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Bopp MHA, Grote A, Gjorgjevski M, Pojskic M, Saß B, Nimsky C. Enabling Navigation and Augmented Reality in the Sitting Position in Posterior Fossa Surgery Using Intraoperative Ultrasound. Cancers (Basel) 2024; 16:1985. [PMID: 38893106 PMCID: PMC11171013 DOI: 10.3390/cancers16111985] [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: 04/03/2024] [Revised: 05/09/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Despite its broad use in cranial and spinal surgery, navigation support and microscope-based augmented reality (AR) have not yet found their way into posterior fossa surgery in the sitting position. While this position offers surgical benefits, navigation accuracy and thereof the use of navigation itself seems limited. Intraoperative ultrasound (iUS) can be applied at any time during surgery, delivering real-time images that can be used for accuracy verification and navigation updates. Within this study, its applicability in the sitting position was assessed. Data from 15 patients with lesions within the posterior fossa who underwent magnetic resonance imaging (MRI)-based navigation-supported surgery in the sitting position were retrospectively analyzed using the standard reference array and new rigid image-based MRI-iUS co-registration. The navigation accuracy was evaluated based on the spatial overlap of the outlined lesions and the distance between the corresponding landmarks in both data sets, respectively. Image-based co-registration significantly improved (p < 0.001) the spatial overlap of the outlined lesion (0.42 ± 0.30 vs. 0.65 ± 0.23) and significantly reduced (p < 0.001) the distance between the corresponding landmarks (8.69 ± 6.23 mm vs. 3.19 ± 2.73 mm), allowing for the sufficient use of navigation and AR support. Navigated iUS can therefore serve as an easy-to-use tool to enable navigation support for posterior fossa surgery in the sitting position.
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Affiliation(s)
- Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Alexander Grote
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
| | - Marko Gjorgjevski
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
| | - Mirza Pojskic
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (A.G.); (M.G.); (M.P.); (B.S.); (C.N.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
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Wei R, Chen H, Cai Y, Chen J. Application of intraoperative ultrasound in the resection of high-grade gliomas. Front Neurol 2023; 14:1240150. [PMID: 37965171 PMCID: PMC10640994 DOI: 10.3389/fneur.2023.1240150] [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] [Received: 06/14/2023] [Accepted: 09/19/2023] [Indexed: 11/16/2023] Open
Abstract
The incidence of gliomas is approximately 3-5/100,000, with high-grade gliomas accounting for approximately 30-40% of these tumors. Surgery is a confirmed positive factor in prolonging the survival of these patients, and a larger resection range means a longer survival time. Therefore, surgery for high-grade glioma patients should aim to maximize the extent of resection while preserving neurological function to achieve a better quality of life. There is consensus regarding the need to lengthen progression-free survival (PFS) and overall survival (OS) times. In glioma surgery, methods such as intraoperative computed tomography (ICT), intraoperative magnetic resonance imaging (IMRI), navigation, 5-aminolevulinic acid (5-ALA), and intraoperative ultrasound (IOUS) are used to achieve an expanded resection during the surgical procedure. IOUS has been increasingly used in the surgery of high-grade gliomas and various tumors due to its convenient intraoperative use, its flexible repeatability, and the relatively low cost of operating room construction. With the continuous upgrading of ultrasound equipment, IOUS has been able to better assist surgeons in achieving an increased extent of resection. This review aims to summarize the application of ultrasound in the surgery of high-grade gliomas in the past decade, its improvement in patient prognosis, and its prospects.
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Affiliation(s)
- RenJie Wei
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - YuXiang Cai
- Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - JingCao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
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Haouchine N, Dorent R, Juvekar P, Torio E, Wells WM, Kapur T, Golby AJ, Frisken S. Learning Expected Appearances for Intraoperative Registration during Neurosurgery. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14228:227-237. [PMID: 38371724 PMCID: PMC10870253 DOI: 10.1007/978-3-031-43996-4_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.
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Affiliation(s)
- Nazim Haouchine
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Reuben Dorent
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Parikshit Juvekar
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Erickson Torio
- 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, Cambridge, MA, USA
| | - Tina Kapur
- Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra J 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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4
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Pirhadi A, Salari S, Ahmad MO, Rivaz H, Xiao Y. Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection. Int J Comput Assist Radiol Surg 2023; 18:501-508. [PMID: 36306056 DOI: 10.1007/s11548-022-02770-5] [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: 03/29/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety. METHODS We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages. An efficient 2.5D approach and an iterative re-weighted least squares algorithm are utilized to perform landmark-based registration for brain shift correction. The proposed method is validated and compared against the state-of-the-art methods using the public BITE and RESECT datasets. RESULTS Registration of pre-resection iUS scans to during- and post-resection iUS images were executed. The results with the proposed method shows a significant improvement from the initial misalignment ([Formula: see text]) and the method is comparable to the state-of-the-art methods validated on the same datasets. CONCLUSIONS We have proposed a robust technique to efficiently detect matching landmarks in iUS and perform brain shift correction with excellent performance. It has the potential to improve the accuracy and safety of neurosurgery.
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Affiliation(s)
- Amir Pirhadi
- Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada.
| | - Soorena Salari
- Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada
| | - M Omair Ahmad
- Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
| | - Hassan Rivaz
- Department of Electrical and Computer Engineering and PERFORM Centre, Concordia University, Montreal, Canada
| | - Yiming Xiao
- Department of Computer Science and Software Engineering and PERFORM Centre, Concordia University, Montreal, Canada
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Jeong H, Park T, Khang S, Koo K, Shin J, Kim KW, Lee J. Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images. Biomed Eng Lett 2022; 13:65-72. [PMID: 36711162 PMCID: PMC9873886 DOI: 10.1007/s13534-022-00254-8] [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: 07/12/2022] [Revised: 11/07/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987 mm, and the average execution time is 2.137 s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.
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Affiliation(s)
- Heeryeol Jeong
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Taeyong Park
- grid.411945.c0000 0000 9834 782XDepartment of Biomedical Informatics , Hallym University Medical Center , 22, Gwanpyeong-ro, 170 beon- gil, Dongan-gu, Anyang-si, Gyeonggi-do 14068 Korea
| | - Seungwoo Khang
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Kyoyeong Koo
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea
| | - Juneseuk Shin
- grid.264381.a0000 0001 2181 989XDepartment of Systems Management Engineering , Sungkyunkwan University , 2066, Seobu-ro, Jangan-gu, Suwon-si , Gyeong gi-do 16419 Korea
| | - Kyung Won Kim
- grid.267370.70000 0004 0533 4667Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine , 88 Olympic‑ro, 43‑gil, Songpa‑gu, Seoul, 05505 Korea
| | - Jeongjin Lee
- grid.263765.30000 0004 0533 3568School of Computer Science and Engineering , Soongsil University , 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978 Korea ,iAID Inc., 7,398, Sangdo-ro, Dongjak-gu, Seoul, 07040 Korea
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6
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The Current State of Visualization Techniques in Endoscopic Skull Base Surgery. Brain Sci 2022; 12:brainsci12101337. [PMID: 36291271 PMCID: PMC9599272 DOI: 10.3390/brainsci12101337] [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: 09/08/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022] Open
Abstract
Skull base surgery has undergone significant progress following key technological developments. From early candle-lit devices to the modern endoscope, refinements in visualization techniques have made endoscopic skull base surgery (ESBS) a standard practice for treating a variety of conditions. The endoscope has also been integrated with other technologies to enhance visualization, including fluorescence agents, intraoperative neuronavigation with augmented reality, and the exoscope. Endoscopic approaches have allowed neurosurgeons to reevaluate skull base neuroanatomy from new perspectives. These advances now serve as the foundation for future developments in ESBS. In this narrative review, we discuss the history and development of ESBS, current visualization techniques, and future innovations.
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7
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Mikaeili M, Bilge HŞ. Trajectory estimation of ultrasound images based on convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Bierbrier J, Gueziri HE, Collins DL. Estimating medical image registration error and confidence: A taxonomy and scoping review. Med Image Anal 2022; 81:102531. [PMID: 35858506 DOI: 10.1016/j.media.2022.102531] [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: 10/28/2021] [Revised: 06/16/2022] [Accepted: 07/01/2022] [Indexed: 11/18/2022]
Abstract
Given that image registration is a fundamental and ubiquitous task in both clinical and research domains of the medical field, errors in registration can have serious consequences. Since such errors can mislead clinicians during image-guided therapies or bias the results of a downstream analysis, methods to estimate registration error are becoming more popular. To give structure to this new heterogenous field we developed a taxonomy and performed a scoping review of methods that quantitatively and automatically provide a dense estimation of registration error. The taxonomy breaks down error estimation methods into Approach (Image- or Transformation-based), Framework (Machine Learning or Direct) and Measurement (error or confidence) components. Following the PRISMA guidelines for scoping reviews, the 570 records found were reduced to twenty studies that met inclusion criteria, which were then reviewed according to the proposed taxonomy. Trends in the field, advantages and disadvantages of the methods, and potential sources of bias are also discussed. We provide suggestions for best practices and identify areas of future research.
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Affiliation(s)
- Joshua Bierbrier
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada.
| | - Houssem-Eddine Gueziri
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - D Louis Collins
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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9
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Chauvin L, Kumar K, Desrosiers C, Wells W, Toews M. Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:836-845. [PMID: 34699353 PMCID: PMC9022638 DOI: 10.1109/tmi.2021.3123252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We propose a novel pairwise distance measure between image keypoint sets, for the purpose of large-scale medical image indexing. Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry. A new kernel is proposed to quantify the variability of keypoint geometry in location and scale. Our distance measure may be estimated between O (N 2) image pairs in [Formula: see text] operations via keypoint indexing. Experiments report the first results for the task of predicting family relationships from medical images, using 1010 T1-weighted MRI brain volumes of 434 families including monozygotic and dizygotic twins, siblings and half-siblings sharing 100%-25% of their polymorphic genes. Soft set equivalence and the keypoint geometry kernel improve upon standard hard set equivalence (HSE) and appearance kernels alone in predicting family relationships. Monozygotic twin identification is near 100%, and three subjects with uncertain genotyping are automatically paired with their self-reported families, the first reported practical application of image-based family identification. Our distance measure can also be used to predict group categories, sex is predicted with an AUC = 0.97. Software is provided for efficient fine-grained curation of large, generic image datasets.
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10
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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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11
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Shapey J, Dowrick T, Delaunay R, Mackle EC, Thompson S, Janatka M, Guichard R, Georgoulas A, Pérez-Suárez D, Bradford R, Saeed SR, Ourselin S, Clarkson MJ, Vercauteren T. Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware. Int J Comput Assist Radiol Surg 2021; 16:1347-1356. [PMID: 33937966 PMCID: PMC8295168 DOI: 10.1007/s11548-021-02374-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/08/2021] [Indexed: 01/19/2023]
Abstract
PURPOSE Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. METHODS We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system's ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. RESULTS Experimental validation of the system's navigated ultrasound component demonstrated accuracy of [Formula: see text] and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of [Formula: see text] and performed with a clinically acceptable level of accuracy. CONCLUSION We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.
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Affiliation(s)
- Jonathan Shapey
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. .,Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK. .,Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
| | - Thomas Dowrick
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK.,Centre for Medical Image Computing, UCL, London, UK.,Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Rémi Delaunay
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK
| | - Eleanor C Mackle
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK
| | - Stephen Thompson
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK.,Centre for Medical Image Computing, UCL, London, UK.,Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Mirek Janatka
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK.,Centre for Medical Image Computing, UCL, London, UK.,Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Roland Guichard
- Research Software Development Group, Research IT Services, UCL, London, UK
| | | | - David Pérez-Suárez
- Research Software Development Group, Research IT Services, UCL, London, UK
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.,The Ear Institute, UCL, London, UK.,The Royal National Throat, Nose and Ear Hospital, London, UK
| | - Sébastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Matthew J Clarkson
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK.,Centre for Medical Image Computing, UCL, London, UK.,Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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12
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Bastos DCDA, Juvekar P, Tie Y, Jowkar N, Pieper S, Wells WM, Bi WL, Golby A, Frisken S, Kapur T. Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI. Front Oncol 2021; 11:656519. [PMID: 34026631 PMCID: PMC8139191 DOI: 10.3389/fonc.2021.656519] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/09/2021] [Indexed: 11/15/2022] Open
Abstract
Introduction Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice. Methods We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor. Results Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface. Conclusion This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.
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Affiliation(s)
| | - Parikshit Juvekar
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Yanmei Tie
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Nick Jowkar
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Steve Pieper
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Willam M Wells
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Alexandra Golby
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Sarah Frisken
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
| | - Tina Kapur
- Department of Neurosurgery, Brigham and Womens Hospital, Harvard Medical School, Boston, MA, United States
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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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Gerard IJ, Kersten-Oertel M, Hall JA, Sirhan D, Collins DL. Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications. Front Oncol 2021; 10:618837. [PMID: 33628733 PMCID: PMC7897668 DOI: 10.3389/fonc.2020.618837] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/22/2020] [Indexed: 11/25/2022] Open
Abstract
Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.
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Affiliation(s)
- Ian J Gerard
- Department of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada
| | | | - Jeffery A Hall
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Denis Sirhan
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
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15
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Abstract
This article discusses intraoperative imaging techniques used during high-grade glioma surgery. Gliomas can be difficult to differentiate from surrounding tissue during surgery. Intraoperative imaging helps to alleviate problems encountered during glioma surgery, such as brain shift and residual tumor. There are a variety of modalities available all of which aim to give the surgeon more information, address brain shift, identify residual tumor, and increase the extent of surgical resection. The article starts with a brief introduction followed by a review of with the latest advances in intraoperative ultrasound, intraoperative MRI, and intraoperative computed tomography.
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Affiliation(s)
- Thomas Noh
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Hawaii Pacific Health, John A Burns School of Medicine, Honolulu, Hawaii, USA
| | - Martina Mustroph
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
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16
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Canalini L, Klein J, Miller D, Kikinis R. Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures. Int J Comput Assist Radiol Surg 2020; 15:1963-1974. [PMID: 33029677 PMCID: PMC7671994 DOI: 10.1007/s11548-020-02273-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 09/25/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. METHODS The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. RESULTS Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. CONCLUSIONS We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.
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Affiliation(s)
- Luca Canalini
- Fraunhofer MEVIS, Institute for Digital Medicine, Bremen, Germany.
- Medical Imaging Computing, University of Bremen, Bremen, Germany.
| | - Jan Klein
- Fraunhofer MEVIS, Institute for Digital Medicine, Bremen, Germany
| | - Dorothea Miller
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Bochum, Germany
| | - Ron Kikinis
- Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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17
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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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18
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Farnia P, Mohammadi M, Najafzadeh E, Alimohamadi M, Makkiabadi B, Ahmadian A. High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging. Biomed Phys Eng Express 2020; 6:045019. [PMID: 33444279 DOI: 10.1088/2057-1976/ab9a10] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.
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Affiliation(s)
- Parastoo Farnia
- Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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19
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Carton FX, Chabanas M, Le Lann F, Noble JH. Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net. J Med Imaging (Bellingham) 2020; 7:031503. [PMID: 32090137 PMCID: PMC7026519 DOI: 10.1117/1.jmi.7.3.031503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 01/17/2020] [Indexed: 11/14/2022] Open
Abstract
To compensate for the intraoperative brain tissue deformation, computer-assisted intervention methods have been used to register preoperative magnetic resonance images with intraoperative images. In order to model the deformation due to tissue resection, the resection cavity needs to be segmented in intraoperative images. We present an automatic method to segment the resection cavity in intraoperative ultrasound (iUS) images. We trained and evaluated two-dimensional (2-D) and three-dimensional (3-D) U-Net networks on two datasets of 37 and 13 cases that contain images acquired from different ultrasound systems. The best overall performing method was the 3-D network, which resulted in a 0.72 mean and 0.88 median Dice score over the whole dataset. The 2-D network also had good results with less computation time, with a median Dice score over 0.8. We also evaluated the sensitivity of network performance to training and testing with images from different ultrasound systems and image field of view. In this application, we found specialized networks to be more accurate for processing similar images than a general network trained with all the data. Overall, promising results were obtained for both datasets using specialized networks. This motivates further studies with additional clinical data, to enable training and validation of a clinically viable deep-learning model for automated delineation of the tumor resection cavity in iUS images.
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Affiliation(s)
- François-Xavier Carton
- University of Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble, France
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States
| | - Matthieu Chabanas
- University of Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble, France
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States
| | - Florian Le Lann
- Grenoble Alpes University Hospital, Department of Neurosurgery, Grenoble, France
| | - Jack H. Noble
- Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States
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20
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Ning G, Zhang X, Zhang Q, Wang Z, Liao H. Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid. Theranostics 2020; 10:4676-4693. [PMID: 32292522 PMCID: PMC7150484 DOI: 10.7150/thno.42830] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 01/26/2020] [Indexed: 12/02/2022] Open
Abstract
Rationale: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information. Methods: In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance. Results: In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases. Conclusion: We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.
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21
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Zhang F, Noh T, Juvekar P, Frisken SF, Rigolo L, Norton I, Kapur T, Pujol S, Wells W, Yarmarkovich A, Kindlmann G, Wassermann D, San Jose Estepar R, Rathi Y, Kikinis R, Johnson HJ, Westin CF, Pieper S, Golby AJ, O’Donnell LJ. SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization. JCO Clin Cancer Inform 2020; 4:299-309. [PMID: 32216636 PMCID: PMC7113081 DOI: 10.1200/cci.19.00141] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2020] [Indexed: 12/27/2022] Open
Abstract
PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
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Affiliation(s)
- Fan Zhang
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Thomas Noh
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | - Sarah F. Frisken
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Laura Rigolo
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Isaiah Norton
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Tina Kapur
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Sonia Pujol
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - William Wells
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Massachusetts Institute of Technology, Boston, MA
| | | | | | - Demian Wassermann
- Parietal, Inria Saclay-lle de France, Neurospin CEA, Université Paris-Saclay, Palaiseau, France
| | | | - Yogesh Rathi
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Ron Kikinis
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- University of Bremen and Fraunhofer MEVIS, Bremen, Germany
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22
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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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23
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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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24
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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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25
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Machado I, Toews M, George E, Unadkat P, Essayed W, Luo J, Teodoro P, Carvalho H, Martins J, Golland P, Pieper S, Frisken S, Golby A, Wells Iii W, Ou Y. Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data. Neuroimage 2019; 202:116094. [PMID: 31446127 DOI: 10.1016/j.neuroimage.2019.116094] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/18/2019] [Accepted: 08/09/2019] [Indexed: 11/16/2022] Open
Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.
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Affiliation(s)
- Inês Machado
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | - Matthew Toews
- Department of Systems Engineering, École de Technologie Supérieure, Montreal, Canada
| | - Elizabeth George
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Prashin Unadkat
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Walid Essayed
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jie Luo
- Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Pedro Teodoro
- Escola Superior Náutica Infante D. Henrique, Lisbon, Portugal
| | - Herculano Carvalho
- Department of Neurosurgery, Hospital de Santa Maria, CHLN, Lisbon, Portugal
| | - Jorge Martins
- Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Steve Pieper
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Isomics, Inc., Cambridge, MA, USA
| | - Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - William Wells Iii
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Yangming Ou
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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26
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Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery. Int J Comput Assist Radiol Surg 2019; 14:1697-1713. [PMID: 31392670 PMCID: PMC6797669 DOI: 10.1007/s11548-019-02045-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 07/29/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.
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Peoples JJ, Bisleri G, Ellis RE. Deformable multimodal registration for navigation in beating-heart cardiac surgery. Int J Comput Assist Radiol Surg 2019; 14:955-966. [PMID: 30888597 DOI: 10.1007/s11548-019-01932-2] [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: 01/27/2019] [Accepted: 03/01/2019] [Indexed: 11/29/2022]
Abstract
PURPOSE Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation. METHODS The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT. RESULTS Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT. CONCLUSION The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.
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28
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Frisken S, Luo M, Machado I, Unadkat P, Juvekar P, Bunevicius A, Toews M, Wells WM, Miga MI, Golby AJ. Preliminary Results Comparing Thin Plate Splines with Finite Element Methods for Modeling Brain Deformation during Neurosurgery using Intraoperative Ultrasound. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10951:1095120. [PMID: 31000909 PMCID: PMC6467062 DOI: 10.1117/12.2512799] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Brain shift compensation attempts to model the deformation of the brain which occurs during the surgical removal of brain tumors to enable mapping of presurgical image data into patient coordinates during surgery and thus improve the accuracy and utility of neuro-navigation. We present preliminary results from clinical tumor resections that compare two methods for modeling brain deformation, a simple thin plate spline method that interpolates displacements and a more complex finite element method (FEM) that models physical and geometric constraints of the brain and its material properties. Both methods are driven by the same set of displacements at locations surrounding the tumor. These displacements were derived from sets of corresponding matched features that were automatically detected using the SIFT-Rank algorithm. The deformation accuracy was tested using a set of manually identified landmarks. The FEM method requires significantly more preprocessing than the spline method but both methods can be used to model deformations in the operating room in reasonable time frames. Our preliminary results indicate that the FEM deformation model significantly out-performs the spline-based approach for predicting the deformation of manual landmarks. While both methods compensate for brain shift, this work suggests that models that incorporate biophysics and geometric constraints may be more accurate.
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Affiliation(s)
- S Frisken
- Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - M Luo
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - I Machado
- Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, PORTUGAL
| | - P Unadkat
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA
| | - P Juvekar
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA
| | - A Bunevicius
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA
| | - M Toews
- Département de Génie des Systems, Ecole de Technologie Superieure, Montreal, CANADA
| | - W M Wells
- Department of Radiology, Brigham and Women's Hospital, Boston, MA
- Comp. Sci. and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA
| | - M I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN
| | - A J Golby
- Department of Radiology, Brigham and Women's Hospital, Boston, MA
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA
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29
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Masoumi N, Xiao Y, Rivaz H. ARENA: Inter-modality affine registration using evolutionary strategy. Int J Comput Assist Radiol Surg 2018; 14:441-450. [DOI: 10.1007/s11548-018-1897-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/03/2018] [Indexed: 10/27/2022]
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