# Ultrasound based neurosurgical navigation with uncertainty visualization

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $500,689

## Abstract

Surgical resection is the initial treatment for nearly all brain tumors and the extent of resection is strongly correlated with
prognosis. However, because brain tumors, especially gliomas, are intimately involved in surrounding functioning brain
tissue, aggressive resection must be balanced against the risk of causing new neurological deficits. Modern advances in
anatomical and functional imaging and the widespread adoption of neuro-navigation now help neurosurgeons to plan and
execute an optimal surgical approach. Unfortunately, changes in the shape of the brain during surgery, known as brain
shift, invalidate the assumption of all commercial neuro-navigation systems that preoperative data can be mapped to
patient coordinates using rigid registration. Because brain shift progresses during surgery, the rigid registration of neuro-
navigation systems is least accurate at the critical final stages of resection when the marginal tissue is being removed.
There has been more than 20 years of research invested in measuring, modeling and compensating for brain shift with
the goal of providing neuro-navigation systems with an accurate nonrigid registration from preoperative image data to
the patient’s brain in the presence of brain shift. While results are promising, they are not yet accurate enough to be
incorporated into commercial systems. Nonrigid registration is subject to both measurement and modeling uncertainty
that varies throughout 3D space. Most nonrigid registration methods do not attempt to quantify this uncertainty and, to
our knowledge, there have been no attempts to present this uncertainty to the surgeon. We believe that it is important
to make surgeons aware of this uncertainty so that they can make informed decisions, particularly in locations where
uncertainty is high. In this project, we plan to investigate nonrigid registration algorithms that model registration
uncertainty explicitly, semi-automatic and fully-automatic nonrigid registration methods that utilize registration
uncertainty to iteratively guide registration improvements, and visualization paradigms for effective presentation of
registration uncertainty to surgeons in the surgical environment.
We hypothesize that effective representation and visualization of registration uncertainty for brain shift correction in
neuro-navigation will 1) lead to iterative semi-automatic and fully-automatic nonrigid registration methods that improve
registration accuracy and 2) allow neurosurgeons to make more informed decisions during tumor resections that will lead
to increased clinical impact of image-guided neurosurgery. We will carry out the following Aims: 1. Develop novel feature-
based image registration algorithms that represent uncertainty explicitly; 2. Use registration uncertainty maps to guide
semi- and fully-automatic nonrigid registration; 3. Evaluate the utility of nonrigid registration with uncertainty visualization
in a clinical setting.

## Key facts

- **NIH application ID:** 10798215
- **Project number:** 5R01EB032387-03
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** SARAH FRISKEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $500,689
- **Award type:** 5
- **Project period:** 2022-06-02 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798215

## Citation

> US National Institutes of Health, RePORTER application 10798215, Ultrasound based neurosurgical navigation with uncertainty visualization (5R01EB032387-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10798215. Licensed CC0.

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