# Non-invasive neurosurgical planning with Random Matrix Theory MRI

> **NIH NIH R41** · MICROSTRUCTURE IMAGING, INC. · 2022 · $55,000

## Abstract

PROJECT SUMMARY
This application is intended for I-CORPs at NIH.
Summary of the associated NIH NCI STTR Phase I
About 23,830 people in the US are diagnosed per year with primary malignant brain tumors, and 200,000-
300,000 with metastatic brain tumors (10-30% of all cancers). Maximizing surgical resection of tumor is a major
predictor of survival, but must be balanced against the risk of injuring eloquent white matter and cortical regions.
To improve outcomes, the unmet need is to radically increase quality of noninvasive preoperative brain mapping.
As the brain mapping gold standard, MRI offers unique soft-tissue contrast, anatomical and functional information
of the brain, yet is inherently signal-to-noise ratio (SNR)-starved. The majority of brain mapping relies on diffusion
(dMRI) and functional (fMRI), which are both especially severely limited by SNR.
The MRI signal can be increased with higher-field; however, scanner prices scale with the field strength: 1.5T ~
$1.5M, 7T ~ $7M, as do installation and service costs. Since 90% of the MRIs in the US are 1.5T or below, it
appears that the majority of hospitals cannot justify or afford high field MRI. SNR increase by the signal averaging
is impractical from the scan time perspective, as brain tumor patients rarely tolerate scan times above 45 min.
Our company, Microstructure Imaging (MICSI), is an award-winning New York University (NYU) spinoff that
offers a software-as-a-service for medical image processing. Our product dramatically enhances the SNR of MRI
brain mapping, which translates into increased resolution, image quality, sensitivity and specificity.
Here we employ random matrix theory (RMT) to achieve an order-of-magnitude gain in SNR purely in software
at the image reconstruction level, by utilizing the information across multiple radiofrequency coils and MRI
contrasts within a single protocol. Our overarching goal is to optimize our RMT/MP-PCA image reconstruction
algorithm for the clinical translation in brain mapping preoperative studies. Our Specific Aims are:
Aim 1: Enabling lower field / higher resolution. We will develop and evaluate a multimodal (dMRI/fMRI) RMT
denoising and reconstruction protocol in 6 volunteers on 1.5T and 3T with different image resolutions, and
retrospectively in 30 preoperative brain mapping MRI patients. This data will be used to justify prospectively
altering clinical MRI protocols during the anticipated Phase II of the STTR.
Aim 2: Clinical feasibility study. 15 minutes of additional scan time for dMRI and 2 fMRI tasks at 1.2 mm
isotropic resolution will be prospectively added to 10 brain mapping cases at 3T. The image quality with and
without denoising will be assessed quantitatively, and qualitatively by radiologists and neurosurgeons.
While the Phase-I STTR will optimize RMT in preoperative planning for brain tumors, in the future we will optimize
protocols for any tumor type or location by joint RMT reconstruction of variety of MRI modalities (per...

## Key facts

- **NIH application ID:** 10541655
- **Project number:** 3R41CA257624-01A1S1
- **Recipient organization:** MICROSTRUCTURE IMAGING, INC.
- **Principal Investigator:** Grigoriy Lemberskiy
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $55,000
- **Award type:** 3
- **Project period:** 2022-02-28 → 2022-04-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10541655

## Citation

> US National Institutes of Health, RePORTER application 10541655, Non-invasive neurosurgical planning with Random Matrix Theory MRI (3R41CA257624-01A1S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10541655. Licensed CC0.

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