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Monji-Azad S, Männle D, Hesser J, Pohlmann J, Rotter N, Affolter A, Weis CA, Ludwig S, Scherl C. Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery. Biomed Hub 2024; 9:9-15. [PMID: 38322041 PMCID: PMC10845096 DOI: 10.1159/000535421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/20/2023] [Indexed: 02/08/2024] Open
Abstract
Introduction A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C. Results Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 μL than the branched RC with 0.5 μL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461). Conclusions The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.
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Affiliation(s)
- Sara Monji-Azad
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - David Männle
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jürgen Hesser
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Central Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
- CZS Heidelberg Center for Model-Based AI, Heidelberg University, Heidelberg, Germany
| | - Jan Pohlmann
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nicole Rotter
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Annette Affolter
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Cleo Aron Weis
- Pathological Institute, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Sonja Ludwig
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Claudia Scherl
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg, Germany
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Safdar S, Zwick BF, Yu Y, Bourantas GC, Joldes GR, Warfield SK, Hyde DE, Frisken S, Kapur T, Kikinis R, Golby A, Nabavi A, Wittek A, Miller K. SlicerCBM: automatic framework for biomechanical analysis of the brain. Int J Comput Assist Radiol Surg 2023; 18:1925-1940. [PMID: 37004646 PMCID: PMC10497672 DOI: 10.1007/s11548-023-02881-7] [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: 09/29/2022] [Accepted: 03/17/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations. METHODS We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI. RESULTS Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI. CONCLUSION Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.
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Affiliation(s)
- Saima Safdar
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia.
| | - Benjamin F Zwick
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Yue Yu
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - George C Bourantas
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Department of Agriculture, University of Patras Nea Ktiria, 30200, Campus Mesologhi, Greece
| | - Grand R Joldes
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Damon E Hyde
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sarah Frisken
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ron Kikinis
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Arya Nabavi
- Department of Neurosurgery, KRH Klinikum Nordstadt, Hannover, Germany
| | - Adam Wittek
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Karol Miller
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Harvard Medical School, Boston, MA, USA
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Männle D, Pohlmann J, Monji-Azad S, Hesser J, Rotter N, Affolter A, Lammert A, Kramer B, Ludwig S, Huber L, Scherl C. Artificial intelligence directed development of a digital twin to measure soft tissue shift during head and neck surgery. PLoS One 2023; 18:e0287081. [PMID: 37556451 PMCID: PMC10411805 DOI: 10.1371/journal.pone.0287081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 07/14/2023] [Indexed: 08/11/2023] Open
Abstract
Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence approach. This framework was applied suggesting feasibility of soft tissue shift detection as a hitherto unsolved problem. In a pig head cadaver model 104 soft tissue resection had been performed. The surface of the removed soft tissue (RTP) and the corresponding resection cavity (RC) was scanned (N = 416) to train an artificial intelligence (AI) with two different 3D object detectors (HoloLens 2; ArtecEva). An artificial tissue shift (TS) was created by changing the tissue temperature from 7,91±4,1°C to 36,37±1,28°C. Digital twins of RTP and RC in cold and warm conditions had been generated and volumes were calculated based on 3D surface meshes. Significant differences in number of vertices created by the different 3D scanners (HoloLens2 51313 vs. ArtecEva 21694, p<0.0001) hence result in differences in volume measurement of the RTC (p = 0.0015). A significant TS could be induced by changing the temperature of the tissue of RC (p = 0.0027) and RTP (p = <0.0001). RC showed more correlation in TS by heating than RTP with a volume increase of 3.1 μl or 9.09% (p = 0.449). Cadaver models are suitable for training a machine learning model for deformable registration through creation of a digital twin. Despite different point cloud densities, HoloLens and ArtecEva provide only slightly different estimates of volume. This means that both devices can be used for the task.TS can be simulated and measured by temperature change, in which RC and RTP react differently. This corresponds to the clinical behaviour of tumour and resection cavity during surgeries, which could be used for frozen section management and a range of other clinical applications.
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Affiliation(s)
- David Männle
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jan Pohlmann
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sara Monji-Azad
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jürgen Hesser
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Central Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
- CZS Heidelberg Center for Model-Based AI, Heidelberg University, Heidelberg, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg University, Heidelberg, Germany
| | - Nicole Rotter
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Annette Affolter
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anne Lammert
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Benedikt Kramer
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sonja Ludwig
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lena Huber
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Claudia Scherl
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg University, Heidelberg, Germany
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Abdolkarimzadeh F, Ashory MR, Ghasemi-Ghalebahman A, Karimi A. A position- and time-dependent pressure profile to model viscoelastic mechanical behavior of the brain tissue due to tumor growth. Comput Methods Biomech Biomed Engin 2023; 26:660-672. [PMID: 35638726 PMCID: PMC9708950 DOI: 10.1080/10255842.2022.2082245] [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: 02/12/2022] [Revised: 04/06/2022] [Accepted: 05/23/2022] [Indexed: 11/03/2022]
Abstract
This study proposed a computational framework to calculate the resultant position- and time-dependent pressure profile on the brain tissue due to tumor growth. A finite element (FE) patch of the brain tissue was constructed and an inverse dynamic FE-optimization algorithm was used to calculate its viscoelastic mechanical properties under compressive uniaxial loading. Two patient-specific post-tumor resection FE models were input to the FE-optimization algorithm to calculate the optimized 3rd-order position-dependent and normal distribution time-dependent pressure profile parameters. The optimized viscoelastic material properties, the most suitable simulation time, and the optimized 3rd-order position- and -time-dependent pressure profiles were calculated.
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Affiliation(s)
| | | | | | - Alireza Karimi
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL, United States
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Bennion NJ, Zappalá S, Potts M, Woolley M, Marshall D, Evans SL. In vivo measurement of human brain material properties under quasi-static loading. J R Soc Interface 2022; 19:20220557. [PMID: 36514891 PMCID: PMC9748497 DOI: 10.1098/rsif.2022.0557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid-structure interaction representation of the pia-arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, -19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.
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Affiliation(s)
| | - Stefano Zappalá
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK,Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Matthew Potts
- School of Engineering, Cardiff University, Cardiff CF10 3AT, UK
| | - Max Woolley
- Functional Neurosurgery Research Group, School of Clinical Sciences, University of Bristol, Bristol, UK,Renishaw Neuro Solutions Ltd, Wotton Road, Wotton-under-Edge GL12 8SP, UK
| | - David Marshall
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
| | - Sam L. Evans
- School of Engineering, Cardiff University, Cardiff CF10 3AT, UK
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Haouchine N, Juvekar P, Nercessian M, Wells W, Golby A, Frisken S. Pose Estimation and Non-Rigid Registration for Augmented Reality During Neurosurgery. IEEE Trans Biomed Eng 2022; 69:1310-1317. [PMID: 34543188 PMCID: PMC9007221 DOI: 10.1109/tbme.2021.3113841] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE A craniotomy is the removal of a part of the skull to allow surgeons to have access to the brain and treat tumors. When accessing the brain, a tissue deformation occurs and can negatively influence the surgical procedure outcome. In this work, we present a novel Augmented Reality neurosurgical system to superimpose pre-operative 3D meshes derived from MRI onto a view of the brain surface acquired during surgery. METHODS Our method uses cortical vessels as main features to drive a rigid then non-rigid 3D/2D registration. We first use a feature extractor network to produce probability maps that are fed to a pose estimator network to infer the 6-DoF rigid pose. Then, to account for brain deformation, we add a non-rigid refinement step formulated as a Shape-from-Template problem using physics-based constraints that helps propagate the deformation to sub-cortical level and update tumor location. RESULTS We tested our method retrospectively on 6 clinical datasets and obtained low pose error, and showed using synthetic dataset that considerable brain shift compensation and low TRE can be achieved at cortical and sub-cortical levels. CONCLUSION The results show that our solution achieved accuracy below the actual clinical errors demonstrating the feasibility of practical use of our system. SIGNIFICANCE This work shows that we can provide coherent Augmented Reality visualization of 3D cortical vessels observed through the craniotomy using a single camera view and that cortical vessels provide strong features for performing both rigid and non-rigid registration.
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Bouattour Y, Sautou V, Hmede R, El Ouadhi Y, Gouot D, Chennell P, Lapusta Y, Chapelle F, Lemaire JJ. A Minireview on Brain Models Simulating Geometrical, Physical, and Biochemical Properties of the Human Brain. Front Bioeng Biotechnol 2022; 10:818201. [PMID: 35419353 PMCID: PMC8996142 DOI: 10.3389/fbioe.2022.818201] [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: 11/19/2021] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
There is a growing body of evidences that brain surrogates will be of great interest for researchers and physicians in the medical field. They are currently mainly used for education and training purposes or to verify the appropriate functionality of medical devices. Depending on the purpose, a variety of materials have been used with specific and accurate mechanical and biophysical properties, More recently they have been used to assess the biocompatibility of implantable devices, but they are still not validated to study the migration of leaching components from devices. This minireview shows the large diversity of approaches and uses of brain phantoms, which converge punctually. All these phantoms are complementary to numeric models, which benefit, reciprocally, of their respective advances. It also suggests avenues of research for the analysis of leaching components from implantable devices.
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Affiliation(s)
- Yassine Bouattour
- Université Clermont Auvergne, CHU Clermont Ferrand, Clermont Auvergne INP, CNRS, ICCF, F-63000, Clermont-Ferrand, France
- *Correspondence: Yassine Bouattour, ; Jean-Jacques Lemaire,
| | - Valérie Sautou
- Université Clermont Auvergne, CHU Clermont Ferrand, Clermont Auvergne INP, CNRS, ICCF, F-63000, Clermont-Ferrand, France
| | - Rodayna Hmede
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Youssef El Ouadhi
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
- Service de Neurochirurgie, CHU Clermont Ferrand, F-63000, Clermont-Ferrand, France
| | - Dimitri Gouot
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Philip Chennell
- Université Clermont Auvergne, CHU Clermont Ferrand, Clermont Auvergne INP, CNRS, ICCF, F-63000, Clermont-Ferrand, France
| | - Yuri Lapusta
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Frédéric Chapelle
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
| | - Jean-Jacques Lemaire
- Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
- Service de Neurochirurgie, CHU Clermont Ferrand, F-63000, Clermont-Ferrand, France
- *Correspondence: Yassine Bouattour, ; Jean-Jacques Lemaire,
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Farnia P, Makkiabadi B, Alimohamadi M, Najafzadeh E, Basij M, Yan Y, Mehrmohammadi M, Ahmadian A. Photoacoustic-MR Image Registration Based on a Co-Sparse Analysis Model to Compensate for Brain Shift. SENSORS 2022; 22:s22062399. [PMID: 35336570 PMCID: PMC8954240 DOI: 10.3390/s22062399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/16/2021] [Accepted: 11/18/2021] [Indexed: 12/13/2022]
Abstract
Brain shift is an important obstacle to the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging to update the image-guided surgery systems. However, due to the innate limitations of the current imaging modalities, accurate brain shift compensation continues to be a challenging task. In this study, the application of intra-operative photoacoustic imaging and registration of the intra-operative photoacoustic with pre-operative MR images are proposed to compensate for brain deformation. Finding a satisfactory registration method is challenging due to the unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for photoacoustic-MR image registration, which can capture the interdependency of the two modalities. The proposed algorithm works based on the minimization of mapping transform via a pair of analysis operators that are learned by the alternating direction method of multipliers. The method was evaluated using an experimental phantom and ex vivo data obtained from a mouse brain. The results of the phantom data show about 63% improvement in target registration error in comparison with the commonly used normalized mutual information method. The results proved that intra-operative photoacoustic images could become a promising tool when the brain shift invalidates pre-operative MRI.
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Affiliation(s)
- Parastoo Farnia
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Bahador Makkiabadi
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Maysam Alimohamadi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran;
| | - Ebrahim Najafzadeh
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
| | - Maryam Basij
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
| | - Yan Yan
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
| | - Mohammad Mehrmohammadi
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; (M.B.); (Y.Y.)
- Barbara Ann Karmanos Cancer Institute, Detroit, MI 48201, USA
- Correspondence: (M.M.); (A.A.)
| | - Alireza Ahmadian
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 1417653761, Iran; (P.F.); (B.M.); (E.N.)
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran
- Correspondence: (M.M.); (A.A.)
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Abdolkarimzadeh F, Ashory MR, Ghasemi-Ghalebahman A, Karimi A. Inverse dynamic finite element-optimization modeling of the brain tumor mass-effect using a variable pressure boundary. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106476. [PMID: 34715517 DOI: 10.1016/j.cmpb.2021.106476] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Statistical atlases of brain structure can potentially contribute in the surgical and radiotherapeutic treatment planning for the brain tumor patients. However, the current brain image-registration methods lack of accuracy when it comes to the mass-effect caused by tumor growth. Numerical simulations, such as finite element method (FEM), allow us to calculate the resultant pressure and deformation in the brain tissue due to tumor growth, and to predict the mass-effect. To date, however, the pressure boundary in the brain tissue due to tumor growth has been simply presented as a constant profile throughout the entire tumor outer surface that resulted in discrepancy between the patient imaging data and brain atlases. METHODS In this study, we employed a fully-coupled inverse dynamic FE-optimization method to estimate the resultant variable pressure boundary due to tumor resection surgery. To do that, magnetic resonance imaging data of two patients' pre- and post-tumor resection surgery were registered, segmented, volume-meshed, and prepared for fully-coupled inverse dynamic FE-optimization simulations. Two different pressure boundaries were defined on the brain cavity after tumor resection including: a) a constant pressure boundary and b) a variable pressure boundary. The inverse FE-optimization algorithm was used to find the optimum constant and variable pressure boundaries that result in the least distance between the surface-nodes of the post-surgery brain cavity and pre-surgery tumor. RESULTS The results revealed that a variable pressure boundary causes a considerably lower mean percentage error compared to a constant pressure one; hence, it can more effectively address the realistic boundary in tumor resection surgery and predict the mass-effect. CONCLUSIONS The proposed variable pressure boundary can be a robust tool that allows batch processing to register the brains with tumors to statistical atlases of normal brains and construction of brain tumor atlases. This approach is also computationally inexpensive and can be coupled to any FE software to run. The findings of this study have implications for not only predicting the accurate pressure boundary and mass-effect before tumor resection surgery, but also for predicting some clinical symptoms of brain cancers and presenting useful tools for APPLICATIONs in image-guided neurosurgery.
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Affiliation(s)
| | | | | | - Alireza Karimi
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL, United States.
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Lesage AC, Simmons A, Sen A, Singh S, Chen M, Cazoulat G, Weinberg JS, Brock KK. Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data. Phys Med Biol 2021; 66. [PMID: 34469879 DOI: 10.1088/1361-6560/ac22dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/01/2021] [Indexed: 11/11/2022]
Abstract
Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation.
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Affiliation(s)
- Anne-Cecile Lesage
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Alexis Simmons
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Anando Sen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Simran Singh
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Melissa Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Jeffrey S Weinberg
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
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11
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Zappalá S, Bennion NJ, Potts MR, Wu J, Kusmia S, Jones DK, Evans SL, Marshall D. Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery. Sci Rep 2021; 11:17684. [PMID: 34480073 PMCID: PMC8417262 DOI: 10.1038/s41598-021-97150-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/13/2021] [Indexed: 11/15/2022] Open
Abstract
Positional brain shift (PBS), the sagging of the brain under the effect of gravity, is comparable in magnitude to the margin of error for the success of stereotactic interventions ([Formula: see text] 1 mm). This non-uniform shift due to slight differences in head orientation can lead to a significant discrepancy between the planned and the actual location of surgical targets. Accurate in-vivo measurements of this complex deformation are critical for the design and validation of an appropriate compensation to integrate into neuronavigational systems. PBS arising from prone-to-supine change of head orientation was measured with magnetic resonance imaging on 11 young adults. The full-field displacement was extracted on a voxel-basis via digital volume correlation and analysed in a standard reference space. Results showed the need for target-specific correction of surgical targets, as a significant displacement ranging from 0.52 to 0.77 mm was measured at surgically relevant structures. Strain analysis further revealed local variability in compressibility: anterior regions showed expansion (both volume and shape change), whereas posterior regions showed small compression, mostly dominated by shape change. Finally, analysis of correlation demonstrated the potential for further patient- and intervention-specific adjustments, as intra-cranial breadth and head tilt correlated with PBS reaching statistical significance.
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Affiliation(s)
- Stefano Zappalá
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
| | | | | | - Jing Wu
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Medical Image Computing, University College London, London, UK
- MRI Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Sam L Evans
- School of Engineering, Cardiff University, Cardiff, UK
| | - David Marshall
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
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12
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Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. SENSORS 2021; 21:s21113827. [PMID: 34073145 PMCID: PMC8199064 DOI: 10.3390/s21113827] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 01/29/2023]
Abstract
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.
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Affiliation(s)
- Gemma Urbanos
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Alberto Martín
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Guillermo Vázquez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Marta Villanueva
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Manuel Villa
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - Luis Jimenez-Roldan
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Miguel Chavarrías
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
- Correspondence:
| | - Alfonso Lagares
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain; (L.J.-R.); (A.L.)
| | - Eduardo Juárez
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
| | - César Sanz
- Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain; (G.U.); (A.M.); (G.V.); (M.V.); (M.V.); (E.J.); (C.S.)
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13
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Chel H, Bora PK, Ramchiary KK. A fast technique for hyper-echoic region separation from brain ultrasound images using patch based thresholding and cubic B-spline based contour smoothing. ULTRASONICS 2021; 111:106304. [PMID: 33360770 DOI: 10.1016/j.ultras.2020.106304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 11/14/2020] [Accepted: 11/14/2020] [Indexed: 06/12/2023]
Abstract
Ultrasound image guided brain surgery (UGBS) requires an automatic and fast image segmentation method. The level-set and active contour based algorithms have been found to be useful for obtaining topology-independent boundaries between different image regions. But slow convergence limits their use in online US image segmentation. The performance of these algorithms deteriorates on US images because of the intensity inhomogeneity. This paper proposes an effective region-driven method for the segmentation of hyper-echoic (HE) regions suppressing the hypo-echoic and anechoic regions in brain US images. An automatic threshold estimation scheme is developed with a modified Niblack's approach. The separation of the hyper-echoic and non-hyper-echoic (NHE) regions is performed by successively applying patch based intensity thresholding and boundary smoothing. First, a patch based segmentation is performed, which separates roughly the two regions. The patch based approach in this process reduces the effect of intensity heterogeneity within an HE region. An iterative boundary correction step with reducing patch size improves further the regional topology and refines the boundary regions. For avoiding the slope and curvature discontinuities and obtaining distinct boundaries between HE and NHE regions, a cubic B-spline model of curve smoothing is applied. The proposed method is 50-100 times faster than the other level-set based image segmentation algorithms. The segmentation performance and the convergence speed of the proposed method are compared with four other competing level-set based algorithms. The computational results show that the proposed segmentation approach outperforms other level-set based techniques both subjectively and objectively.
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Affiliation(s)
- Haradhan Chel
- Department of Electronics and Communication, Central Institute of Technology Kokrajhar, Assam 783370, India; City Clinic and Research Centre, Kokrajhar, Assam, India.
| | - P K Bora
- Department of EEE, Indian Institute of Technology Guwahati, Assam, India.
| | - K K Ramchiary
- City Clinic and Research Centre, Kokrajhar, Assam, India.
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14
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Haouchine N, Juvekar P, Wells WM, Cotin S, Golby A, Frisken S. Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12264:735-744. [PMID: 33778818 PMCID: PMC7999185 DOI: 10.1007/978-3-030-59719-1_71] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Intra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3D data onto the deformed brain surface viewed through a surgical microscope. We formulate our non-rigid registration as a Shape-from-Template problem. A pre-operative 3D wire-like deformable model is registered onto a single 2D image of the cortical vessels, which is automatically segmented. This 3D/2D registration drives the underlying brain structures, such as tumors, and compensates for the brain shift in sub-cortical regions. We evaluated our approach on simulated and real data composed of 6 patients. It achieved good quantitative and qualitative results making it suitable for neurosurgical guidance.
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Affiliation(s)
- Nazim Haouchine
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Parikshit Juvekar
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - William M Wells
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambdridge, MA, USA
| | | | - Alexandra Golby
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah Frisken
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
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15
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Narasimhan S, Weis JA, Luo M, Simpson AL, Thompson RC, Miga MI. Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection. J Med Imaging (Bellingham) 2020; 7:031506. [PMID: 32613027 DOI: 10.1117/1.jmi.7.3.031506] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 06/05/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. Approach: This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of n = 756 simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of n = 84 deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. Results: In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. Conclusions: These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.
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Affiliation(s)
- Saramati Narasimhan
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Jared A Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States
| | - Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Amber L Simpson
- Queen's University, Department of Biomedical and Molecular Sciences, Ontario, Canada
| | - Reid C Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
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16
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Luo M, Larson PS, Martin AJ, Miga MI. Accounting for Deformation in Deep Brain Stimulation Surgery With Models: Comparison to Interventional Magnetic Resonance Imaging. IEEE Trans Biomed Eng 2020; 67:2934-2944. [PMID: 32078527 DOI: 10.1109/tbme.2020.2974102] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The efficacy of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be jeopardized by brain shift due to burr hole and dura opening during surgery. Brain shift violates assumed rigid alignment between preoperative image and intraoperative anatomy, negatively impacting therapy. OBJECTIVE This study presents a deformation-atlas biomechanical model-based approach to address shift. METHODS Six patients, who underwent interventional magnetic resonance (iMR) image-guided DBS burr hole surgery, were studied. A patient-specific model was employed under varying surgical conditions, generating a collection of possible intraoperative shift estimations or a 'deformation atlas.' An inverse problem was driven by sparse measurements derived from iMR to determine an optimal fit of solutions of the atlas. This fit was then used to obtain a volumetric deformation field, which was utilized to update preoperative MR and estimate shift at surgical target region localized on iMR. Model performance was examined by quantitatively comparing intraoperative subsurface measurements to their model-predicted counterparts, and qualitatively comparing iMR, preoperative MR, and model updated MR. A nonrigid image registration was introduced as a comparator. RESULTS Model-based approach reduced general parenchyma shift from 8.2 ± 2.2 to 2.7 ± 1.1 mm (∼66.8% correction), and produced updated MR with better agreement to iMR than that of preoperative MR. The average model estimated shift at target region was 1.2 mm. CONCLUSIONS This study demonstrates the feasibility of a model-based shift correction strategy in DBS surgery with only sparse data. SIGNIFICANCE The developed strategy has the potential to complement and/or enhance current clinical approaches in addressing shift.
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17
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Haouchine N, Juvekar P, Golby A, Wells WM, Cotin S, Frisken S. Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shift. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11315:113151V. [PMID: 33840881 PMCID: PMC8035814 DOI: 10.1117/12.2547620] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor. Through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagate cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance.
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Affiliation(s)
- Nazim Haouchine
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Parikshit Juvekar
- Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra Golby
- Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - William M Wells
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Sarah Frisken
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
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18
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Haouchine N, Juvekar P, Golby A, Frisken S. Predicted Microscopic Cortical Brain Images for Optimal Craniotomy Positioning and Visualization. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2020; 9:407-413. [PMID: 34676151 DOI: 10.1080/21681163.2020.1834874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
During a craniotomy, the skull is opened to allow surgeons to have access to the brain and perform the procedure. The position and size of this opening are chosen in a way to avoid critical structures, such as vessels, and facilitate the access to tumors. Planning the operation is done based on pre-operative images and does not account for intra-operative surgical events. We present a novel image-guided neurosurgical system to optimize the craniotomy opening. Using physics-based modeling we define a cortical deformation map that estimates the displacement field at candidate craniotomy locations. This deformation map is coupled with an image analogy algorithm that produces realistic synthetic images that can be used to predict both the geometry and the appearance of the brain surface before opening the skull. These images account for cortical vessel deformations that may occur after opening the skull and is rendered in a way that increases the surgeon's understanding and assimilation. Our method was tested retrospectively on patients data showing good results and demonstrating the feasibility of practical use of our system.
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Affiliation(s)
- Nazim Haouchine
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Pariskhit Juvekar
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra Golby
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah Frisken
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
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19
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Yang X, Narasimhan S, Luo M, Thompson RC, Chambless LB, Morone PJ, He L, Dawant BM, Miga MI. Development and evaluation of a "trackerless" surgical planning and guidance system based on 3D Slicer. J Med Imaging (Bellingham) 2019; 6:035002. [PMID: 31528660 DOI: 10.1117/1.jmi.6.3.035002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 08/02/2019] [Indexed: 11/14/2022] Open
Abstract
Conventional optical tracking systems use cameras sensitive to near-infrared (NIR) light and NIR illuminated/active-illuminating markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely used within the neurosurgical theater and is a staple in the standard of care for craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure in the OR with the patient in a fixed head orientation. We propose a framework to achieve this in the OR without conventional tracking technology, i.e., a "trackerless" approach. Briefly, we investigate an extension of the 3D Slicer which combines surgical planning and craniotomy designation. While taking advantage of the well-developed 3D Slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with an intraoperative magnetic resonance imaging-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without the use of a conventional optical tracking technology. To test this approach, four experienced neurosurgeons performed experiments on five different surgical cases using our 3D Slicer module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this craniotomy planning approach and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental realization of an integrated trackerless surgical guidance platform.
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Affiliation(s)
- Xiaochen Yang
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States
| | - Saramati Narasimhan
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Reid C Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Lola B Chambless
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Peter J Morone
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Le He
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Benoit M Dawant
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States.,Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Radiology, Nashville, Tennessee, United States
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Radiology, Nashville, Tennessee, United States
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20
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Frisken S, Luo M, Juvekar P, Bunevicius A, Machado I, Unadkat P, Bertotti MM, Toews M, Wells WM, Miga MI, Golby AJ. A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection. Int J Comput Assist Radiol Surg 2019; 15:75-85. [PMID: 31444624 DOI: 10.1007/s11548-019-02057-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 08/14/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data. METHODS Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation. RESULTS The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods. CONCLUSIONS In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.
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Affiliation(s)
- Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
| | - Ma Luo
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Parikshit Juvekar
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Adomas Bunevicius
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Ines Machado
- Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal
| | - Prashin Unadkat
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Melina M Bertotti
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Matt Toews
- Département de Génie des Systems, Ecole de Technologie Superieure, Montreal, Canada
| | - William M Wells
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.,Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
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21
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Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR. Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection. Front Oncol 2019; 9:426. [PMID: 31192130 PMCID: PMC6549594 DOI: 10.3389/fonc.2019.00426] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.
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Affiliation(s)
- Jamie D Costabile
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Elsa Alaswad
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Shawn D'Souza
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - John A Thompson
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - D Ryan Ormond
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
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22
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Bunyaratavej K, Wangsawatwong P. Catheter guided cerebral glioma resection combined with awake craniotomy: its usefulness and surgical outcome. Br J Neurosurg 2019; 33:528-535. [PMID: 30860928 DOI: 10.1080/02688697.2019.1587380] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Purpose: A challenging aspect of glioma surgery is to distinguish tumour tissue from surrounding eloquent structures and perform resection with accuracy. Various technologies have been used to address this issue including neuronavigator, intraoperative magnetic resonant imaging, intraoperative ultrasound, and fluorescence, each of which has certain drawbacks and limitations. In this study, authors demonstrate the technique of using stereotactically placed catheters as guidance during cerebral glioma resection and report the surgical outcomes. Materials and methods: This study included patients with intrinsic cerebral tumour adjacent to the eloquent structures. Catheter trajectories were planned using three-dimensional cerebral reconstruction on navigation software and catheters were stereotactically placed to mark the intended extent of resection. All craniotomies were performed in awake fashion under neurophysiologic mapping and continuous physical examination for safe maximal resection. Clinical outcome and intended versus actual extent of resection were analysed. Results: Between January 2015 and December 2016, 15 consecutive patients (8 males and 7 females) with intrinsic cerebral tumour underwent craniotomy with this technique. Median age was 43 years. Seven patients (46.7%) had worsening neurological status within 24 h postoperatively. Of these 7 patients, 6 patients (85.7%) regained preoperative neurological status by 6 months. The intended extent of resections were total, subtotal and partial in 3 (20%), 9 (60%), and 3 (20%) patients, respectively. The actual extent of resections were total, subtotal and partial in 3 (20%), 8(53.3%), and 4 (26.7%) patients, respectively. There were no catheter related complications. There was no 30-day postoperative mortality. Conclusions: Catheter guided resection along with awake surgery and neurophysiologic monitoring is a valid technique for infiltrative tumour, especially for ones locating near eloquent structures where the margin of error is low. This is a simple and economical technique which requires only standard equipment widely available to neurosurgical operating theatres.
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Affiliation(s)
- Krishnapundha Bunyaratavej
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society , Bangkok , Thailand
| | - Piyanat Wangsawatwong
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society , Bangkok , Thailand
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23
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Léger É, Reyes J, Drouin S, Collins DL, Popa T, Kersten-Oertel M. Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system. Healthc Technol Lett 2018; 5:137-142. [PMID: 30800320 PMCID: PMC6372086 DOI: 10.1049/htl.2018.5063] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 08/20/2018] [Indexed: 01/02/2023] Open
Abstract
In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors' method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.
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Affiliation(s)
- Étienne Léger
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
| | - Jonatan Reyes
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
| | - Simon Drouin
- Department of Biomedical Engineering, McGill University, Montréal, Canada
| | - D. Louis Collins
- Department of Biomedical Engineering, McGill University, Montréal, Canada
| | - Tiberiu Popa
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
- PERFORM Centre, Concordia University, Montréal, Canada
| | - Marta Kersten-Oertel
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
- PERFORM Centre, Concordia University, Montréal, Canada
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24
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Morin F, Courtecuisse H, Reinertsen I, Le Lann F, Palombi O, Payan Y, Chabanas M. Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation. Med Image Anal 2017. [DOI: 10.1016/j.media.2017.06.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI. Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery. J Med Imaging (Bellingham) 2017; 4:035003. [PMID: 28924573 PMCID: PMC5596210 DOI: 10.1117/1.jmi.4.3.035003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 08/21/2017] [Indexed: 11/14/2022] Open
Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
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Affiliation(s)
- Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Sarah F. Frisken
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Jared A. Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States
| | - Logan W. Clements
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Prashin Unadkat
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Reid C. Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Alexandra J. Golby
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University, Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
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26
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Grabner G, Haider T, Glassner M, Rauscher A, Traxler H, Trattnig S, Robinson SD. Post Mortem Validation of MRI-Identified Veins on the Surface of the Cerebral Cortex as Potential Landmarks for Neurosurgery. Front Neurosci 2017; 11:355. [PMID: 28680389 PMCID: PMC5478689 DOI: 10.3389/fnins.2017.00355] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 06/07/2017] [Indexed: 01/18/2023] Open
Abstract
Background and Objective: Image-guided neurosurgery uses information from a wide spectrum of methods to inform the neurosurgeon's judgement about which tissue to resect and which to spare. Imaging data are registered to the patient's skull so that they correspond to the intraoperative macro- and microscopic view. The correspondence between imaging and optical systems breaks down during surgery, however, as a result of cerebro-spinal fluid drain age, tissue resection, and gravity-based brain shift. In this work we investigate whether a map of surface veins, automatically segmented from MRI, could serve as additional reference system. Methods: Gradient-echo based T2*-weighted imaging was performed on two human cadavers heads using a 7 Tesla MRI scanner. Automatic vessel segmentation was performed using the Frangi vesselness filter, and surface renderings of vessels compared with photographs of the surface of the brain following craniotomy. Results: A high level of correspondence was established between vessel maps and the post autopsy photographs. Corresponding veins, including the prominent superior anastomotic veins, could be identified in all brain lobes. Conclusion: Automatic surface vessel segmentation is feasible and the high correspondence to post autopsy photographs indicates that they could be used as an additional reference system for image-guided neurosurgery in order to maintain the correspondence between imaging and optical systems.This has the advantage over a skull-based reference system that veins are clearly visible to the surgeon and move and deform with the underlying tissue, potentially making this surface net of landmarks robust to brain shift.
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Affiliation(s)
- Günther Grabner
- Department of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Centre, Medical University of ViennaVienna, Austria.,Department of Radiologic Technology, Carinthia University of Applied SciencesKlagenfurt, Austria
| | - Thomas Haider
- Department of Trauma Surgery, Medical University of ViennaVienna, Austria
| | - Mark Glassner
- Department of Photography, University of Applied ArtsVienna, Austria
| | - Alexander Rauscher
- Division of Neurology, Department of Pediatrics, University of British ColumbiaVancouver, BC, Canada.,UBC MRI Research Centre, University of British ColumbiaVancouver, BC, Canada
| | - Hannes Traxler
- Center of Anatomy and Cell Biology, Medical University of ViennaVienna, Austria
| | - Siegfried Trattnig
- Department of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Centre, Medical University of ViennaVienna, Austria
| | - Simon D Robinson
- Department of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Centre, Medical University of ViennaVienna, Austria
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27
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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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28
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Vijayan RC, Thompson RC, Chambless LB, Morone PJ, He L, Clements LW, Griesenauer RH, Kang H, Miga MI. Android application for determining surgical variables in brain-tumor resection procedures. J Med Imaging (Bellingham) 2017; 4:015003. [PMID: 28331887 DOI: 10.1117/1.jmi.4.1.015003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 02/13/2017] [Indexed: 11/14/2022] Open
Abstract
The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.
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Affiliation(s)
- Rohan C Vijayan
- Vanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Reid C Thompson
- Vanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Lola B Chambless
- Vanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Peter J Morone
- Vanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Le He
- Vanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Logan W Clements
- Vanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Rebekah H Griesenauer
- Vanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Hakmook Kang
- Vanderbilt University Medical Center , Department of Biostatistics, Nashville, Tennessee, United States
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States; Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States; Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
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