# Preoperative Image Updating for Guidance during Brain Tumor Resection

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2022 · $516,624

## Abstract

PROJECT SUMMARY
This project will continue a productive academic-industrial partnership between Dartmouth and Medtronic
through which Dartmouth’s intraoperative image updating techniques have already been merged with Med-
tronic’s state-of-the-art S7 navigation system to produce and visualize updated MR (uMR) images concurrent
with surgery. In a second funding period, we will fully validate and prospectively evaluate uMRs in open-cranial
surgery via comparisons with intraoperative MR (iMR). We also propose a new direction: generating uMR to
guide minimally invasive neurosurgeries as a possible low-cost, yet effective, alternative to in-bore MR-
guidance
(new preliminary results of image-updating in minimally-invasive deep brain stimulation indicate feas-
ibility and suggest promise).
While validation studies completed to date with a tracked stylus as “ground truth”
have been impressive, relatively few points are available at any given time during surgery, and tracking and
feature identification/localization errors are embedded in the TRE (target registration error) results. Thus, a
cornerstone of our continuation proposal is validation with iMR, which is available in Dartmouth’s Center for
Surgical Innovation (CSI). Since iMR is deployed at end-of-resection to survey for residual disease in open
cranial surgery and multiple iMRs are difficult to justify because of patient safety concerns, we have proposed
a new large animal glioma model which has been developed successfully in our hands (we can now grow solid
tumors of varying size and shape located in different intra-cranial positions)
in which iMR will be acquired
multiple times during a resection procedure for uMR validation. These animal studies are also ideally-suited to
minimally-invasive cases because we can determine the image-updating requirements to guide the procedure
as a more efficient and cost-effective alternative to iMR. CSI can accommodate these experiments, and as a
result, we are in a unique position to conduct animal and human studies in the same space with the same
navigation/imaging equipment for uMR validation with iMR under both open cranial and minimally invasive
conditions. Based on progress to date, and these considerations, we propose technical advances that will
apply image-updating to minimally-invasive neurosurgical procedures, accelerate image-updating through GPU
processing, and add uMR data into the surgeon’s heads-up display; validation in large animal glioma open-
cranial and minimally-invasive studies where iMR acquisitions are not limited, and during similar human brain
tumor cases where iMR use is restricted; and prospective evaluation of end-of-resection surgical accuracy of
procedures navigated with preoperative MR (pMR) to those where uMR is also available. By the end of the
proposed 2nd funding period, we will have an uMR guidance platform that is fully validated for open cranial and
minimally-invasive procedures, and will have demonstrated the extent to...

## Key facts

- **NIH application ID:** 10348113
- **Project number:** 5R01CA159324-09
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** KEITH D. PAULSEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $516,624
- **Award type:** 5
- **Project period:** 2011-04-04 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10348113

## Citation

> US National Institutes of Health, RePORTER application 10348113, Preoperative Image Updating for Guidance during Brain Tumor Resection (5R01CA159324-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10348113. Licensed CC0.

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