Evidence map›Paper›PMID 38072667›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Interactive Multi-Stage Robotic Positioner for Intra-Operative MRI-Guided Stereotactic Neurosurgery.

Zhuoliang He, Jing Dai, Justin Di-Lang Ho, Hon-Sing Tong, Xiaomei Wang, Ge Fang, Liyuan Liang, Chim-Lee Cheung, Ziyan Guo, Hing-Chiu Chang and 5 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

15 authors.

Zhuoliang HeDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.ORCID 0000-0003-0683-1772
Jing DaiDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Justin Di-Lang HoDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Hon-Sing TongDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Xiaomei WangDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Ge FangDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Liyuan LiangDepartment of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Chim-Lee CheungDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.
Ziyan GuoDepartment of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
Hing-Chiu ChangDepartment of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Iulian IordachitaDepartment of Mechanical Engineering and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA.
Russell H TaylorDepartment of Computer Science and Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA.
Wai-Sang PoonDivision of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Danny Tat-Ming ChanDivision of Neurosurgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, 999077, China.
Ka-Wai KwokDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong, 999077, China.ORCID 0000-0003-1879-9730

Funding

Innovation and Technology Commission - Hong Kong MRP/029/20XMulti-Scale Medical Robotics Center Ltd.Research Grants Council of Hong Kong 17 205 919Research Grants Council of Hong Kong 17 207 020Research Grants Council of Hong Kong 17 209 021
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) demonstrates clear advantages over other imaging modalities in neurosurgery with its ability to delineate critical neurovascular structures and cancerous tissue in high-resolution 3D anatomical roadmaps. However, its application has been limited to interventions performed based on static pre/post-operative imaging, where errors accrue from stereotactic frame setup, image registration, and brain shift. To leverage the powerful intra-operative functions of MRI, e.g., instrument tracking, monitoring of physiological changes and tissue temperature in MRI-guided bilateral stereotactic neurosurgery, a multi-stage robotic positioner is proposed. The system positions cannula/needle instruments using a lightweight (203 g) and compact (Ø97 × 81 mm) skull-mounted structure that fits within most standard imaging head coils. With optimized design in soft robotics, the system operates in two stages: i) manual coarse adjustment performed interactively by the surgeon (workspace of ±30°), ii) automatic fine adjustment with precise (<0.2° orientation error), responsive (1.4 Hz bandwidth), and high-resolution (0.058°) soft robotic positioning. Orientation locking provides sufficient transmission stiffness (4.07 N/mm) for instrument advancement. The system's clinical workflow and accuracy is validated with lab-based (<0.8 mm) and MRI-based testing on skull phantoms (<1.7 mm) and a cadaver subject (<2.2 mm). Custom-made wireless omni-directional tracking markers facilitated robot registration under MRI.

Indexed as

NeurosurgeryRoboticsBrainMagnetic Resonance ImagingNeurosurgical Proceduresframeless stereotaxisMRI-guided interventionssoft roboticsstereotactic neurosurgerysurgical robotics

Identifiers

PMID38072667
PMCPMC10870025

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.