Evidence map›Paper›PMID 27796600›Full record

ArticleSurgical endoscopy2017

Robust augmented reality registration method for localization of solid organs' tumors using CT-derived virtual biomechanical model and fluorescent fiducials.

Seong-Ho Kong, Nazim Haouchine, Renato Soares, Andrey Klymchenko, Bohdan Andreiuk, Bruno Marques, Galyna Shabat, Thierry Piechaud, Michele Diana, Stéphane Cotin and 1 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.1field-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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Pooled it
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  7. A deep learning framework for real-time 3D model registration in robot-assisted laparoscopic surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2022
    Article
  8. Review
  9. Augmented reality technology in image-guided therapy: State-of-the-art review.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2021
    Review
  10. Article
  11. Laparoscopic augmented reality registration for oncological resection site repair.International journal of computer assisted radiology and surgery · 2021
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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

11 authors at 6 institutions in 2 countries.

Seong-Ho KongIHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.
Nazim HaouchineInstitut national de recherche en informatique et en automatique (INRIA) Mimesis, Strasbourg, France.
Renato SoaresIHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.
Andrey KlymchenkoBiophotonic and Pharmacology Lab, UMR 7213 CNRS, Pharmacological Faculty, University of Strasbourg, Strasbourg, France.
Bohdan AndreiukBiophotonic and Pharmacology Lab, UMR 7213 CNRS, Pharmacological Faculty, University of Strasbourg, Strasbourg, France.
Bruno MarquesInstitut national de recherche en informatique et en automatique (INRIA) Mimesis, Strasbourg, France.
Galyna ShabatIRCAD, Research Institute against Cancer of the Digestive System, 1, Place de l'Hôpital, 67091, Strasbourg, France.
Thierry PiechaudDivision of Urology, Clinique Saint-Augustin, Bordeaux, France.
Michele DianaIHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France. michele.diana@ircad.fr.ORCID 0000-0002-1390-8486
Stéphane CotinInstitut national de recherche en informatique et en automatique (INRIA) Mimesis, Strasbourg, France.
Jacques MarescauxIHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.
Institut de Recherche contre les Cancers de l’Appareil Digestif · FRInstitut national de recherche en sciences et technologies du numérique · FRCentre National de la Recherche Scientifique · FRClinique du Sport · FRInstitut de Chirurgie Guidée par l'Image · FRSeoul National University Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAugmented reality (AR) is the fusion of computer-generated and real-time images. AR can be used in surgery as a navigation tool, by creating a patient-specific virtual model through 3D software manipulation of DICOM imaging (e.g., CT scan). The virtual model can be superimposed to real-time images enabling transparency visualization of internal anatomy and accurate localization of tumors. However, the 3D model is rigid and does not take into account inner structures' deformations. We present a concept of automated AR registration, while the organs undergo deformation during surgical manipulation, based on finite element modeling (FEM) coupled with optical imaging of fluorescent surface fiducials.

methodsTwo 10 × 1 mm wires (pseudo-tumors) and six 10 × 0.9 mm fluorescent fiducials were placed in ex vivo porcine kidneys (n = 10). Biomechanical FEM-based models were generated from CT scan. Kidneys were deformed and the shape changes were identified by tracking the fiducials, using a near-infrared optical system. The changes were registered automatically with the virtual model, which was deformed accordingly. Accuracy of prediction of pseudo-tumors' location was evaluated with a CT scan in the deformed status (ground truth). In vivo: fluorescent fiducials were inserted under ultrasound guidance in the kidney of one pig, followed by a CT scan. The FEM-based virtual model was superimposed on laparoscopic images by automatic registration of the fiducials.

resultsBiomechanical models were successfully generated and accurately superimposed on optical images. The mean measured distance between the estimated tumor by biomechanical propagation and the scanned tumor (ground truth) was 0.84 ± 0.42 mm. All fiducials were successfully placed in in vivo kidney and well visualized in near-infrared mode enabling accurate automatic registration of the virtual model on the laparoscopic images.

conclusionsOur preliminary experiments showed the potential of a biomechanical model with fluorescent fiducials to propagate the deformation of solid organs' surface to their inner structures including tumors with good accuracy and automatized robust tracking.

Indexed as

Fiducial MarkersTomography, X-Ray ComputedVirtual RealityAnimalsBiomechanical PhenomenaFinite Element AnalysisFluorescent DyesImaging, Three-DimensionalIn Vitro TechniquesKidneyLaparoscopyModels, AnatomicNeoplasmsSurgery, Computer-AssistedSwineFluorescent DyesAugmented realityAutomatic registrationFiducialsFinite element modelingFluorescence-guided surgeryOptical imagingSolid organ tumor

Identifiers

PMID27796600
OpenAlexW2541905211

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.