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Callejas A, Melchor J, Faris IH, Rus G. Hyperelastic Ex Vivo Cervical Tissue Mechanical Characterization. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4362. [PMID: 32764345 PMCID: PMC7472274 DOI: 10.3390/s20164362] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/19/2020] [Accepted: 08/03/2020] [Indexed: 12/18/2022]
Abstract
This paper presents the results of the comparison between a proposed Fourth Order Elastic Constants (FOECs) nonlinear model defined in the sense of Landau's theory, and the two most contrasted hyperelastic models in the literature, Mooney-Rivlin, and Ogden models. A mechanical testing protocol is developed to investigate the large-strain response of ex vivo cervical tissue samples in uniaxial tension in its two principal anatomical locations, the epithelial and connective layers. The final aim of this work is to compare the reconstructed shear modulus of the epithelial and connective layers of cervical tissue. According to the obtained results, the nonlinear parameter A from the proposed FOEC model could be an important biomarker in cervical tissue diagnosis. In addition, the calculated shear modulus depended on the anatomical location of the cervical tissue (μepithelial = 1.29 ± 0.15 MPa, and μconnective = 3.60 ± 0.63 MPa).
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Affiliation(s)
- Antonio Callejas
- Department of Structural Mechanics, University of Granada, 18010 Granada, Spain; (I.H.F.); (G.R.)
- Instituto de Investigación Biosanitaria, ibs.GRANADA, 18012 Granada, Spain;
| | - Juan Melchor
- Instituto de Investigación Biosanitaria, ibs.GRANADA, 18012 Granada, Spain;
- Excellence Research Unit, “Modelling Nature” (MNat), University of Granada, 18010 Granada, Spain
- Department of Statistics and Operations Research, University of Granada, 18010 Granada, Spain
| | - Inas H. Faris
- Department of Structural Mechanics, University of Granada, 18010 Granada, Spain; (I.H.F.); (G.R.)
- Instituto de Investigación Biosanitaria, ibs.GRANADA, 18012 Granada, Spain;
| | - Guillermo Rus
- Department of Structural Mechanics, University of Granada, 18010 Granada, Spain; (I.H.F.); (G.R.)
- Instituto de Investigación Biosanitaria, ibs.GRANADA, 18012 Granada, Spain;
- Excellence Research Unit, “Modelling Nature” (MNat), University of Granada, 18010 Granada, Spain
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Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI. Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery. J Med Imaging (Bellingham) 2017; 4:035003. [PMID: 28924573 PMCID: PMC5596210 DOI: 10.1117/1.jmi.4.3.035003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 08/21/2017] [Indexed: 11/14/2022] Open
Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
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Affiliation(s)
- Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Sarah F. Frisken
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Jared A. Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States
| | - Logan W. Clements
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Prashin Unadkat
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Reid C. Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Alexandra J. Golby
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University, Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
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Bayer S, Maier A, Ostermeier M, Fahrig R. Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery. Int J Biomed Imaging 2017; 2017:6028645. [PMID: 28676821 PMCID: PMC5476838 DOI: 10.1155/2017/6028645] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Accepted: 05/03/2017] [Indexed: 11/26/2022] Open
Abstract
Intraoperative brain shift during neurosurgical procedures is a well-known phenomenon caused by gravity, tissue manipulation, tumor size, loss of cerebrospinal fluid (CSF), and use of medication. For the use of image-guided systems, this phenomenon greatly affects the accuracy of the guidance. During the last several decades, researchers have investigated how to overcome this problem. The purpose of this paper is to present a review of publications concerning different aspects of intraoperative brain shift especially in a tumor resection surgery such as intraoperative imaging systems, quantification, measurement, modeling, and registration techniques. Clinical experience of using intraoperative imaging modalities, details about registration, and modeling methods in connection with brain shift in tumor resection surgery are the focuses of this review. In total, 126 papers regarding this topic are analyzed in a comprehensive summary and are categorized according to fourteen criteria. The result of the categorization is presented in an interactive web tool. The consequences from the categorization and trends in the future are discussed at the end of this work.
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Affiliation(s)
- Siming Bayer
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany
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In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction. Sci Rep 2016; 6:28654. [PMID: 27387301 PMCID: PMC4937391 DOI: 10.1038/srep28654] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/07/2016] [Indexed: 11/08/2022] Open
Abstract
Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).
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Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery. Ann Biomed Eng 2015; 44:128-38. [PMID: 26354118 DOI: 10.1007/s10439-015-1433-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 08/18/2015] [Indexed: 01/14/2023]
Abstract
With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.
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Wex C, Arndt S, Stoll A, Bruns C, Kupriyanova Y. Isotropic incompressible hyperelastic models for modelling the mechanical behaviour of biological tissues: a review. ACTA ACUST UNITED AC 2015; 60:577-92. [DOI: 10.1515/bmt-2014-0146] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 05/07/2015] [Indexed: 11/15/2022]
Abstract
AbstractModelling the mechanical behaviour of biological tissues is of vital importance for clinical applications. It is necessary for surgery simulation, tissue engineering, finite element modelling of soft tissues, etc. The theory of linear elasticity is frequently used to characterise biological tissues; however, the theory of nonlinear elasticity using hyperelastic models, describes accurately the nonlinear tissue response under large strains. The aim of this study is to provide a review of constitutive equations based on the continuum mechanics approach for modelling the rate-independent mechanical behaviour of homogeneous, isotropic and incompressible biological materials. The hyperelastic approach postulates an existence of the strain energy function – a scalar function per unit reference volume, which relates the displacement of the tissue to their corresponding stress values. The most popular form of the strain energy functions as Neo-Hookean, Mooney-Rivlin, Ogden, Yeoh, Fung-Demiray, Veronda-Westmann, Arruda-Boyce, Gent and their modifications are described and discussed considering their ability to analytically characterise the mechanical behaviour of biological tissues. The review provides a complete and detailed analysis of the strain energy functions used for modelling the rate-independent mechanical behaviour of soft biological tissues such as liver, kidney, spleen, brain, breast, etc.
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Lalys F, Haegelen C, D'albis T, Jannin P. Analysis of electrode deformations in deep brain stimulation surgery. Int J Comput Assist Radiol Surg 2014; 9:107-17. [PMID: 23780571 PMCID: PMC5071382 DOI: 10.1007/s11548-013-0911-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 06/06/2013] [Indexed: 11/28/2022]
Abstract
PURPOSE Deep brain stimulation (DBS) surgery is used to reduce motor symptoms when movement disorders are refractory to medical treatment. Post-operative brain morphology can induce electrode deformations as the brain recovers from an intervention. The inverse brain shift has a direct impact on accuracy of the targeting stage, so analysis of electrode deformations is needed to predict final positions. METHODS DBS electrode curvature was evaluated in 76 adults with movement disorders who underwent bilateral stimulation, and the key variables that affect electrode deformations were identified. Non-linear modelling of the electrode axis was performed using post-operative computed tomography (CT) images. A mean curvature index was estimated for each patient electrode. Multivariate analysis was performed using a regression decision tree to create a hierarchy of predictive variables. The identification and classification of key variables that determine electrode curvature were validated with statistical analysis. RESULTS The principal variables affecting electrode deformations were found to be the date of the post-operative CT scan and the stimulation target location. The main pathology, patient's gender, and disease duration had a smaller although important impact on brain shift. CONCLUSIONS The principal determinants of electrode location accuracy during DBS procedures were identified and validated. These results may be useful for improved electrode targeting with the help of mathematical models.
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Affiliation(s)
- Florent Lalys
- Unite INSERM U1099 LTSI, Equipe Medicis, Faculté de médecine, Université Rennes I, 2 Av. du Pr Leon Bernard, 35043 , Rennes, France,
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A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner. Int J Comput Assist Radiol Surg 2013; 9:669-81. [PMID: 24293030 PMCID: PMC4082653 DOI: 10.1007/s11548-013-0958-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2013] [Accepted: 10/23/2013] [Indexed: 12/31/2022]
Abstract
BACKGROUND Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application. METHODS We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance. RESULTS The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %). CONCLUSIONS The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.
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Poliachik SL, Poliakov AV, Jansen LA, McDaniel SS, Wray CD, Kuratani J, Saneto RP, Ojemann JG, Novotny EJ. Tissue localization during resective epilepsy surgery. Neurosurg Focus 2013; 34:E8. [DOI: 10.3171/2013.3.focus1360] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Object
Imaging-guided surgery (IGS) systems are widely used in neurosurgical practice. During epilepsy surgery, the authors routinely use IGS landmarks to localize intracranial electrodes and/or specific brain regions. The authors have developed a technique to coregister these landmarks with pre- and postoperative scans and the Montreal Neurological Institute (MNI) standard space brain MRI to allow 1) localization and identification of tissue anatomy; and 2) identification of Brodmann areas (BAs) of the tissue resected during epilepsy surgery. Tracking tissue in this fashion allows for better correlation of patient outcome to clinical factors, functional neuroimaging findings, and pathological characteristics and molecular studies of resected tissue.
Methods
Tissue samples were collected in 21 patients. Coordinates from intraoperative tissue localization were downloaded from the IGS system and transformed into patient space, as defined by preoperative high-resolution T1-weighted MRI volume. Tissue landmarks in patient space were then transformed into MNI standard space for identification of the BAs of the tissue samples.
Results
Anatomical locations of resected tissue were identified from the intraoperative resection landmarks. The BAs were identified for 17 of the 21 patients. The remaining patients had abnormal brain anatomy that could not be meaningfully coregistered with the MNI standard brain without causing extensive distortion.
Conclusions
This coregistration and landmark tracking technique allows localization of tissue that is resected from patients with epilepsy and identification of the BAs for each resected region. The ability to perform tissue localization allows investigators to relate preoperative, intraoperative, and postoperative functional and anatomical brain imaging to better understand patient outcomes, improve patient safety, and aid in research.
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Affiliation(s)
- Sandra L. Poliachik
- 1Divisions of Pediatric Neurology,
- 2Pediatric Radiology, and
- 6Centers for Clinical and Translational Research and
| | - Andrew V. Poliakov
- 2Pediatric Radiology, and
- 3Pediatric Neurosurgery, Seattle Children's Hospital
| | - Laura A. Jansen
- 4Departments of Neurology and
- 7Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington
| | | | - Carter D. Wray
- 1Divisions of Pediatric Neurology,
- 4Departments of Neurology and
| | - John Kuratani
- 1Divisions of Pediatric Neurology,
- 4Departments of Neurology and
- 6Centers for Clinical and Translational Research and
| | - Russell P. Saneto
- 1Divisions of Pediatric Neurology,
- 4Departments of Neurology and
- 6Centers for Clinical and Translational Research and
| | - Jeffrey G. Ojemann
- 3Pediatric Neurosurgery, Seattle Children's Hospital
- 5Neurosurgery, and
- 7Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington
- 8Integrative Brain Imaging Center, University of Washington; and
| | - Edward J. Novotny
- 1Divisions of Pediatric Neurology,
- 4Departments of Neurology and
- 7Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington
- 8Integrative Brain Imaging Center, University of Washington; and
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DeLorenzo C, Papademetris X, Staib LH, Vives KP, Spencer DD, Duncan JS. Volumetric intraoperative brain deformation compensation: model development and phantom validation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1607-19. [PMID: 22562728 PMCID: PMC3600363 DOI: 10.1109/tmi.2012.2197407] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.
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