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Griffiths E, Jayamohan J, Budday S. A comparison of brain retraction mechanisms using finite element analysis and the effects of regionally heterogeneous material properties. Biomech Model Mechanobiol 2024; 23:793-808. [PMID: 38361082 DOI: 10.1007/s10237-023-01806-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/14/2023] [Indexed: 02/17/2024]
Abstract
Finite element (FE) simulations of the brain undergoing neurosurgical procedures present us with the great opportunity to better investigate, understand, and optimize surgical techniques and equipment. FE models provide access to data such as the stress levels within the brain that would otherwise be inaccessible with the current medical technology. Brain retraction is often a dangerous but necessary part of neurosurgery, and current research focuses on minimizing trauma during the procedure. In this work, we present a simulation-based comparison of different types of retraction mechanisms. We focus on traditional spatulas and tubular retractors. Our results show that tubular retractors result in lower average predicted stresses, especially in the subcortical structures and corpus callosum. Additionally, we show that changing the location of retraction can greatly affect the predicted stress results. As the model predictions highly depend on the material model and parameters used for simulations, we also investigate the importance of using region-specific hyperelastic and viscoelastic material parameters when modelling a three-dimensional human brain during retraction. Our investigations demonstrate how FE simulations in neurosurgical techniques can provide insight to surgeons and medical device manufacturers. They emphasize how further work into this direction could greatly improve the management and prevention of injury during surgery. Additionally, we show the importance of modelling the human brain with region-dependent parameters in order to provide useful predictions for neurosurgical procedures.
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Affiliation(s)
- Emma Griffiths
- Department of Mechanical Engineering, Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058, Erlangen, Germany.
| | - Jayaratnam Jayamohan
- Department of Pediatric Neurosurgery, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Silvia Budday
- Department of Mechanical Engineering, Institute of Continuum Mechanics and Biomechanics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058, Erlangen, Germany
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2
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Wang R, Sarntinoranont M. Biphasic analysis of rat brain slices under creep indentation shows nonlinear tension-compression behavior. J Mech Behav Biomed Mater 2018; 89:1-8. [PMID: 30236976 DOI: 10.1016/j.jmbbm.2018.08.043] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 08/11/2018] [Accepted: 08/28/2018] [Indexed: 01/29/2023]
Abstract
Biphasic theory can provide a mechanistic description of deformation and transport phenomena in soft tissues, and has been used to model surgery and drug delivery in the brain for decades. Knowledge of corresponding mechanical properties of the brain is needed to accurately predict tissue deformation and flow transport in these applications. Previously in our group, creep indentation tests were conducted for multiple anatomical regions in acute rat brain tissue slices. In the current study, a biphasic finite element model of creep indentation was developed with which to compare these data. Considering the soft tissue structure of brain, the solid matrix was assumed to be composed of a neo-Hookean ground matrix reinforced by continuously distributed fibers that exhibits tension-compression nonlinearity during deformation. By fixing Poisson's ratio of the ground matrix, Young's modulus, fiber modulus and hydraulic permeability were estimated. Hydraulic permeability was found to be nearly independent of the properties of the solid matrix. Estimated modulus (40 Pa to 1.1 kPa for the ground matrix, 3.2-18.2 kPa for fibers) and hydraulic permeability (1.2-5.5×10-13m4/N s) fell within an acceptable range compared with those in previous studies. Instantaneous indentation depth was dominated by tension provided by fibers, while the tissue response at equilibrium was sensitive to Poisson's ratio. Results of sensitivity analysis also point to the necessity of considering tension-compression nonlinearity in the solid phase when the biphasic material undergoes large creep deformation.
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Affiliation(s)
- Ruizhi Wang
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, United States.
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3
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Tan L, McGarry MDJ, Van Houten EEW, Ji M, Solamen L, Zeng W, Weaver JB, Paulsen KD. A numerical framework for interstitial fluid pressure imaging in poroelastic MRE. PLoS One 2017; 12:e0178521. [PMID: 28586393 PMCID: PMC5460821 DOI: 10.1371/journal.pone.0178521] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 05/15/2017] [Indexed: 11/18/2022] Open
Abstract
A numerical framework for interstitial fluid pressure imaging (IFPI) in biphasic materials is investigated based on three-dimensional nonlinear finite element poroelastic inversion. The objective is to reconstruct the time-harmonic pore-pressure field from tissue excitation in addition to the elastic parameters commonly associated with magnetic resonance elastography (MRE). The unknown pressure boundary conditions (PBCs) are estimated using the available full-volume displacement data from MRE. A subzone-based nonlinear inversion (NLI) technique is then used to update mechanical and hydrodynamical properties, given the appropriate subzone PBCs, by solving a pressure forward problem (PFP). The algorithm was evaluated on a single-inclusion phantom in which the elastic property and hydraulic conductivity images were recovered. Pressure field and material property estimates had spatial distributions reflecting their true counterparts in the phantom geometry with RMS errors around 20% for cases with 5% noise, but degraded significantly in both spatial distribution and property values for noise levels > 10%. When both shear moduli and hydraulic conductivity were estimated along with the pressure field, property value error rates were as high as 58%, 85% and 32% for the three quantities, respectively, and their spatial distributions were more distorted. Opportunities for improving the algorithm are discussed.
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Affiliation(s)
- Likun Tan
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Matthew D. J. McGarry
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, United States of America
| | - Elijah E. W. Van Houten
- Department of Mechanical Engineering, University de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - Ming Ji
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States of America
| | - Ligin Solamen
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Wei Zeng
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - John B. Weaver
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756 United States of America
| | - Keith D. Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756 United States of America
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4
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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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Gerard IJ, Kersten-Oertel M, Petrecca K, Sirhan D, Hall JA, Collins DL. Brain shift in neuronavigation of brain tumors: A review. Med Image Anal 2016; 35:403-420. [PMID: 27585837 DOI: 10.1016/j.media.2016.08.007] [Citation(s) in RCA: 153] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 08/22/2016] [Accepted: 08/23/2016] [Indexed: 10/21/2022]
Abstract
PURPOSE Neuronavigation based on preoperative imaging data is a ubiquitous tool for image guidance in neurosurgery. However, it is rendered unreliable when brain shift invalidates the patient-to-image registration. Many investigators have tried to explain, quantify, and compensate for this phenomenon to allow extended use of neuronavigation systems for the duration of surgery. The purpose of this paper is to present an overview of the work that has been done investigating brain shift. METHODS A review of the literature dealing with the explanation, quantification and compensation of brain shift is presented. The review is based on a systematic search using relevant keywords and phrases in PubMed. The review is organized based on a developed taxonomy that classifies brain shift as occurring due to physical, surgical or biological factors. RESULTS This paper gives an overview of the work investigating, quantifying, and compensating for brain shift in neuronavigation while describing the successes, setbacks, and additional needs in the field. An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention. The analysis indicates the need for additional research to be done in quantifying independent effects of brain shift in order for some of the state of the art compensation methods to become useful. CONCLUSION This review allows for a thorough understanding of the work investigating brain shift and introduces the needs for future avenues of investigation of the phenomenon.
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Affiliation(s)
- Ian J Gerard
- McConnell Brain Imaging Center, MNI, McGill University, Montreal, Canada.
| | | | - Kevin Petrecca
- Department of Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Denis Sirhan
- Department of Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jeffery A Hall
- Department of Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, MNI, McGill University, Montreal, Canada; Department of Neurosurgery, McGill University, Montreal, Quebec, Canada
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Tavner A, Roy TD, Hor K, Majimbi M, Joldes G, Wittek A, Bunt S, Miller K. On the appropriateness of modelling brain parenchyma as a biphasic continuum. J Mech Behav Biomed Mater 2016; 61:511-518. [DOI: 10.1016/j.jmbbm.2016.04.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 04/06/2016] [Accepted: 04/06/2016] [Indexed: 10/21/2022]
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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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8
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Jiang J, Nakajima Y, Sohma Y, Saito T, Kin T, Oyama H, Saito N. Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features. Int J Comput Assist Radiol Surg 2016; 11:1687-701. [PMID: 26945999 DOI: 10.1007/s11548-016-1358-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 02/09/2016] [Indexed: 10/22/2022]
Abstract
PURPOSE To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface. METHOD Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions. RESULTS The minimum and maximum bias errors were 0.32 and 0.61 mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30 mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9 mm, respectively. CONCLUSION The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.
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Affiliation(s)
- Jue Jiang
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan.
| | - Yoshikazu Nakajima
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan
| | - Yoshio Sohma
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan
| | - Toki Saito
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan.,Department of Clinical Information Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Taichi Kin
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan.,Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Horoshi Oyama
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan.,Department of Clinical Information Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Nobuhito Saito
- Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan.,Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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9
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Drakopoulos F, Chrisochoides NP. Accurate and fast deformable medical image registration for brain tumor resection using image-guided neurosurgery. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2016. [DOI: 10.1080/21681163.2015.1067869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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10
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McGarry MDJ, Johnson CL, Sutton BP, Georgiadis JG, Van Houten EEW, Pattison AJ, Weaver JB, Paulsen KD. Suitability of poroelastic and viscoelastic mechanical models for high and low frequency MR elastography. Med Phys 2015; 42:947-57. [PMID: 25652507 DOI: 10.1118/1.4905048] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Descriptions of the structure of brain tissue as a porous cellular matrix support application of a poroelastic (PE) mechanical model which includes both solid and fluid phases. However, the majority of brain magnetic resonance elastography (MRE) studies use a single phase viscoelastic (VE) model to describe brain tissue behavior, in part due to availability of relatively simple direct inversion strategies for mechanical property estimation. A notable exception is low frequency intrinsic actuation MRE, where PE mechanical properties are imaged with a nonlinear inversion algorithm. METHODS This paper investigates the effect of model choice at each end of the spectrum of in vivo human brain actuation frequencies. Repeat MRE examinations of the brains of healthy volunteers were used to compare image quality and repeatability for each inversion model for both 50 Hz externally produced motion and ≈1 Hz intrinsic motions. Additionally, realistic simulated MRE data were generated with both VE and PE finite element solvers to investigate the effect of inappropriate model choice for ideal VE and PE materials. RESULTS In vivo, MRE data revealed that VE inversions appear more representative of anatomical structure and quantitatively repeatable for 50 Hz induced motions, whereas PE inversion produces better results at 1 Hz. Reasonable VE approximations of PE materials can be derived by equating the equivalent wave velocities for the two models, provided that the timescale of fluid equilibration is not similar to the period of actuation. An approximation of the equilibration time for human brain reveals that this condition is violated at 1 Hz but not at 50 Hz. Additionally, simulation experiments when using the "wrong" model for the inversion demonstrated reasonable shear modulus reconstructions at 50 Hz, whereas cross-model inversions at 1 Hz were poor quality. Attenuation parameters were sensitive to changes in the forward model at both frequencies, however, no spatial information was recovered because the mechanisms of VE and PE attenuation are different. CONCLUSIONS VE inversions are simpler with fewer unknown properties and may be sufficient to capture the mechanical behavior of PE brain tissue at higher actuation frequencies. However, accurate modeling of the fluid phase is required to produce useful mechanical property images at the lower frequencies of intrinsic brain motions.
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Affiliation(s)
- M D J McGarry
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755
| | - C L Johnson
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - B P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 and Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - J G Georgiadis
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801; Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801; and Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801
| | - E E W Van Houten
- Department of Mechanical Engineering, University de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada
| | - A J Pattison
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755
| | - J B Weaver
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755 and Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03755
| | - K D Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755 and Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03755
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Shakarami M, Suratgar AA, Talebi HA. Estimation of intra-operative brain shift based on constrained Kalman filter. ISA TRANSACTIONS 2015; 55:260-6. [PMID: 25451818 DOI: 10.1016/j.isatra.2014.09.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Revised: 07/27/2014] [Accepted: 09/30/2014] [Indexed: 05/08/2023]
Abstract
In this study, the problem of estimation of brain shift is addressed by which the accuracy of neuronavigation systems can be improved. To this end, the actual brain shift is considered as a Gaussian random vector with a known mean and an unknown covariance. Then, brain surface imaging is employed together with solutions of linear elastic model and the best estimation is found using constrained Kalman filter (CKF). Moreover, a recursive method (RCKF) is presented, the computational cost of which in the operating room is significantly lower than CKF, because it is not required to compute inverse of any large matrix. Finally, the theory is verified by the simulation results, which show the superiority of the proposed method as compared to one existing method.
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Affiliation(s)
- M Shakarami
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran; The Center of Excellence in Control and Robotics, Amirkabir University of Technology, Tehran, Iran.
| | - A A Suratgar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran; The Center of Excellence in Control and Robotics, Amirkabir University of Technology, Tehran, Iran.
| | - H A Talebi
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran; The Center of Excellence in Control and Robotics, Amirkabir University of Technology, Tehran, Iran.
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12
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Gulsen S. Correlation of the CT Compatible Stereotaxic Craniotomy with MRI Scans of the Patients for Removing Cranial Lesions Located Eloquent Areas and Deep Sites of Brain. Open Access Maced J Med Sci 2015; 3:111-6. [PMID: 27275206 PMCID: PMC4877767 DOI: 10.3889/oamjms.2015.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Revised: 01/20/2015] [Accepted: 02/10/2015] [Indexed: 11/13/2022] Open
Abstract
The first goal in neurosurgery is to protect neural function as long as it is possible. Moreover, while protecting the neural function, a neurosurgeon should extract the maximum amount of tumoral tissue from the tumour region of the brain. So neurosurgery and technological advancement go hand in hand to realize this goal. Using of CT compatible stereotaxy for removing a cranial tumour is to be commended as a cornerstone of these technological advancements. Following CT compatible stereotaxic system applications in neurosurgery, different techniques have taken place in neurosurgical practice. These techniques are magnetic resonance imaging (MRI), MRI compatible stereotaxis, frameless stereotaxy, volumetric stereotaxy, functional MRI, diffusion tensor (DT) imaging techniques (tractography of the white matter), intraoperative MRI and neuronavigation systems. However, to use all of this equipment having these technologies would be impossible because of economic reasons. However, when we correlated this technique with MRI scans of the patients with CT compatible stereotaxy scans, it is possible to provide gross total resection and protect and improve patients’ neural functions.
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13
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Drakopoulos F, Foteinos P, Liu Y, Chrisochoides NP. Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection. Front Neuroinform 2014; 8:11. [PMID: 24596553 PMCID: PMC3925835 DOI: 10.3389/fninf.2014.00011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2013] [Accepted: 01/27/2014] [Indexed: 11/16/2022] Open
Abstract
This paper presents an adaptive non-rigid registration method for aligning pre-operative MRI with intra-operative MRI (iMRI) to compensate for brain deformation during brain tumor resection. This method extends a successful existing Physics-Based Non-Rigid Registration (PBNRR) technique implemented in ITKv4.5. The new method relies on a parallel adaptive heterogeneous biomechanical Finite Element (FE) model for tissue/tumor removal depicted in the iMRI. In contrast the existing PBNRR in ITK relies on homogeneous static FE model designed for brain shift only (i.e., it is not designed to handle brain tumor resection). As a result, the new method (1) accurately captures the intra-operative deformations associated with the tissue removal due to tumor resection and (2) reduces the end-to-end execution time to within the time constraints imposed by the neurosurgical procedure. The evaluation of the new method is based on 14 clinical cases with: (i) brain shift only (seven cases), (ii) partial tumor resection (two cases), and (iii) complete tumor resection (five cases). The new adaptive method can reduce the alignment error up to seven and five times compared to a rigid and ITK's PBNRR registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK. Moreover, the total execution time for all the case studies is about 1 min or less in a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM.
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Affiliation(s)
- Fotis Drakopoulos
- CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA
| | - Panagiotis Foteinos
- CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA ; Computer Science, College of William and Mary Williamsburg, VA, USA
| | - Yixun Liu
- Radiology and Imaging Science, National Institutes of Health Bethesda, MD, USA
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14
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Duffau H. Intraoperative cortico–subcortical stimulations in surgery of low-grade gliomas. Expert Rev Neurother 2014; 5:473-85. [PMID: 16026231 DOI: 10.1586/14737175.5.4.473] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In order to increase the impact of surgery on the natural history of low-grade glioma, resection should be of maximum importance. Nevertheless, since low-grade gliomas are frequently located in eloquent structures, function needs to be preserved. Therefore, studying the functional organization of the brain is mandatory for each patient due to the inter-individual anatomofunctional variability, increased in tumors due to cerebral plasticity. This strategy enables performance of a resection according to functional boundaries. However, preoperative neurofunctional imaging only allows the study of the gray matter. Consequently, since low-grade glioma invades cortical and subcortical structures and shows an infiltrative progression along the fibers, the goal of this review is to focus on the techniques able to map both cortical and subcortical regions. In addition to diffusion tensor imaging, which gives only anatomical information and still needs to be validated, intraoperative direct cortico-subcortical electrostimulation is the sole current method allowing a reliable study of the individual anatomofunctional connectivity, concerning sensorimotor, language and other cognitive functions. Its actual contribution is detailed, both in clinical issues, especially the improvement of the benefit/risk ratio of low-grade glioma resection, and in fundamental applications--namely, a new door to the connectionism and cerebral plasticity.
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Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, INSERM U678, UPMC, Hôpital Salpêtrière, 47-83 Bd de l'hôpital, 75013, Paris, France.
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15
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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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Abstract
This paper reviews the literature of the brain retraction injury during the last century. The review focused on the instrument characteristic as well as the physiopathological and histopathological damage of the brain induced by brain retraction. It was found that lesions were induced by cerebral ischemia. We conclude that a better monitoring system needs to be developed to avoid brain injury.
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Affiliation(s)
- Jun Zhong
- Biomechanics Laboratory, Department of Neurosurgery, Wayne State University School of Medicine, 4201 St. Antoine UHC 6E, Detroit 48201, USA
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17
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Chen I, Ong RE, Simpson AL, Sun K, Thompson RC, Miga MI. Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction. IEEE Trans Biomed Eng 2013; 60:3494-504. [PMID: 23864146 DOI: 10.1109/tbme.2013.2272658] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework's accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework.
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18
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D'Andrea G, Angelini A, Romano A, Di Lauro A, Sessa G, Bozzao A, Ferrante L. Intraoperative DTI and brain mapping for surgery of neoplasm of the motor cortex and the corticospinal tract: our protocol and series in BrainSUITE. Neurosurg Rev 2012; 35:401-12; discussion 412. [PMID: 22370809 DOI: 10.1007/s10143-012-0373-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Revised: 09/19/2011] [Accepted: 09/25/2011] [Indexed: 12/22/2022]
Affiliation(s)
- Giancarlo D'Andrea
- S Andrea Hospital, Institute of Neurosurgery, University of Rome "La Sapienza", V. Raineri 27, 00151, Rome, Italy.
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19
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Vigneron LM, Warfield SK, Robe PA, Verly JG. 3D XFEM-based modeling of retraction for preoperative image update. ACTA ACUST UNITED AC 2011; 16:121-34. [PMID: 21476788 DOI: 10.3109/10929088.2011.570090] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Outcomes for neurosurgery patients can be improved by enhancing intraoperative navigation and guidance. Current navigation systems do not accurately account for intraoperative brain deformation. So far, most studies of brain deformation have focused on brain shift, whereas this paper focuses on the brain deformation due to retraction. The heart of our system is a 3D nonrigid registration technique using a biomechanical model driven by the deformations of key surfaces tracked between two intraoperative images. The key surfaces, e.g., the whole-brain region boundary and the lips of the retraction cut, thus deform due to the combination of gravity and retractor deployment. The tissue discontinuity due to retraction is handled via the eXtended Finite Element Method (XFEM), which has the appealing feature of being able to handle arbitrarily shaped discontinuity without any remeshing. Our approach is shown to significantly improve the alignment of intraoperative MRI.
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Affiliation(s)
- Lara M Vigneron
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
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20
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Abstract
Multimodal functional navigation enables removing a tumor close to eloquent brain areas with low postoperative deficits, whereas additional intraoperative imaging ensures that the maximum extent of the resection can be achieved and updates the image data compensating for the effects of brain shift. Intraoperative imaging beyond standard anatomic imaging, that is, intraoperative functional magnetic resonance imaging (fMRI) and especially intraoperative diffusion tensor imaging (DTI), add further safety for complex tumor resections. This article discusses the acquisition of intraoperative fMRI, DTI, and imaging.
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Affiliation(s)
- Christopher Nimsky
- Department of Neurosurgery, University Marburg, Baldingerstrasse, Marburg 35033, Germany.
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21
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Risholm P, Golby AJ, Wells W. Multimodal image registration for preoperative planning and image-guided neurosurgical procedures. Neurosurg Clin N Am 2011; 22:197-206, viii. [PMID: 21435571 DOI: 10.1016/j.nec.2010.12.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary, structural, and functional information to improve the information based on which a surgeon makes critical decisions. Brigham and Women's Hospital (BWH) has been one of the pioneers in developing intraoperative registration methods for aligning preoperative and intraoperative images of the brain. This article presents an overview of intraoperative registration and highlights some recent developments at BWH.
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Affiliation(s)
- Petter Risholm
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
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22
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Vigneron LM, Duflot MP, Robe PA, Warfield SK, Verly JG. 2D XFEM-based modeling of retraction and successive resections for preoperative image update. ACTA ACUST UNITED AC 2011; 14:1-20. [PMID: 19634040 DOI: 10.3109/10929080903052677] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
This paper considers an approach to improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Currently, intraoperative navigation systems do not accurately account for brain shift or tissue resection. We describe how preoperative images can be incrementally updated to take into account any type of brain tissue deformation that may occur during surgery, and thus to improve the accuracy of image-guided navigation systems. For this purpose, we have developed a non-rigid image registration technique using a biomechanical model, which deforms based on the Finite Element Method (FEM). While the FEM has been used successfully for dealing with deformations such as brain shift, it has difficulty with tissue discontinuities. Here, we describe a novel application of the eXtended Finite Element Method (XFEM) in the field of image-guided surgery in order to model brain deformations that imply tissue discontinuities. In particular, this paper presents a detailed account of the use of XFEM for dealing with retraction and successive resections, and demonstrates the feasibility of the approach by considering 2D examples based on intraoperative MR images. To evaluate our results, we compute the modified Hausdorff distance between Canny edges extracted from images before and after registration. We show that this distance decreases after registration, and thus demonstrate that our approach improves alignment of intraoperative images.
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Affiliation(s)
- Lara M Vigneron
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
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23
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Perriñez PR, Pattison AJ, Kennedy FE, Weaver JB, Paulsen KD. Contrast detection in fluid-saturated media with magnetic resonance poroelastography. Med Phys 2010; 37:3518-26. [PMID: 20831058 DOI: 10.1118/1.3443563] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Recent interest in the poroelastic behavior of tissues has led to the development of magnetic resonance poroelastography (MRPE) as an alternative to single-phase MR elastographic image reconstruction. In addition to the elastic parameters (i.e., Lamé's constants) commonly associated with magnetic resonance elastography (MRE), MRPE enables estimation of the time-harmonic pore-pressure field induced by external mechanical vibration. METHODS This study presents numerical simulations that demonstrate the sensitivity of the computed displacement and pore-pressure fields to a priori estimates of the experimentally derived model parameters. In addition, experimental data collected in three poroelastic phantoms are used to assess the quantitative accuracy of MR poroelastographic imaging through comparisons with both quasistatic and dynamic mechanical tests. RESULTS The results indicate hydraulic conductivity to be the dominant parameter influencing the deformation behavior of poroelastic media under conditions applied during MRE. MRPE estimation of the matrix shear modulus was bracketed by the values determined from independent quasistatic and dynamic mechanical measurements as expected, whereas the contrast ratios for embedded inclusions were quantitatively similar (10%-15% difference between the reconstructed images and the mechanical tests). CONCLUSIONS The findings suggest that the addition of hydraulic conductivity and a viscoelastic solid component as parameters in the reconstruction may be warranted.
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Affiliation(s)
- Phillip R Perriñez
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA.
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24
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Wittek A, Joldes G, Couton M, Warfield SK, Miller K. Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2010; 103:292-303. [PMID: 20868706 DOI: 10.1016/j.pbiomolbio.2010.09.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Revised: 08/30/2010] [Accepted: 09/14/2010] [Indexed: 11/18/2022]
Abstract
Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.
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Affiliation(s)
- Adam Wittek
- Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia.
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25
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Data-guide for brain deformation in surgery: comparison of linear and nonlinear models. Biomed Eng Online 2010; 9:51. [PMID: 20843360 PMCID: PMC2949882 DOI: 10.1186/1475-925x-9-51] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2010] [Accepted: 09/15/2010] [Indexed: 11/10/2022] Open
Abstract
Background Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain. Methods We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic). Results Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy. Conclusions The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.
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26
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Dumpuri P, Thompson RC, Cao A, Ding S, Garg I, Dawant BM, Miga MI. A fast and efficient method to compensate for brain shift for tumor resection therapies measured between preoperative and postoperative tomograms. IEEE Trans Biomed Eng 2010; 57:1285-96. [PMID: 20172796 PMCID: PMC2891363 DOI: 10.1109/tbme.2009.2039643] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution preoperative (pre-op) and postoperative (post-op) MR images were acquired for eight in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate post-op tomograms. Cortical surface deformation data were then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison toward validation. The proposed framework recaptured 85% of the mean subsurface shift. This translated to a subsurface shift error of 0.4 +/- 0.4 mm for a measured shift of 3.1 +/- 0.6 mm. The patient's pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively and visually. While intraoperative (intra-op) MR imaging data would be optimal, the extent of shift measured from pre- to post-op MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full-volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery.
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Affiliation(s)
- Prashanth Dumpuri
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
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27
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Ji S, Hartov A, Roberts D, Paulsen K. Data assimilation using a gradient descent method for estimation of intraoperative brain deformation. Med Image Anal 2009; 13:744-56. [PMID: 19647473 DOI: 10.1016/j.media.2009.07.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2007] [Revised: 06/28/2009] [Accepted: 07/02/2009] [Indexed: 11/24/2022]
Abstract
Biomechanical models that simulate brain deformation are gaining attention as alternatives for brain shift compensation. One approach, known as the "forced-displacement method", constrains the model to exactly match the measured data through boundary condition (BC) assignment. Although it improves model estimates and is computationally attractive, the method generates fictitious forces and may be ill-advised due to measurement uncertainty. Previously, we have shown that by assimilating intraoperatively acquired brain displacements in an inversion scheme, the Representer algorithm (REP) is able to maintain stress-free BCs and improve model estimates by 33% over those without data guidance in a controlled environment. However, REP is computationally efficient only when a few data points are used for model guidance because its costs scale linearly in the number of data points assimilated, thereby limiting its utility (and accuracy) in clinical settings. In this paper, we present a steepest gradient descent algorithm (SGD) whose computational complexity scales nearly invariantly with the number of measurements assimilated by iteratively adjusting the forcing conditions to minimize the difference between measured and model-estimated displacements (model-data misfit). Solutions of full linear systems of equations are achieved with a parallelized direct solver on a shared-memory, eight-processor Linux cluster. We summarize the error contributions from the entire process of model-updated image registration compensation and we show that SGD is able to attain model estimates comparable to or better than those obtained with REP, capturing about 74-82% of tumor displacement, but with a computational effort that is significantly less (a factor of 4-fold or more reduction relative to REP) and nearly invariant to the amount of sparse data involved when the number of points assimilated is large. Based on five patient cases, an average computational cost of approximately 2 min for estimating whole-brain deformation has been achieved with SGD using 100 sparse data points, suggesting the new algorithm is sufficiently fast with adequate accuracy for routine use in the operating room (OR).
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Affiliation(s)
- Songbai Ji
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
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28
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Bjarkam CR, Cancian G, Glud AN, Ettrup KS, Jørgensen RL, Sørensen JC. MRI-guided stereotaxic targeting in pigs based on a stereotaxic localizer box fitted with an isocentric frame and use of SurgiPlan computer-planning software. J Neurosci Methods 2009; 183:119-26. [PMID: 19559051 DOI: 10.1016/j.jneumeth.2009.06.019] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Revised: 06/15/2009] [Accepted: 06/16/2009] [Indexed: 11/28/2022]
Abstract
We present a stereotaxic procedure enabling MRI-guided isocentric stereotaxy in pigs. The procedure is based on the Leksell stereotaxic arch principle, and a stereotaxic localizer box with an incorporated fiducial marking system (sideplates) defining a stereotaxic space similar to the clinical Leksell system. The obtained MRIs can be imported for 3D-reconstruction and coordinate calculation in the clinical stereotaxic software planning system (Leksell SurgiPlan, Elekta AB, Sweden). After MRI the sideplates are replaced by a modified Leksell arch accommodating clinical standard manipulators for isocentric placement of DBS-electrodes, neural tracers and therapeutics in the calculated target coordinates. The mechanical accuracy of the device was within 0.3-0.5 mm. Stereotaxic MRIs were imported to the stereotaxic software planning system with a mean error of 0.4-0.5 mm and a max error of 0.8-0.9 mm. Application accuracy measured on a phantom and on inserted skull markers in nine pigs was within 1 mm in all planes. The intracerebral application accuracy found after placement of 10 manganese trajectories within the full extent of the intracerebral stereotaxic space in two minipigs was equally randomly distributed and within 0.7+/-0.4; 0.5+/-0.4; and 0.7+/-0.3mm in the X, Y, and Z plane. Injection of neural tracers in the subgenual gyrus of three minipigs and placement of encapsulated gene-modified cells in four minipigs confirmed the accuracy and functionality of the described procedure. We conclude that the devised technique and instrumentation enable high-precision stereotaxic procedures in pigs that may benefit future large animal neuroscience research and outline the technical considerations for a similar stereotaxic methodology in other animals.
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Affiliation(s)
- Carsten R Bjarkam
- Institute of Anatomy, The Faculty of Health Sciences, Aarhus University, Denmark.
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29
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Brain-skull contact boundary conditions in an inverse computational deformation model. Med Image Anal 2009; 13:659-72. [PMID: 19560393 DOI: 10.1016/j.media.2009.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2007] [Revised: 04/09/2009] [Accepted: 05/15/2009] [Indexed: 11/20/2022]
Abstract
Biomechanical models simulating brain motion under loading and boundary conditions in the operating room (OR) are gaining attention as alternatives for brain shift compensation during open cranial neurosurgeries. Although the significance of brain-skull boundary conditions (BCs) in these models has been explored in dynamic simulations, it has not been fully investigated in models representing the quasi-static brain motion that prevails during neurosurgery. In this study, we extend the application of a brain-skull contact BC by incorporating it into an inversion estimation scheme for the deformation field using the steepest gradient descent (SGD) framework. The technique allows parenchymal surface motion normal to the skull while maintaining stress-free BCs at the craniotomy and minimizing the effect of measurement noise. Application of the algorithm in five clinical cases using sparse data generated at the tumor boundary confirms the significance of brain-skull BCs in the model response. Specifically, the results demonstrate that the contact BC enhances model flexibility and achieves improved or comparable performance at the tumor boundary (recovering about 85% of the deformation) relative to that obtained when normal motion of the parenchymal surface is not allowed. It also significantly improves model estimation accuracy at the craniotomy (1.6mm on average), especially when the normal motion is large. The importance of the method is that model performance significantly improves when brain-skull contact influences the deformation field but does not degrade when the contact is less critical and simpler BCs would suffice. The computational cost of the technique is currently 3.9 min on average, but may be further reduced by applying an iterative solver to the linear systems of equations involved and/or by local refinement of the mesh in regions of interest.
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30
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Perriñez PR, Kennedy FE, Van Houten EEW, Weaver JB, Paulsen KD. Modeling of soft poroelastic tissue in time-harmonic MR elastography. IEEE Trans Biomed Eng 2008; 56:598-608. [PMID: 19272864 DOI: 10.1109/tbme.2008.2009928] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Elastography is an emerging imaging technique that focuses on assessing the resistance to deformation of soft biological tissues in vivo. Magnetic resonance elastography (MRE) uses measured displacement fields resulting from low-amplitude, low-frequency (10 Hz-1 kHz) time-harmonic vibration to recover images of the elastic property distribution of tissues including breast, liver, muscle, prostate, and brain. While many soft tissues display complex time-dependent behavior not described by linear elasticity, the models most commonly employed in MRE parameter reconstructions are based on elastic assumptions. Further, elasticity models fail to include the interstitial fluid phase present in vivo. Alternative continuum models, such as consolidation theory, are able to represent tissue and other materials comprising two distinct phases, generally consisting of a porous elastic solid and penetrating fluid. MRE reconstructions of simulated elastic and poroelastic phantoms were performed to investigate the limitations of current-elasticity-based methods in producing accurate elastic parameter estimates in poroelastic media. The results indicate that linearly elastic reconstructions of fluid-saturated porous media at amplitudes and frequencies relevant to steady-state MRE can yield misleading effective property distributions resulting from the complex interaction between their solid and fluid phases.
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Affiliation(s)
- Phillip R Perriñez
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
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31
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Stone SSD, Rutka JT. Utility of neuronavigation and neuromonitoring in epilepsy surgery. Neurosurg Focus 2008; 25:E17. [DOI: 10.3171/foc/2008/25/9/e17] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The management of medically refractory epilepsy poses both a valuable therapeutic opportunity and a formidable technical challenge to epilepsy surgeons. Recent decades have produced significant advancements in the capabilities and availability of adjunctive tools in epilepsy surgery. In particular, image-based neuronavigation and electrophysiological neuromonitoring represent versatile and informative modalities that can assist a surgeon in performing safe and effective resections. In the present article the authors discuss these 2 subjects with reference to how they can be applied and what evidence supports their use. As technologies evolve with demonstrated and potential utility, it is important for all clinicians who deal with epilepsy to understand where neuronavigation and neuromonitoring stand in the present and what avenues for improvement exist for the future.
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32
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Biomechanical modelling of normal pressure hydrocephalus. J Biomech 2008; 41:2263-71. [DOI: 10.1016/j.jbiomech.2008.04.014] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2007] [Revised: 04/02/2008] [Accepted: 04/11/2008] [Indexed: 11/20/2022]
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33
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Duffau H. Intraoperative neurophysiology during surgery for cerebral tumors. ACTA ACUST UNITED AC 2008. [DOI: 10.1016/s1567-4231(07)08035-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Abstract
Functional brain mapping may be useful for both preoperative planning and intraoperative neurosurgical decision making. "Gold standard" functional studies such as direct electrical stimulation and recording are complemented by newer, less invasive techniques such as functional magnetic resonance imaging. Less invasive techniques allow more areas of the brain to be mapped in more subjects (including healthy subjects) more often (including pre- and postoperatively). Expansion of the armamentarium of tools allows convergent evidence from multiple brain mapping techniques to bear on pre- and intraoperative decision making. Functional imaging techniques are used to map motor, sensory, language, and memory areas in neurosurgical patients with conditions as diverse as brain tumors, vascular lesions, and epilepsy. In the future, coregistration of high resolution anatomic and physiological data from multiple complementary sources will be used to plan more neurosurgical procedures, including minimally invasive procedures. Along the way, new insights on fundamental processes such as the biology of tumors and brain plasticity are likely to be revealed.
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Affiliation(s)
- Suzanne Tharin
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA
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35
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Dumpuri P, Thompson RC, Dawant BM, Cao A, Miga MI. An atlas-based method to compensate for brain shift: preliminary results. Med Image Anal 2007; 11:128-45. [PMID: 17336133 PMCID: PMC3819812 DOI: 10.1016/j.media.2006.11.002] [Citation(s) in RCA: 75] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2005] [Revised: 11/06/2006] [Accepted: 11/06/2006] [Indexed: 11/22/2022]
Abstract
Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7+/-0.3 mm, and 1.0+/-0.4 mm, respectively, for deformations on the order of 1cm.
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Affiliation(s)
- Prashanth Dumpuri
- Vanderbilt University, Department of Biomedical Engineering, P.O. 1631, Station B, Nashville, TN 37235, United States
| | - Reid C. Thompson
- Vanderbilt University, Department of Neurological Surgery, T-4224MCN/VUMC, Nashville, TN 37232 2380, United States
| | - Benoit M. Dawant
- Vanderbilt University, Department of Electrical Engineering and Computer Science, P.O. 351679, Station B, Nashville, TN 37235, United States
| | - A. Cao
- Vanderbilt University, Department of Biomedical Engineering, P.O. 1631, Station B, Nashville, TN 37235, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, P.O. 1631, Station B, Nashville, TN 37235, United States
- Corresponding author. Tel.: +1 615 343 8336; fax: +1 615 343 7919. , (M.I. Miga)
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Cornejo A, Algorri ME. Construction of a frameless camera-based stereotactic neuronavigator. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2004:1864-7. [PMID: 17272074 DOI: 10.1109/iembs.2004.1403554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
We built an infrared vision system to be used as the real time 3D motion sensor in a prototype low cost, high precision, frameless neuronavigator. The objective of the prototype is to develop accessible technology for increased availability of neuronavigation systems in research labs and small clinics and hospitals. We present our choice of technology including camera and IR emitter characteristics. We describe the methodology for setting up the 3D motion sensor, from the arrangement of the cameras and the IR emitters on surgical instruments, to triangulation equations from stereo camera pairs, high bandwidth computer communication with the cameras and real time image processing algorithms. We briefly cover the issues of camera calibration and characterization. Although our performance results do not yet fully meet the high precision, real time requirements of neuronavigation systems we describe the current improvements being made to the 3D motion sensor that will make it suitable for surgical applications.
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Affiliation(s)
- A Cornejo
- Department of Digital Systems, Instituto Tecnológico Autónomo de México, Mexico City, Mexico
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Archip N, Clatz O, Whalen S, Kacher D, Fedorov A, Kot A, Chrisochoides N, Jolesz F, Golby A, Black PM, Warfield SK. Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. Neuroimage 2006; 35:609-24. [PMID: 17289403 PMCID: PMC3358788 DOI: 10.1016/j.neuroimage.2006.11.060] [Citation(s) in RCA: 93] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2006] [Revised: 11/15/2006] [Accepted: 11/16/2006] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI. MATERIALS AND METHODS Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration. RESULTS Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001). CONCLUSIONS We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.
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Affiliation(s)
- Neculai Archip
- Department of Radiology, Harvard Medical School, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02115, USA.
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Lunn KE, Paulsen KD, Liu F, Kennedy FE, Hartov A, Roberts DW. Data-guided brain deformation modeling: evaluation of a 3-D adjoint inversion method in porcine studies. IEEE Trans Biomed Eng 2006; 53:1893-900. [PMID: 17019852 DOI: 10.1109/tbme.2006.881771] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Biomechanical models of brain deformation are useful tools for estimating parenchymal shift that results during open cranial procedures. Intraoperative data is likely to improve model estimates, but incorporation of such data into the model is not trivial. This study tests the adjoint equations method (AEM) for data assimilation as a viable approach for integrating displacement data into a brain deformation model. AEM was applied to two porcine experiments. AEM-based estimates were compared both to measured displacement data [from computed tomography (CT) scans] and to model solutions obtained without the guidance of sparse data, which we term the best prior estimate (BPE). Additionally, the sensitivity of the AEM solution to inverse parameter selection was investigated. The results suggest that it is most important to estimate the size of the variance in the measurement error correctly, make the correlation length long and estimate displacement (over stress) boundary conditions. Application of AEM shows an average 33% improvement over BPE. This paper represents the first evidence of successful use of the AEM technique in three dimensions with experimental data validation. The guidelines established for selection of model parameters are starting points for further optimization of the method under clinical conditions.
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Affiliation(s)
- Karen E Lunn
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
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Camara O, Schweiger M, Scahill RI, Crum WR, Sneller BI, Schnabel JA, Ridgway GR, Cash DM, Hill DLG, Fox NC. Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE TRANSACTIONS ON MEDICAL IMAGING 2006; 25:1417-30. [PMID: 17117771 DOI: 10.1109/tmi.2006.880588] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.
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Affiliation(s)
- Oscar Camara
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, Department of Computer Science, University College London, London WCEI 6BT, UK
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Duffau H. New concepts in surgery of WHO grade II gliomas: functional brain mapping, connectionism and plasticity – a review. J Neurooncol 2006; 79:77-115. [PMID: 16607477 DOI: 10.1007/s11060-005-9109-6] [Citation(s) in RCA: 161] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2005] [Accepted: 12/21/2005] [Indexed: 10/24/2022]
Abstract
Despite a recent literature supporting the impact of surgery on the natural history of low-grade glioma (LGG), the indications of resection still remain a matter of debate, especially because of the frequent location of these tumors within eloquent brain areas - thus with a risk to induce a permanent postoperative deficit. Therefore, since the antagonist nature of this surgery is to perform the most extensive glioma removal possible, while preserving the function and the quality of life, new concepts were recently applied to LGG resection in order to optimize the benefit/risk ratio of the surgery.First, due to the development of functional mapping methods, namely perioperative neurofunctional imaging and intrasurgical direct electrical stimulation, the study of cortical functional organization is currently possible for each patient - in addition to an extensive neuropsychological assessment. Such knowledge is essential because of the inter-individual anatomo-functional variability, increased in tumors due to cerebral plasticity phenomena. Thus, brain mapping enables to envision and perform a resection according to individual functional boundaries.Second, since LGG invades not only cortical but also subcortical structures, and shows an infiltrative progression along the white matter tracts, new techniques of anatomical tracking and functional mapping of the subcortical white matter pathways were also used with the goal to study the individual effective connectivity - which needs imperatively to be preserved during the resection.Third, the better understanding of brain plasticity mechanisms, induced both by the slow-growing LGG and by the surgery itself, were equally studied in each patient and applied to the surgical strategy by incorporating individual dynamic potential of reorganization into the operative planning. The integration of these new concepts of individual functional mapping, connectivity and plastic potential to the surgery of LGG has allowed an extent of surgical indications, an optimization of the quality of resection (neuro-oncological benefit), and a minimization of the risk of sequelae (benefit on the quality of life). In addition, such a strategy has also fundamental applications, since it represents a new door to the connectionism and cerebral plasticity.
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Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, UMR-S678 Inserm, Hôpital Salpêtrière, Paris, France
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Carter TJ, Sermesant M, Cash DM, Barratt DC, Tanner C, Hawkes DJ. Application of soft tissue modelling to image-guided surgery. Med Eng Phys 2005; 27:893-909. [PMID: 16271490 DOI: 10.1016/j.medengphy.2005.10.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2005] [Revised: 10/10/2005] [Accepted: 10/10/2005] [Indexed: 01/21/2023]
Abstract
The deformation of soft tissue compromises the accuracy of image-guided surgery based on preoperative images, and restricts its applicability to surgery on or near bony structures. One way to overcome these limitations is to combine biomechanical models with sparse intraoperative data, in order to realistically warp the preoperative image to match the surgical situation. We detail the process of biomechanical modelling in the context of image-guided surgery. We focus in particular on the finite element method, which is shown to be a promising approach, and review the constitutive relationships which have been suggested for representing tissue during surgery. Appropriate intraoperative measurements are required to constrain the deformation, and we discuss the potential of the modalities which have been applied to this task. This technology is on the verge of transition into clinical practice, where it promises to increase the guidance accuracy and facilitate less invasive interventions. We describe here how soft tissue modelling techniques have been applied to image-guided surgery applications.
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Affiliation(s)
- Timothy J Carter
- Centre for Medical Image Computing, Malet Place Engineering Building, University College London, Gower Street, London WC1E 6BT, UK.
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Clatz O, Delingette H, Talos IF, Golby AJ, Kikinis R, Jolesz FA, Ayache N, Warfield SK. Robust nonrigid registration to capture brain shift from intraoperative MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2005; 24:1417-27. [PMID: 16279079 PMCID: PMC2042023 DOI: 10.1109/tmi.2005.856734] [Citation(s) in RCA: 125] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We present a new algorithm to register 3-D preoperative magnetic resonance (MR) images to intraoperative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3-D image registration in 35 s (including the image update time) on a heterogeneous cluster of 15 personal computers. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.
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Affiliation(s)
- Olivier Clatz
- Epidaure Research Project, INRIA Sophia Antipolis, 06902 Sophia Antipolis Cedex, France.
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Cash DM, Miga MI, Sinha TK, Galloway RL, Chapman WC. Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements. IEEE TRANSACTIONS ON MEDICAL IMAGING 2005; 24:1479-91. [PMID: 16279084 DOI: 10.1109/tmi.2005.855434] [Citation(s) in RCA: 54] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Image-guided liver surgery requires the ability to identify and compensate for soft tissue deformation in the organ. The predeformed state is represented as a complete three-dimensional surface of the organ, while the intraoperative data is a range scan point cloud acquired from the exposed liver surface. The first step is to rigidly align the coordinate systems of the intraoperative and preoperative data. Most traditional rigid registration methods minimize an error metric over the entire data set. In this paper, a new deformation-identifying rigid registration (DIRR) is reported that identifies and aligns minimally deformed regions of the data using a modified closest point distance cost function. Once a rigid alignment has been established, deformation is accounted for using a linearly elastic finite element model (FEM) and implemented using an incremental framework to resolve geometric nonlinearities. Boundary conditions for the incremental formulation are generated from intraoperatively acquired range scan surfaces of the exposed liver surface. A series of phantom experiments is presented to assess the fidelity of the DIRR and the combined DIRR/FEM approaches separately. The DIRR approach identified deforming regions in 90% of cases under conditions of realistic surgical exposure. With respect to the DIRR/FEM algorithm, subsurface target errors were correctly located to within 4 mm in phantom experiments.
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Affiliation(s)
- David M Cash
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235 USA
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Duffau H. Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 2005; 4:476-86. [PMID: 16033690 DOI: 10.1016/s1474-4422(05)70140-x] [Citation(s) in RCA: 441] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Surgical treatment of low-grade gliomas (LGGs) aims to maximise the amount of tumour tissue resected, while minimising the risk of functional sequelae. In this review I address the issue of how to reconcile these two conflicting goals. First, I review the natural history of LGG-growth, invasion, and anaplastic transformation. Second, I discuss the contribution of new techniques, such as functional mapping, to our understanding of brain reorganisation in response to progressive growth of LGG. Third, I consider the clinical implications of interactions between tumour progression and brain plasticity. In particular, I show how longitudinal studies (preoperative, intraoperative, and postoperative) could allow us to optimise the surgical risk-to-benefit ratios. I will also discuss controversial issues such as defining surgical indications for LGGs, predicting the risk of postoperative deficit, aspects of operative surgical neuro-oncology (eg, preoperative planning and preservation of functional areas and tracts), and postoperative functional recovery.
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Affiliation(s)
- Hugues Duffau
- Department of Neurosurgery, INSERM U678, Hôpital Salpêtrière, Paris, France.
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Gasser T, Ganslandt O, Sandalcioglu E, Stolke D, Fahlbusch R, Nimsky C. Intraoperative functional MRI: Implementation and preliminary experience. Neuroimage 2005; 26:685-93. [PMID: 15955478 DOI: 10.1016/j.neuroimage.2005.02.022] [Citation(s) in RCA: 84] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2004] [Revised: 02/07/2005] [Accepted: 02/17/2005] [Indexed: 11/25/2022] Open
Abstract
For a non-invasive identification of eloquent brain areas in neurosurgical procedures up to now only preoperative functional brain mapping techniques are available. These are based, e.g., on preoperative functional magnetic resonance imaging (fMRI) investigations in awake patients. The aim of this study was to investigate the feasibility to perform fMRI during neurosurgical procedures in anesthetized patients. For that purpose, a passive stimulation paradigm with peripheral nerve stimulation was applied. A 1.5-T MR scanner placed in a radiofrequency-shielded operating room with an adapted operating table was used for intraoperative fMRI. The fMRI data were analyzed during acquisition by an online statistical evaluation package installed on the MR scanner console. In addition, phase reversal of somatosensory evoked potentials was used for verification of intraoperative fMRI. In four anesthetized patients with lesions in the vicinity of the central region a total of 11 fMRI measurements were successfully acquired and analyzed online. Activation was found in the somatosensory cortex, which could be confirmed by intraoperative phase reversal for each measurement. Furthermore, statistical parametric mapping (SPM) was employed for an extensive offline data analysis. We did not observe any neurological deterioration or complications due to the stimulation technique. Intraoperative fMRI is technically feasible allowing a real-time identification of eloquent brain areas despite brain shift.
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Affiliation(s)
- Thomas Gasser
- Department of Neurosurgery, University of Essen, Hufelandstrasse 55, 45122 Essen, Germany.
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Warfield SK, Haker SJ, Talos IF, Kemper CA, Weisenfeld N, Mewes AUJ, Goldberg-Zimring D, Zou KH, Westin CF, Wells WM, Tempany CMC, Golby A, Black PM, Jolesz FA, Kikinis R. Capturing intraoperative deformations: research experience at Brigham and Women's Hospital. Med Image Anal 2004; 9:145-62. [PMID: 15721230 DOI: 10.1016/j.media.2004.11.005] [Citation(s) in RCA: 64] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient's brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women's Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.
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Affiliation(s)
- Simon K Warfield
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
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Sun H, Kennedy FE, Carlson EJ, Hartov A, Roberts DW, Paulsen KD. Modeling of Brain Tissue Retraction Using Intraoperative Data. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2004 2004. [DOI: 10.1007/978-3-540-30136-3_29] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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