Evidence map›Paper›PMID 40911919›Full record

ArticleJournal of neurosurgery2026

Fully automated image updating for brain shift compensation after dural opening.

Chengpei Li, Linton T Evans, Jennifer Hong, Scott C Davis, David W Roberts, Keith D Paulsen, Xiaoyao Fan

Abstract read
In one paragraph

Article in Journal of neurosurgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Chengpei Li1Thayer School of Engineering, Dartmouth College, Hanover.
Linton T Evans2Geisel School of Medicine, Dartmouth College, Hanover.
Jennifer Hong2Geisel School of Medicine, Dartmouth College, Hanover.
Scott C Davis1Thayer School of Engineering, Dartmouth College, Hanover.
David W Roberts1Thayer School of Engineering, Dartmouth College, Hanover.
Keith D Paulsen1Thayer School of Engineering, Dartmouth College, Hanover.
Xiaoyao Fan1Thayer School of Engineering, Dartmouth College, Hanover.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
Zhao - Proj 2P20GM130454 · NIGMS · DARTMOUTH COLLEGE · PI MICHAEL L WHITFIELD · 2019 to 2026
$27.2M
Intraoperative Stereovision for Surgical GuidanceR44CA239843 · NCI · INSIGHT SURGICAL TECHNOLOGIES INC. · PI ROBERTS, DAVID W · 2022 to 2024
$1.8M
Exoscope System for Advancing Research in Image and Molecularly Guided SurgeryS10OD032308 · OD · DARTMOUTH COLLEGE · PI PAULSEN, KEITH D. · 2023 to 2023
$1.3M
NCI NIH HHS P30 CA023108NCI NIH HHS R44 CA239843NIGMS NIH HHS P20 GM130454NIH HHS S10 OD032308
6 · The paper itself

Abstract

objectiveIn open cranial procedures, intraoperative brain shift can degrade the accuracy of surgical navigation on the basis of preoperative MR (pMR) images as soon as the cortical surface is exposed. The aim of this study was to develop a fully automated image updating system to address brain shift at the start of open cranial surgery and to evaluate its accuracy and efficiency.

methodsThis study included patients undergoing open cranial procedures at a single center. Intraoperative stereovision (iSV) images of the surgical field were acquired as an easily integrated nondisruptive source of high-resolution image data on surgical surface deformation and were integrated with a computational model to compensate for volumetric brain shift after dural opening by updating the coregistered preoperative images. A Fast Segment Anything Model algorithm segmented the exposed cortical surface on iSV images automatically. Vessel and sulcus features were also segmented automatically from both iSV and pMR images and registered using a two-step registration method. Extracted nonrigid cortical displacements were assimilated by a finite element model to estimate whole-brain deformation. Updated MR (uMR) images were generated by deforming pMR by the resulting displacement field. A tracked stylus sampled the exposed cortical surface to provide independent measurements for error assessments. The uMR images were evaluated in terms of the misfit between model estimates and measured displacements, target registration error (TRE), and point-to-surface distance (PSD) relative to their pMR counterparts.

resultsFifteen patients (age range 45-85 years) who underwent open cranial procedures were included in the study. The overall accuracy of reconstructed iSV surfaces relative to stylus positions was 0.8 ± 0.7 mm. The overall mean misfit, TRE, and PSD of uMR images were 2.1 ± 1.2 mm, 1.9 ± 1.0 mm, and 1.6 ± 1.0 mm, respectively, compared with 6.5 ± 1.3 mm, 6.2 ± 1.2 mm, and 4.5 ± 1.2 mm for pMR images. Image updating was completed automatically without any user intervention in an overall mean of 3.9 ± 0.6 minutes.

conclusionsAutomatic image updating compensated for brain shift due to dural opening and achieved clinically acceptable accuracy and efficiency. The system required no user intervention or expertise and caused minimal interruptions to surgical flow, suggesting it has potential for future integration into open cranial procedures.

Indexed as

BrainDura MaterImage Processing, Computer-AssistedMagnetic Resonance ImagingNeuronavigationNeurosurgical ProceduresAgedAlgorithmsFemaleHumansMaleMiddle Agedautomationbrain shiftimage updatingopen cranial surgerystereovision

Identifiers

PMID40911919
PMCPMC13032198

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.