# Fingerprinting-Based Neuronal Fiber Identification in Brain Surgery

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $431,667

## Abstract

PROJECT ABSTRACT
When planning brain tumor surgery, neurosurgeons use MRI images to assess the location of tissues affected
by the tumor. A key question in this planning is the location and/or displacement of important neuronal
connections. For visualization of neuronal connections surgeons rely on fiber tractography results derived from
diffusion MRI acquisitions. Current fiber tractography methods however fail to individually detect fiber bundles
crossing at angles less than 40°. This fiber detection limitation hampers the reliability of fiber tractography in
neurosurgery and neuroscience research. Indeed, in neurosurgery applications, tractography struggles with
absent or limited visualization of the lateral corticospinal tract, temporal projections of the arcuate fasciculus
and anterior optic radiations, nerve bundles essential for preservation of respectively motor, language and
visual function.
This proposal is dedicated to the development of novel fiber identification methods for diffusion MRI inspired by
MR Fingerprinting. In fingerprinting approaches diffusion weighted signals are matched to a pre-computed
signal library of potential fiber configurations. Our preliminary data show that fingerprinting-based fiber
identification makes better use of the information available in the diffusion MRI measurement and hence
outperforms current methods. This improved characterization of the diffusion signal will better inform
tractography algorithms on the underlying tissue microstructure, increasing adherence of tractography results
to the biological truth.
The initial goals of the proposal are to establish the fiber identification performance of the proposed method
using simulations, Human Connectome Protocol datasets and a biomimetic hollow fiber phantom in order to
achieve smaller angular resolution and to validate the improved tractography results in an animal model. The
progress made in these initial goals will be employed to retrospectively assess the impact of the proposed
Fingerprinting-based fiber identification on fiber tractography in in vivo brain, the final goal of the proposal.
Attainment of these goals will significantly further the adherence of tractography to tissue microstructure aiding
in the understanding and visualizing of brain structure in both fundamental research and clinical applications.

## Key facts

- **NIH application ID:** 10133652
- **Project number:** 5R01EB028774-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Steven H. Baete
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $431,667
- **Award type:** 5
- **Project period:** 2020-04-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133652

## Citation

> US National Institutes of Health, RePORTER application 10133652, Fingerprinting-Based Neuronal Fiber Identification in Brain Surgery (5R01EB028774-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10133652. Licensed CC0.

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