1
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Chrisochoides N, Liu Y, Drakopoulos F, Kot A, Foteinos P, Tsolakis C, Billias E, Clatz O, Ayache N, Fedorov A, Golby A, Black P, Kikinis R. Comparison of physics-based deformable registration methods for image-guided neurosurgery. Front Digit Health 2023; 5:1283726. [PMID: 38144260 PMCID: PMC10740151 DOI: 10.3389/fdgth.2023.1283726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/02/2023] [Indexed: 12/26/2023] Open
Abstract
This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.
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Affiliation(s)
- Nikos Chrisochoides
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Yixun Liu
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Fotis Drakopoulos
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Andriy Kot
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Panos Foteinos
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Christos Tsolakis
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Emmanuel Billias
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
| | - Olivier Clatz
- Inria, French Research Institute for Digital Science, Sophia Antipolis, Valbonne, France
| | - Nicholas Ayache
- Inria, French Research Institute for Digital Science, Sophia Antipolis, Valbonne, France
| | - Andrey Fedorov
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA, United States
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Alex Golby
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA, United States
- Image-guided Neurosurgery, Department of Neurosurgery, Harvard Medical School, Boston, MA, United States
| | - Peter Black
- Image-guided Neurosurgery, Department of Neurosurgery, Harvard Medical School, Boston, MA, United States
| | - Ron Kikinis
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA, United States
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2
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Li Y, Hou Y, Li X, Li Q, Lu J, Tang J. Quantitative Validation of the Correlation Between Optimized Pyramidal Tract Delineation After Brain Shift Compensation and Direct Electrical Subcortical Stimulation During Brain Tumor Surgery. J Digit Imaging 2023; 36:1974-1986. [PMID: 37340196 PMCID: PMC10501987 DOI: 10.1007/s10278-023-00867-0] [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: 02/23/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/22/2023] Open
Abstract
It remains unclear whether tractography of pyramidal tracts is correlated with the intraoperative direct electrical subcortical stimulation (DESS), and brain shift further complicates the issue. The objective of this research is to quantitatively verify the correlation between optimized tractography (OT) of pyramidal tracts after brain shift compensation and DESS during brain tumor surgery. OT was performed for 20 patients with lesions in proximity to the pyramidal tracts based on preoperative diffusion-weighted magnetic resonance imaging. During surgery, tumor resection was guided by DESS. A total of 168 positive stimulation points and their corresponding stimulation intensity thresholds were recorded. Using the brain shift compensation algorithm based on hierarchical B-spline grids combined with a Gaussian resolution pyramid, we warped the preoperative pyramidal tract models and used receiver operating characteristic (ROC) curves to investigate the reliability of our brain shift compensation method based on anatomic landmarks. Additionally, the minimum distance between the DESS points and warped OT (wOT) model was measured and correlated with DESS intensity threshold. Brain shift compensation was achieved in all cases, and the area under the ROC curve was 0.96 in the registration accuracy analysis. The minimum distance between the DESS points and the wOT model was found to have a significantly high correlation with the DESS stimulation intensity threshold (r = 0.87, P < 0.001), with a linear regression coefficient of 0.96. Our OT method can provide comprehensive and accurate visualization of the pyramidal tracts for neurosurgical navigation and was quantitatively verified by intraoperative DESS after brain shift compensation.
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Affiliation(s)
- Ye Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China
| | - Yuanzheng Hou
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China
| | - Xiaoyu Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China
| | - Qiongge Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
| | - Jie Tang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China.
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3
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Chrisochoides N, Fedorov A, Liu Y, Kot A, Foteinos P, Drakopoulos F, Tsolakis C, Billias E, Clatz O, Ayache N, Golby A, Black P, Kikinis R. Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects. ARXIV 2023:arXiv:2309.03336v1. [PMID: 37731651 PMCID: PMC10508827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.
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Affiliation(s)
- Nikos Chrisochoides
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Andrey Fedorov
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA
| | - Yixun Liu
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Andriy Kot
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Panos Foteinos
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Fotis Drakopoulos
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Christos Tsolakis
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Emmanuel Billias
- Center for Real-Time Computing, Computer Science Department, Old Dominion University, Norfolk, VA
| | - Olivier Clatz
- Inria, French Research Institute for Digital Science, Sophia Antipolis, France
| | - Nicholas Ayache
- Inria, French Research Institute for Digital Science, Sophia Antipolis, France
| | - Alex Golby
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA
- Image-guided Neurosurgery, Department of Neurosurgery, Harvard Medical School, Boston, MA
| | - Peter Black
- Image-guided Neurosurgery, Department of Neurosurgery, Harvard Medical School, Boston, MA
| | - Ron Kikinis
- Neuroimaging Analysis Center, Department of Radiology, Harvard Medical School, Boston, MA
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4
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Drakopoulos F, Tsolakis C, Angelopoulos A, Liu Y, Yao C, Kavazidi KR, Foroglou N, Fedorov A, Frisken S, Kikinis R, Golby A, Chrisochoides N. Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems. Front Digit Health 2021; 2:613608. [PMID: 34713074 PMCID: PMC8521897 DOI: 10.3389/fdgth.2020.613608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/23/2020] [Indexed: 12/21/2022] Open
Abstract
Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.
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Affiliation(s)
- Fotis Drakopoulos
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States
| | - Christos Tsolakis
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
| | - Angelos Angelopoulos
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
| | - Yixun Liu
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States
| | - Chengjun Yao
- Department of Neurosurgery, Huashan Hospital, Shanghai, China
| | | | - Nikolaos Foroglou
- Department of Neurosurgery, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Alexandra Golby
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.,Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Nikos Chrisochoides
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
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5
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Sajedi H, Pardakhti N. Age Prediction Based on Brain MRI Image: A Survey. J Med Syst 2019; 43:279. [PMID: 31297614 DOI: 10.1007/s10916-019-1401-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 06/25/2019] [Indexed: 01/13/2023]
Abstract
Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs, knee radiographs, dental images and etc. Most of the age prediction researches have mainly been based on images. Since the image processing and Machine Learning (ML) techniques have grown up, the investigations were led to use them in age prediction problem. The implementations would be used in different fields, especially in medical applications. Brain Age Estimation (BAE) has attracted more attention in recent years and it would be so helpful in early diagnosis of some neurodegenerative diseases such as Alzheimer, Parkinson, Huntington, etc. BAE is performed on Magnetic Resonance Imaging (MRI) images to compute the brain ages. Studies based on brain MRI shows that there is a relation between accelerated aging and accelerated brain atrophy. This refers to the effects of neurodegenerative diseases on brain structure while making the whole of it older. This paper reviews and summarizes the main approaches for age prediction based on brain MRI images including preprocessing methods, useful tools used in different research works and the estimation algorithms. We categorize the BAE methods based on two factors, first the way of processing MRI images, which includes pixel-based, surface-based, or voxel-based methods and second, the generation of ML algorithms that includes traditional or Deep Learning (DL) methods. The modern techniques as DL methods help MRI based age prediction to get results that are more accurate. In recent years, more precise and statistical ML approaches have been utilized with the help of related tools for simplifying computations and getting accurate results. Pros and cons of each research and the challenges in each work are expressed and some guidelines and deliberations for future research are suggested.
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Affiliation(s)
- Hedieh Sajedi
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran. .,School of Computer Science, Institute for Research in Fundamental Science (IPM), P.O. Box 19395-5746, Tehran, Iran.
| | - Nastaran Pardakhti
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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6
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Tamura M, Sato I, Maruyama T, Ohshima K, Mangin JF, Nitta M, Saito T, Yamada H, Minami S, Masamune K, Kawamata T, Iseki H, Muragaki Y. Integrated datasets of normalized brain with functional localization using intra-operative electrical stimulation. Int J Comput Assist Radiol Surg 2019; 14:2109-2122. [PMID: 30955195 DOI: 10.1007/s11548-019-01957-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 04/01/2019] [Indexed: 01/22/2023]
Abstract
PURPOSE The purpose of this study was to transform brain mapping data into a digitized intra-operative MRI and integrated brain function dataset for predictive glioma surgery considering tumor resection volume, as well as the intra-operative and postoperative complication rates. METHODS Brain function data were transformed into digitized localizations on a normalized brain using a modified electric stimulus probe after brain mapping. This normalized brain image with functional information was then projected onto individual patient's brain images including predictive brain function data. RESULTS Log data were successfully acquired using a medical device integrated into intra-operative MR images, and digitized brain function was converted to a normalized brain data format in 13 cases. For the electrical stimulation positions in which patients showed speech arrest (SA), speech impairment (SI), motor and sensory responses during cortical mapping processes in awake craniotomy, the data were tagged, and the testing task and electric current for the stimulus were recorded. There were 13 SA, 7 SI, 8 motor and 4 sensory responses (32 responses) in total. After evaluation of transformation accuracy in 3 subjects, the first transformation from intra- to pre-operative MRI using non-rigid registration was calculated as 2.6 ± 1.5 and 2.1 ± 0.9 mm, examining neighboring sulci on the electro-stimulator position and the cortex surface near each tumor, respectively; the second transformation from pre-operative to normalized brain was 1.7 ± 0.8 and 1.4 ± 0.5 mm, respectively, representing acceptable accuracy. CONCLUSION This image integration and transformation method for brain normalization should facilitate practical intra-operative brain mapping. In the future, this method may be helpful for pre-operatively or intra-operatively predicting brain function.
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Affiliation(s)
- Manabu Tamura
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan. .,Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.
| | - Ikuma Sato
- Faculty of System Information Science Engineering, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate City, Hokkaido, 041-8655, Japan
| | - Takashi Maruyama
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.,Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Kazuma Ohshima
- Faculty of System Information Science Engineering, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate City, Hokkaido, 041-8655, Japan
| | - Jean-François Mangin
- The Computer Assisted Neuroimaging Laboratory, Neurospin, Biomedical Imaging Institute, CEA, Centre d'études de Saclay, 91191, Gif-Sur-Yvette, France
| | - Masayuki Nitta
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.,Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Taiichi Saito
- Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Hiroyuki Yamada
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Shinji Minami
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Ken Masamune
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Takakazu Kawamata
- Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Hiroshi Iseki
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Yoshihiro Muragaki
- Faculty of Advanced Techno-Surgery, Institute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, 8-1 (TWIns) Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan.,Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
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7
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Liu Y, Yao C, Drakopoulos F, Wu J, Zhou L, Chrisochoides N. A nonrigid registration method for correcting brain deformation induced by tumor resection. Med Phys 2015; 41:101710. [PMID: 25281949 DOI: 10.1118/1.4893754] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE This paper presents a nonrigid registration method to align preoperative MRI with intraoperative MRI to compensate for brain deformation during tumor resection. This method extends traditional point-based nonrigid registration in two aspects: (1) allow the input data to be incomplete and (2) simulate the underlying deformation with a heterogeneous biomechanical model. METHODS The method formulates the registration as a three-variable (point correspondence, deformation field, and resection region) functional minimization problem, in which point correspondence is represented by a fuzzy assign matrix; Deformation field is represented by a piecewise linear function regularized by the strain energy of a heterogeneous biomechanical model; and resection region is represented by a maximal simply connected tetrahedral mesh. A nested expectation and maximization framework is developed to simultaneously resolve these three variables. RESULTS To evaluate this method, the authors conducted experiments on both synthetic data and clinical MRI data. The synthetic experiment confirmed their hypothesis that the removal of additional elements from the biomechanical model can improve the accuracy of the registration. The clinical MRI experiments on 25 patients showed that the proposed method outperforms the ITK implementation of a physics-based nonrigid registration method. The proposed method improves the accuracy by 2.88 mm on average when the error is measured by a robust Hausdorff distance metric on Canny edge points, and improves the accuracy by 1.56 mm on average when the error is measured by six anatomical points. CONCLUSIONS The proposed method can effectively correct brain deformation induced by tumor resection.
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Affiliation(s)
- Yixun Liu
- The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
| | - Chengjun Yao
- The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China
| | - Fotis Drakopoulos
- The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
| | - Jinsong Wu
- The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China
| | - Liangfu Zhou
- The Department of Neurosurgery, Huashan Hospital, Shanghai 200040, China
| | - Nikos Chrisochoides
- The Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
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8
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Avants BB, Johnson HJ, Tustison NJ. Neuroinformatics and the The Insight ToolKit. Front Neuroinform 2015; 9:5. [PMID: 25859213 PMCID: PMC4374465 DOI: 10.3389/fninf.2015.00005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2015] [Accepted: 03/05/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Brian B Avants
- Penn Image Computing and Science Laboratory, University of Pennsylvania Philadelphia, PA, USA
| | - Hans J Johnson
- Department of Psychiatry, University of Iowa Iowa City, IA, USA
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA
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9
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Drakopoulos F, Foteinos P, Liu Y, Chrisochoides NP. Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection. Front Neuroinform 2014; 8:11. [PMID: 24596553 PMCID: PMC3925835 DOI: 10.3389/fninf.2014.00011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2013] [Accepted: 01/27/2014] [Indexed: 11/16/2022] Open
Abstract
This paper presents an adaptive non-rigid registration method for aligning pre-operative MRI with intra-operative MRI (iMRI) to compensate for brain deformation during brain tumor resection. This method extends a successful existing Physics-Based Non-Rigid Registration (PBNRR) technique implemented in ITKv4.5. The new method relies on a parallel adaptive heterogeneous biomechanical Finite Element (FE) model for tissue/tumor removal depicted in the iMRI. In contrast the existing PBNRR in ITK relies on homogeneous static FE model designed for brain shift only (i.e., it is not designed to handle brain tumor resection). As a result, the new method (1) accurately captures the intra-operative deformations associated with the tissue removal due to tumor resection and (2) reduces the end-to-end execution time to within the time constraints imposed by the neurosurgical procedure. The evaluation of the new method is based on 14 clinical cases with: (i) brain shift only (seven cases), (ii) partial tumor resection (two cases), and (iii) complete tumor resection (five cases). The new adaptive method can reduce the alignment error up to seven and five times compared to a rigid and ITK's PBNRR registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK. Moreover, the total execution time for all the case studies is about 1 min or less in a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM.
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Affiliation(s)
- Fotis Drakopoulos
- CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA
| | - Panagiotis Foteinos
- CRTC Lab and Computer Science, Old Dominion University Norfolk, VA, USA ; Computer Science, College of William and Mary Williamsburg, VA, USA
| | - Yixun Liu
- Radiology and Imaging Science, National Institutes of Health Bethesda, MD, USA
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