A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation

Zhuang, Dongxiao; Liu, Y.-X; Wu, Jinsong; Yao, Chengjun; Mao, Ying; Zhang, C.-X; Meng, Wang,; Wang, W; Zhou, Lei

doi:10.3174/ajnr.a2288

Cited by 22 publications

(16 citation statements)

References 40 publications

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“…An alternative strategy is to build a computational model (e.g., a Finite Element Model) of the brain based on constitutive constraints, which describe the stress-strain response of the tissue under various loading conditions. This model is combined with sparse intraoperative image data to update preoperative images [45, 53–55, 57, 58, 71–76, 81–86]. A summary of the compensation techniques for brain shift is shown in Table 3.…”

Section: Compensation For Intraoperative Brain Deformationmentioning

confidence: 99%

“…Generally, the patient specific volumetric model is computed with preoperative MR images before the surgery. Surface images are acquired with LRS [71–76, 78, 85] or Stereo Vision [78, 81–84]. After the surface images are registered with the model nonrigidly, the volumetric model is deformed considering the boundary conditions.…”

Section: Compensation For Intraoperative Brain Deformationmentioning

confidence: 99%

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Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Bayer

Maier

Ostermeier

et al. 2017

International Journal of Biomedical Imaging

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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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Section: Compensation For Intraoperative Brain Deformationmentioning

confidence: 99%

Section: Compensation For Intraoperative Brain Deformationmentioning

confidence: 99%

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Bayer

Maier

Ostermeier

et al. 2017

International Journal of Biomedical Imaging

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show abstract

“…15,16 Similar approaches with respect to data integration have been proposed using continuum models of elastic and nonlinear viscoelastic models and have moved this direction forward. 77,82,83 In Wittek et al , 77 investigators demonstrate dramatic increases in computational speed for nonlinear models with results reflecting good deep structure alignment over several surgeries with a subsequent study showing similar results over many retrospective patient data sets. 23 …”

Section: Role Of Patient-specific Computational Modeling Towards Braimentioning

confidence: 94%

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

Miga

2015

Ann Biomed Eng

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With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

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“…A sparse intraoperative data driven biomechanical model to compensate for brain shift during neuronavigation [78] Shanghai Neurosurgical Center and others This project presents a brain shift calculation based on a linear elastic model and its implementation in the 3DIMAGE system (developed by the same research group). The precision of the brain deformation compensation of this model was validated with real-time image data acquired from the PoEStar system.…”

Section: University Of Medical Sciencesmentioning

confidence: 99%

Neurosurgery and brain shift: review of the state of the art and main contributions of robotics

Correa-Arana

Vivas-Albán

Navarro

2017

TecnoL.

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This paper presents a review about neurosurgery, robotic assistants in this type of procedure, and the approach to the problem of brain tissue displacement, including techniques for obtaining medical images. It is especially focused on the phenomenon of brain displacement, commonly known as brain shift, which causes a loss of reference between the preoperative images and the volumes to be treated during image-guided surgery. Hypothetically, with brain shift prediction and correction for the neuronavigation system, minimal invasion trajectories could be planned and shortened. This would reduce damage to functional tissues and possibly lower the morbidity and mortality in delicate and demanding medical procedures such as the removal of a brain tumor. This paper also mentions other issues associated with neurosurgery and shows the way robotized systems have helped solve these problems. Finally, it highlights the future perspectives of neurosurgery, a branch of medicine that seeks to treat the ailments of the main organ of the human body from the perspective of many disciplines.

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A Sparse Intraoperative Data-Driven Biomechanical Model to Compensate for Brain Shift during Neuronavigation

Abstract: BACKGROUND AND PURPOSE: Intraoperative brain deformation is an important factor compromising the accuracy of image-guided neurosurgery. The purpose of this study was to elucidate the role of a model-updated image in the compensation of intraoperative brain shift.

Cited by 22 publications

References 40 publications

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

Neurosurgery and brain shift: review of the state of the art and main contributions of robotics

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