# Image based registration and intraoperative updating for guiding spine surgery

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2020 · $361,988

## Abstract

PROJECT SUMMARY
Image-guidance has not been widely adopted in open lumbar fusion procedures because of cumbersome
patient registration techniques. Accumulated mobility between vertebrae prohibits use of skin-affixed fiducials
(requiring the surgeon to identify, expose, and localize anatomical landmarks within the surgical field), and
registration at the start of surgery does not compensate for intervertebral motion between preoperative supine
CT scans and intraoperative prone patient position. An automated registration procedure could accelerate
adoption and improve outcomes, in addition to reducing costs, complexity, and x-ray dose associated with
spine surgical guidance methods in current clinical use. Our target population is patients with symptomatic
lumbar degenerative spondylolisthesis, where open decompression and fusion surgery, performed more than
300,000 times annually, improves patient-reported outcomes compared to non-surgical treatment. Image-
guidance allows more accurate placement of pedicle screws, which could reduce revision rates and minimize
patient harm, thereby allowing hospitals and surgeons to avoid reimbursement penalties from Medicare and
other payers. We have developed an automated image-based intraoperative stereovision (iSV) to preoperative
CT (pCT) registration that compensates for intervertebral motion, and have successfully applied the technique
in two live animals, achieving excellent target registration errors (TREs less than 2.1 mm). We now propose to
improve technical aspects of the approach in experimental studies and assess feasibility of this novel
registration technology for clinical implementation. Specifically, we will (i) develop a portable iSV scanner as a
radiation-free alternative to intraoperative CT (iCT) or O-arm, (ii) validate, evaluate and optimize the tehcnique
in a systematic series of live animal surgeries, and (iii) translate the technology into a series of human cases of
open lumbar fusion for degenerative spondylolisthesis. Comparisons of the new iSV approach to standard-of-
care image-guidance with a commercial system (e.g., Medtronic StealthStation) and to high-fidelity navigation
achieved with iCT will be performed to establish the relative performance improvements that can be obtained.

## Key facts

- **NIH application ID:** 9976510
- **Project number:** 5R01EB025747-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Sohail K Mirza
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $361,988
- **Award type:** 5
- **Project period:** 2017-09-30 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976510

## Citation

> US National Institutes of Health, RePORTER application 9976510, Image based registration and intraoperative updating for guiding spine surgery (5R01EB025747-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9976510. Licensed CC0.

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