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

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          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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          Radial Basis Functions

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            Brain shift in neuronavigation of brain tumors: A review.

            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.
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              Multivariate interpolation at arbitrary points made simple

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                Author and article information

                Journal
                Int J Biomed Imaging
                Int J Biomed Imaging
                IJBI
                International Journal of Biomedical Imaging
                Hindawi
                1687-4188
                1687-4196
                2017
                5 June 2017
                : 2017
                : 6028645
                Affiliations
                1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany
                2Siemens Healthcare GmbH, 91301 Forchheim, Germany
                Author notes

                Academic Editor: D. L. Wilson

                Author information
                http://orcid.org/0000-0003-2874-4805
                http://orcid.org/0000-0002-9550-5284
                Article
                10.1155/2017/6028645
                5476838
                28676821
                92675749-cae7-4f07-a8ef-1ffda4f9f335
                Copyright © 2017 Siming Bayer et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 February 2017
                : 3 May 2017
                Categories
                Review Article

                Radiology & Imaging
                Radiology & Imaging

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