# ADVANCED COMPREHENSIVE MAGNETIC RESONANCE SOLUTION FOR THE NONINVASIVE CHARACTERIZATION OF HIGH RESOLUTION METABOLIC BIOMARKERS OF RISK IN PATIENTS WITH ALZHEIMER'S DISEASE AND DEMENTIA

> **NIH NIH R44** · ADVANCED IMAGING RESEARCH, INC. · 2024 · $1,275,000

## Abstract

NIH SBIR Fast-Track, PAS-22-196 Advanced Comprehensive Magnetic Resonance Solution for the Non Invasive Characterization of
No Clinical Trials High Resolution Metabolic Biomarkers of Risk in Patients with Alzheimer’s Disease and Dementia
Project Summary
This project addresses the Advancing Research on Alzheimer's Disease (AD) and AD-Related Dementias
(ADRD) Initiative of the National Institute on Aging (PAS-22-196). We propose to develop an advanced
hardware-software package, ADRD360, to optimally acquire, process, and analyze magnetic resonance
imaging (MRI) and spectroscopy (MRS) in the human brain. The ADRD360 will help to localize metabolic
signatures and obtain MRI and MRS brain data at much higher resolutions than currently possible on
clinical MR systems. Highly resolved MRI and MRS will be of utmost importance to aid in the early
prediction of the risk of developing AD. The ADRD360 may lead to the early detection of structural and
metabolic parameters as predictive diagnostic tool biomarkers in patients with AD, ADRD, other
neurological disorders and psychiatric diseases. This project will offer a complete solution with the
development and optimization of 1) advanced radio-frequency (RF) coils approaching ultimate intrinsic
signal-to-noises (UISNRs) and acceptable specific absorption rates (SARs); 2) pushing structural MR
imaging resolutions over commercial devices; 3) advancing shimming procedures to improve the
homogeneity of the B0 field; 4) permitting high-resolution, multi-dimensional, multi-volume MRS exams
with 100% structure-function correlation in reasonable scan times; 5) a stand-alone, easy-to-use program
that provides the crucial link between multi-dimensional structural and multi-volume MRS acquisition,
including its clinical and metabolic interpretation by effectively tackling the demanding spectral processing
and analysis needs; and 6) a comprehensive MRI-MRS metrics system calibration, quality assurance and
validation phantom essential to confirm reproducibility of measurements. Initially, we will demonstrate the
research utility and clinical applicability by determining the structural and metabolic MRI-MRS parameter
spread in healthy subjects. Results from this study will become essential to interpretation in subjects at
higher risk of developing AD. This work builds upon our expertise in creating the first commercial dual-
tuned 1H-31P 1.5T and 3T head coils (R44NS037273). This research also builds upon our work with the
development of an automated B0 shimming program (FWHM 11.7 ± 1.9 Hz, n=94 at 1.5T) and the
interactive MRS software. This project will offer 'A Complete Solution for the Early AD Detection’ with
advanced brain MR assessments in the high-resolution structural and functional MRI exams, and
multinuclear metabolic-based MRS acquisitions. Our licensed Siemens vendor agreement will propel the
development and commercialization of the ADRD360. We aim to satiate the researchers by approaching
the ultimate intrins...

## Key facts

- **NIH application ID:** 11130773
- **Project number:** 4R44AG085817-02
- **Recipient organization:** ADVANCED IMAGING RESEARCH, INC.
- **Principal Investigator:** Ravi Srinivasan
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,275,000
- **Award type:** 4N
- **Project period:** 2023-09-20 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11130773

## Citation

> US National Institutes of Health, RePORTER application 11130773, ADVANCED COMPREHENSIVE MAGNETIC RESONANCE SOLUTION FOR THE NONINVASIVE CHARACTERIZATION OF HIGH RESOLUTION METABOLIC BIOMARKERS OF RISK IN PATIENTS WITH ALZHEIMER'S DISEASE AND DEMENTIA (4R44AG085817-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11130773. Licensed CC0.

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