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AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI) MRI for Early Detection and Monitoring in Multiple Sclerosis

Award Number: K01EB039415
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 07/01/2026
PERIOD OF PERFORMANCE END DATE: 06/30/2030

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AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI) MRI for Early Detection and Monitoring in Multiple Sclerosis - Project Summary/Abstract Progressive multiple sclerosis (PMS) presents unique challenges due to its gradual progression and subtle, diffuse changes in normal-appearing gray and white matter (NAGM, NAWM). While current imaging techniques provide valuable insights, there remains an opportunity to enhance early detection and improve the precision of monitoring PMS pathology. This project introduces an innovative neuroimaging platform that integrates deep learning with Gradient Echo Plural Contrast Imaging (AI-GEPCI) to generate inherently co-registered, multiparametric contrasts from a single, efficient scan. AI-GEPCI is designed to reduce scan times and motion artifacts while enhancing sensitivity to early structural changes by producing clinically relevant contrasts such as R2*, T2*-weighted, FLAIR*, and MP*. FLAIR* and MP* are multiparametric contrasts derived from combining fluid-attenuated inversion recovery (FLAIR) and magnetization-prepared rapid gradient-echo (MPRAGE) sequences with T2*-weighted imaging, respectively. These contrasts facilitate the visualization of conventional lesions alongside advanced markers such as the Central Vein Sign (CVS) and Paramagnetic Rim Lesions (PRLs). When combined with quantitative Gradient Recalled Echo (qGRE) maps sensitive to neuronal injury and demyelination, AI-GEPCI enables a comprehensive and nuanced assessment of PMS pathology that complements existing imaging approaches. Specific aims include: Together, these aims will support the development and validation of an integrated imaging platform designed to enhance PMS detection, monitoring, and personalized care. • Aim 1: Develop an AI-GEPCI toolbox using deep learning to generate a suite of inherently co-registered multiparametric images from a single GEPCI MRI scan, enabling a comprehensive neurological imaging protocol from one acquisition. • Aim 2: Apply AI-GEPCI-generated multiparametric images together with qGRE quantitative maps sensitive to neuronal injury and demyelination to identify, assess, and characterize progressive multiple sclerosis across retrospective, longitudinal, and prospective datasets. This project will establish a unified, efficient imaging platform with sensitivity and specificity to earlier PMS detection and monitoring, supporting personalized care. Its broader translational potential offers promise for application in other neurodegenerative and neuroinflammatory diseases, advancing the goals of precision medicine and improved patient outcomes. Importantly, this research also serves as a structured platform for career development, enabling training in advanced MRI physics, deep learning, and clinical translation, critical skills for my long-term goal of becoming an independent investigator in MS-focused neuroimaging.


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 = $119,818 )
2026	2026	WASHINGTON UNIVERSITY, THE	1 BROOKINGS DR	SAINT LOUIS	MO	63130	SAINT LOUIS	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	6/19/2026	NEW	$119,818
														Subtotal = $119,818

Grand Total All Awards = $119,818

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AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI) MRI for Early Detection and Monitoring in Multiple Sclerosis

Award Number: K01EB039415

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 07/01/2026

PERIOD OF PERFORMANCE END DATE: 06/30/2030

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