Literature DB >> 12494947

Model-driven brain shift compensation.

Oskar Skrinjar1, Arya Nabavi, James Duncan.   

Abstract

Surgical navigation systems provide the surgeon with a display of preoperative and intraoperative data in the same coordinate system. However, the systems currently in use in neurosurgery are subject to inaccuracy caused by intraoperative brain deformation (brain shift), since they typically assume that the intracranial structures are rigid. Experiments show brain shift of up to 1 cm, making it the dominant error in the system. We propose a biomechanical-model-based approach for brain shift compensation. Two models are presented: a damped spring-mass model and a model based on continuum mechanics. Both models are guided by limited intraoperative (exposed brain) surface data, with the aim to recover the deformation in the full volume. The two models are compared and their advantages and disadvantages discussed. A partial validation using intraoperative MR image sequences indicates that the approach reduces the error caused by brain shift. Copyright 2002 Elsevier Science B.V.

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Year:  2002        PMID: 12494947     DOI: 10.1016/s1361-8415(02)00062-2

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  44 in total

1.  A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation.

Authors:  D-X Zhuang; Y-X Liu; J-S Wu; C-J Yao; Y Mao; C-X Zhang; M-N Wang; W Wang; L-F Zhou
Journal:  AJNR Am J Neuroradiol       Date:  2010-11-18       Impact factor: 3.825

2.  Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

Authors:  Adam Wittek; Grand Joldes; Mathieu Couton; Simon K Warfield; Karol Miller
Journal:  Prog Biophys Mol Biol       Date:  2010-09-22       Impact factor: 3.667

3.  The generation of tetrahedral mesh models for neuroanatomical MRI.

Authors:  Carl Lederman; Anand Joshi; Ivo Dinov; Luminita Vese; Arthur Toga; John Darrell Van Horn
Journal:  Neuroimage       Date:  2010-11-10       Impact factor: 6.556

4.  A fast and efficient method to compensate for brain shift for tumor resection therapies measured between preoperative and postoperative tomograms.

Authors:  Prashanth Dumpuri; Reid C Thompson; Aize Cao; Siyi Ding; Ishita Garg; Benoit M Dawant; Michael I Miga
Journal:  IEEE Trans Biomed Eng       Date:  2010-02-17       Impact factor: 4.538

5.  Robust nonrigid registration to capture brain shift from intraoperative MRI.

Authors:  Olivier Clatz; Hervé Delingette; Ion-Florin Talos; Alexandra J Golby; Ron Kikinis; Ferenc A Jolesz; Nicholas Ayache; Simon K Warfield
Journal:  IEEE Trans Med Imaging       Date:  2005-11       Impact factor: 10.048

6.  A method to track cortical surface deformations using a laser range scanner.

Authors:  Tuhin K Sinha; Benoit M Dawant; Valerie Duay; David M Cash; Robert J Weil; Reid C Thompson; Kyle D Weaver; Michael I Miga
Journal:  IEEE Trans Med Imaging       Date:  2005-06       Impact factor: 10.048

7.  An atlas-based method to compensate for brain shift: preliminary results.

Authors:  Prashanth Dumpuri; Reid C Thompson; Benoit M Dawant; A Cao; Michael I Miga
Journal:  Med Image Anal       Date:  2007-03-01       Impact factor: 8.545

8.  Intraoperative brain shift compensation: accounting for dural septa.

Authors:  Ishita Chen; Aaron M Coffey; Siyi Ding; Prashanth Dumpuri; Benoit M Dawant; Reid C Thompson; Michael I Miga
Journal:  IEEE Trans Biomed Eng       Date:  2010-11-22       Impact factor: 4.538

9.  Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models.

Authors:  Kay Sun; Thomas S Pheiffer; Amber L Simpson; Jared A Weis; Reid C Thompson; Michael I Miga
Journal:  IEEE J Transl Eng Health Med       Date:  2014-04-30       Impact factor: 3.316

10.  Registering imaged ECoG electrodes to human cortex: A geometry-based technique.

Authors:  David Brang; Zhongtian Dai; Weili Zheng; Vernon L Towle
Journal:  J Neurosci Methods       Date:  2016-08-10       Impact factor: 2.390

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