Evidence map›Paper›PMID 28391583›Full record

ArticleInternational journal of computer assisted radiology and surgery2017

3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation.

Marco Riva, Christoph Hennersperger, Fausto Milletari, Amin Katouzian, Federico Pessina, Benjamin Gutierrez-Becker, Antonella Castellano, Nassir Navab, Lorenzo Bello

Open access · greenAbstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 8% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

  1. Pooled it
  2. DDiagnostics (Basel, Switzerland) · 2024
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  6. GliomaCancers · 2021
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  7. Review
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  11. Review
  12. Using the variogram for vector outlier screening: application to feature-based image registration.International journal of computer assisted radiology and surgery · 2018
    Review
  13. Review
  14. Article
  15. Article
  16. Functional MRI for Surgery of Gliomas.Current treatment options in neurology · 2017
    Review
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

9 authors at 6 institutions in 3 countries.

Marco RivaDepartment of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Vanvitelli 32, Milan, Italy.
Christoph HennerspergerComputer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany. christoph.hennersperger@tum.de.ORCID http://orcid.org/0000-0003-3810-9856
Fausto MilletariComputer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany.
Amin KatouzianComputer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany.
Federico PessinaUnit of Surgical Neuro-Oncology, Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan, Italy.
Benjamin Gutierrez-BeckerComputer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany.
Antonella CastellanoNeuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.
Nassir NavabComputer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany.
Lorenzo BelloUnit of Surgical Neuro-Oncology, Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan, Italy.
Technical University of Munich · DEUniversity of Milan · ITIBM Research - Almaden · USIRCCS Humanitas Research Hospital · ITJohns Hopkins University · USVita-Salute San Raffaele University · IT

Funding

EU FP7 270460H2020 Societal Challenges 688279
6 · The paper itself

Abstract

backgroundBrainshift is still a major issue in neuronavigation. Incorporating intra-operative ultrasound (iUS) with advanced registration algorithms within the surgical workflow is regarded as a promising approach for a better understanding and management of brainshift. This work is intended to (1) provide three-dimensional (3D) ultrasound reconstructions specifically for brain imaging in order to detect brainshift observed intra-operatively, (2) evaluate a novel iterative intra-operative ultrasound-based deformation correction framework, and (3) validate the performance of the proposed image-registration-based deformation estimation in a clinical environment.

methodsEight patients with brain tumors undergoing surgical resection are enrolled in this study. For each patient, a 3D freehand iUS system is employed in combination with an intra-operative navigation (iNav) system, and intra-operative ultrasound data are acquired at three timepoints during surgery. On this foundation, we present a novel resolution-preserving 3D ultrasound reconstruction, as well as a framework to detect brainshift through iterative registration of iUS images. To validate the system, the target registration error (TRE) is evaluated for each patient, and both rigid and elastic registration algorithms are analyzed.

resultsThe mean TRE based on 3D-iUS improves significantly using the proposed brainshift compensation compared to neuronavigation (iNav) before (2.7 vs. 5.9 mm; [Formula: see text]) and after dural opening (4.2 vs. 6.2 mm, [Formula: see text]), but not after resection (6.7 vs. 7.5 mm; [Formula: see text]). iUS depicts a significant ([Formula: see text]) dynamic spatial brainshift throughout the three timepoints. Accuracy of registration can be improved through rigid and elastic registrations by 29.2 and 33.3%, respectively, after dural opening, and by 5.2 and 0.4%, after resection.

conclusion3D-iUS systems can improve the detection of brainshift and significantly increase the accuracy of the navigation in a real scenario. 3D-iUS can thus be regarded as a robust, reliable, and feasible technology to enhance neuronavigation.

Indexed as

AlgorithmsAdultAgedBrainBrain NeoplasmsFemaleHumansImaging, Three-DimensionalMaleMiddle AgedNeuronavigationSurgery, Computer-AssistedUltrasonography3D imagingBrainshiftBrain tumorClinical evaluationCompensationIntra-operative imagingIterative registrationSonographyUltrasound imaging

Identifiers

PMID28391583
OpenAlexW2605253965

What OpenQuestion holds

Textmetadata
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.