learning based pattern recognition to quantify low-frequency (0.01–0.2 Hz) cerebrospinal-fluid (CSF) oscillations (LFO-CSF) in the human brain. CSF pulsations support brain health by facilitating glymphatic clearance of metabolic waste, and LFO driven by cerebral vasomotion is a major contributor. However, noninvasive characterization is obscured by higher-frequency cardiac and respiratory activity, and conventional phase-contrast MRI suffers from hardware- and physiology-related phase instabilities. We leverage the displacement sensitivity of balanced steady-state free precession (bSSFP) stop-bands, appearing as dark tags on the images, that integrate CSF motion over ~2 seconds rather than instantaneous velocity, making them well suited for LFO-CSF detection. Separation of LFO-CSF from residual cardiac and respiratory components was achieved using dictionary matching and filters on assumed waveform features, an approach that is feasible but susceptible to inter-subject variability and lacking on accuracy. To fully exploit the displacement-sensitive tagging principle, we propose the following specific aims. Aim 1 increases bSSFP temporal resolution 5-fold via a tiny golden-angle radial trajectory and spatio-temporal sparsity-promoting reconstruction to enrich tagged CSF dynamics; Aim 2 adapts a robust, pre-trained Vision Transformer to cleanly separate LFO-CSF from cardiac and respiratory pulsations while accommodating inter-subject variation (e.g., systolic fraction, breathing patterns); Aim 3 establishes reliability in a test–retest study and demonstrates translational value using an LFO-CSF modulation paradigm combining intermittent deep breathing and caffeine ingestion. Upon completion, we will have developed and validated a rapidly acquired tagging-MRI method and deep-learning-based analysis pipeline for accurate LFO-CSF measurement. This Trailblazer R21 aligns with the NIBIB mission to advance human health through technological innovation, and lays the groundwork for a future R01 to further improve accuracy using realistic ventricular anatomy and to evaluate diagnostic utility in differentiating Alzheimer’s-related mild cognitive impairment from CSF disorders such as normal pressure hydrocephalus that possess shared symptomology.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $194,819 )
2026	2026	THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK	W5510 FRANKS MELVILLE MEMORIAL LIBRARY	STONY BROOK	NY	11794	SUFFOLK	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	8/7/2026	NEW	$194,819
														Subtotal = $194,819

Grand Total All Awards = $194,819

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Quantification of Low-Frequency Oscillations in Cerebrospinal Fluid Dynamics with Rapidly Acquired Tagging MRI and Learning-based Spatiotemporal Pattern Recognition

Award Number: R21EB040046

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/15/2026

PERIOD OF PERFORMANCE END DATE: 06/30/2029

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