Evidence map›Paper›PMID 35368731›Full record

ArticleIEEE transactions on medical robotics and bionics2022

Pre-Clinical Development of Robot-Assisted Ventriculoscopy for 3D Image Reconstruction and Guidance of Deep Brain Neurosurgery.

Prasad Vagdargi, Ali Uneri, Craig K Jones, Pengwei Wu, Runze Han, Mark G Luciano, William S Anderson, Patrick A Helm, Gregory D Hager, Jeffrey H Siewerdsen

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on medical robotics and bionics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Real-Time 3-D Video Reconstruction for Guidance of Transventricular Neurosurgery.IEEE transactions on medical robotics and bionics · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Prasad VagdargiDepartment of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA.
Ali UneriDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Craig K JonesMalone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD USA.
Pengwei WuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Runze HanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Mark G LucianoDepartment of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD, USA.
William S AndersonDepartment of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD, USA.
Patrick A HelmMedtronic, Littleton MA, USA.
Gregory D HagerDepartment of Computer Science, Johns Hopkins University, Baltimore, MD 21218 USA.
Jeffrey H SiewerdsenDepartment of Biomedical Engineering and Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins University · USMedtronic (United States) · US

Funding

Imaging, Guidance, and QA for Emerging High-Precision Neurosurgical TechniquesU01NS107133 · NINDS · JOHNS HOPKINS UNIVERSITY · PI SIEWERDSEN, JEFFREY H, UNERI, ALI · 2019 to 2023
$3.3M
NINDS NIH HHS U01 NS107133
6 · The paper itself

Abstract

Conventional neuro-navigation can be challenged in targeting deep brain structures via transventricular neuroendoscopy due to unresolved geometric error following soft-tissue deformation. Current robot-assisted endoscopy techniques are fairly limited, primarily serving to planned trajectories and provide a stable scope holder. We report the implementation of a robot-assisted ventriculoscopy (RAV) system for 3D reconstruction, registration, and augmentation of the neuroendoscopic scene with intraoperative imaging, enabling guidance even in the presence of tissue deformation and providing visualization of structures beyond the endoscopic field-of-view. Phantom studies were performed to quantitatively evaluate image sampling requirements, registration accuracy, and computational runtime for two reconstruction methods and a variety of clinically relevant ventriculoscope trajectories. A median target registration error of 1.2 mm was achieved with an update rate of 2.34 frames per second, validating the RAV concept and motivating translation to future clinical studies.

Indexed as

augmented realitycomputer visionImage-guided surgeryintraoperative imagingneurosurgeryventriculoscopy

Identifiers

PMID35368731
PMCPMC8967072
OpenAlexW3213453253

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.