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Moiyadi A, Shetty P, Singh VK, Yeole U. Intraoperative Navigated Three-Dimensional Ultrasound Guidance Improves Resection in Gliomas Compared with Standard Two-Dimensional Ultrasound-Results from a Comparative Cohort Study. World Neurosurg 2023; 180:e233-e242. [PMID: 37739176 DOI: 10.1016/j.wneu.2023.09.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
OBJECTIVE Intraoperative ultrasound is a promising tool for intraoperative tumor resection control. Navigated three-dimensional US (n3DUS) has many benefits over standard two-dimensional US (2DUS). METHODS Two cohorts (2DUS and n3DUS) of patients with histologically confirmed adult diffuse gliomas undergoing US-guided resection control were compared. The primary outcomes assessed were extent of resection and morbidity. Multivariate analysis was performed to account for tumor characteristics (delineation and eloquence) and surgeon experience, which could confound the results. RESULTS n3DUS was used more often (n = 252) than 2DUS (n = 86). Tumor delineation was similar in 2DUS and n3DUS cohorts, although the n3DUS cohort included more nonenhancing, histologically lower grade (2-3) gliomas and had more gliomas located in eloquent regions; also, n3DUS was more often used by senior surgeons. Gross total resection (GTR) rates were 47%, and major morbidity was 9.5%. On multivariate analysis, after controlling for all other variables between the 2 groups, patients with well-delineated tumors, patients with prior treatment, and patients who underwent n3DUS were more likely to have GTR (adjusted odds ratios 3.0, 1.8, and 2.2, respectively), whereas patients with tumors in eloquent locations were half as likely (adjusted odds ratio 0.5) to have GTR. Eloquent located tumors were likely to be associated with higher neurological morbidity, although major morbidity was not significantly different. CONCLUSIONS Good delineation, noneloquent location, and use of n3DUS was associated with a higher probability of GTR in glioma surgery. Surgeons' experience did not influence the extent of resection. Morbidity was predominantly associated with eloquent location, independent of all other factors.
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Affiliation(s)
- Aliasgar Moiyadi
- Department of Surgical Oncology, Neurosurgical Services, Tata Memorial Centre, Mumbai, India; Department of Health Sciences, Homi Bhabha National Institute, Mumbai, India.
| | - Prakash Shetty
- Department of Surgical Oncology, Neurosurgical Services, Tata Memorial Centre, Mumbai, India; Department of Health Sciences, Homi Bhabha National Institute, Mumbai, India
| | - Vikas Kumar Singh
- Department of Surgical Oncology, Neurosurgical Services, Tata Memorial Centre, Mumbai, India; Department of Health Sciences, Homi Bhabha National Institute, Mumbai, India
| | - Ujwal Yeole
- Department of Surgical Oncology, Neurosurgical Services, Tata Memorial Centre, Mumbai, India; Department of Health Sciences, Homi Bhabha National Institute, Mumbai, India
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Jeung D, Jung K, Lee HJ, Hong J. Augmented reality-based surgical guidance for wrist arthroscopy with bone-shift compensation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 230:107323. [PMID: 36608430 DOI: 10.1016/j.cmpb.2022.107323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 08/17/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVES Intraoperative joint condition is different from preoperative CT/MR due to the motion applied during surgery, inducing an inaccurate approach to surgical targets. This study aims to provide real-time augmented reality (AR)-based surgical guidance for wrist arthroscopy based on a bone-shift model through an in vivo computed tomography (CT) study. METHODS To accurately visualize concealed wrist bones on the intra-articular arthroscopic image, we propose a surgical guidance system with a novel bone-shift compensation method using noninvasive fiducial markers. First, to measure the effect of traction during surgery, two noninvasive fiducial markers were attached before surgery. In addition, two virtual link models connecting the wrist bones were implemented. When wrist traction occurs during the operation, the movement of the fiducial marker is measured, and bone-shift compensation is applied to move the virtual links in the direction of the traction. The proposed bone-shift compensation method was verified with the in vivo CT data of 10 participants. Finally, to introduce AR, camera calibration for the arthroscope parameters was performed, and a patient-specific template was used for registration between the patient and the wrist bone model. As a result, a virtual bone model with three-dimensional information could be accurately projected on a two-dimensional arthroscopic image plane. RESULTS The proposed method was possible to estimate the position of wrist bone in the traction state with an accuracy of 1.4 mm margin. After bone-shift compensation was applied, the target point error was reduced by 33.6% in lunate, 63.3% in capitate, 55.0% in scaphoid, and 74.8% in trapezoid than those in preoperative wrist CT. In addition, a phantom experiment was introduced simulating the real surgical environment. AR display allowed to expand the field of view (FOV) of the arthroscope and helped in visualizing the anatomical structures around the bones. CONCLUSIONS This study demonstrated the successful handling of AR error caused by wrist traction using the proposed method. In addition, the method allowed accurate AR visualization of the concealed bones and expansion of the limited FOV of the arthroscope. The proposed bone-shift compensation can also be applied to other joints, such as the knees or shoulders, by representing their bone movements using corresponding virtual links. In addition, the movement of the joint skin during surgery can be measured using noninvasive fiducial markers in the same manner as that used for the wrist joint.
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Affiliation(s)
- Deokgi Jeung
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea
| | - Kyunghwa Jung
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea; Korea Research Institute of Standards and Science, Daejeon, South Korea
| | - Hyun-Joo Lee
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, South Korea.
| | - Jaesung Hong
- Department of Robotics and Mechatronics Engineering, DGIST, Daegu, South Korea.
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Review of Intraoperative Adjuncts for Maximal Safe Resection of Gliomas and Its Impact on Outcomes. Cancers (Basel) 2022; 14:cancers14225705. [PMID: 36428797 PMCID: PMC9688206 DOI: 10.3390/cancers14225705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/12/2022] [Accepted: 11/18/2022] [Indexed: 11/22/2022] Open
Abstract
Maximal safe resection is the mainstay of treatment in the neurosurgical management of gliomas, and preserving functional integrity is linked to favorable outcomes. How these modalities differ in their effectiveness on the extent of resection (EOR), survival, and complications remains unknown. A systematic literature search was performed with the following inclusion criteria: published between 2005 and 2022, involving brain glioma surgery, and including one or a combination of intraoperative modalities: intraoperative magnetic resonance imaging (iMRI), awake/general anesthesia craniotomy mapping (AC/GA), fluorescence-guided imaging, or combined modalities. Of 525 articles, 464 were excluded and 61 articles were included, involving 5221 glioma patients, 7(11.4%) articles used iMRI, 21(36.8%) used cortical mapping, 15(24.5%) used 5-aminolevulinic acid (5-ALA) or fluorescein sodium, and 18(29.5%) used combined modalities. The heterogeneity in reporting the amount of surgical resection prevented further analysis. Progression-free survival/overall survival (PFS/OS) were reported in 18/61(29.5%) articles, while complications and permanent disability were reported in 38/61(62.2%) articles. The reviewed studies demonstrate that intraoperative adjuncts such as iMRI, AC/GA mapping, fluorescence-guided imaging, and a combination of these modalities improve EOR. However, PFS/OS were underreported. Combining multiple intraoperative modalities seems to have the highest effect compared to each adjunct alone.
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Lv J, Wang Z, Shi H, Zhang H, Wang S, Wang Y, Li Q. Joint Progressive and Coarse-to-Fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2788-2802. [PMID: 35482699 DOI: 10.1109/tmi.2022.3170879] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated deformation sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated deformation field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and two public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.
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Weiss Lucas C, Faymonville AM, Loução R, Schroeter C, Nettekoven C, Oros-Peusquens AM, Langen KJ, Shah NJ, Stoffels G, Neuschmelting V, Blau T, Neuschmelting H, Hellmich M, Kocher M, Grefkes C, Goldbrunner R. Surgery of Motor Eloquent Glioblastoma Guided by TMS-Informed Tractography: Driving Resection Completeness Towards Prolonged Survival. Front Oncol 2022; 12:874631. [PMID: 35692752 PMCID: PMC9186060 DOI: 10.3389/fonc.2022.874631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/21/2022] [Indexed: 12/13/2022] Open
Abstract
Background Surgical treatment of patients with glioblastoma affecting motor eloquent brain regions remains critically discussed given the risk–benefit dilemma of prolonging survival at the cost of motor-functional damage. Tractography informed by navigated transcranial magnetic stimulation (nTMS-informed tractography, TIT) provides a rather robust estimate of the individual location of the corticospinal tract (CST), a highly vulnerable structure with poor functional reorganisation potential. We hypothesised that by a more comprehensive, individualised surgical decision-making using TIT, tumours in close relationship to the CST can be resected with at least equal probability of gross total resection (GTR) than less eloquently located tumours without causing significantly more gross motor function harm. Moreover, we explored whether the completeness of TIT-aided resection translates to longer survival. Methods A total of 61 patients (median age 63 years, m = 34) with primary glioblastoma neighbouring or involving the CST were operated on between 2010 and 2015. TIT was performed to inform surgical planning in 35 of the patients (group T; vs. 26 control patients). To achieve largely unconfounded group comparisons for each co-primary outcome (i.e., gross-motor functional worsening, GTR, survival), (i) uni- and multivariate regression analyses were performed to identify features of optimal outcome prediction; (ii), optimal propensity score matching (PSM) was applied to balance those features pairwise across groups, followed by (iii) pairwise group comparison. Results Patients in group T featured a significantly higher lesion-CST overlap compared to controls (8.7 ± 10.7% vs. 3.8 ± 5.7%; p = 0.022). The frequency of gross motor worsening was higher in group T, albeit non-significant (n = 5/35 vs. n = 0/26; p = 0.108). PSM-based paired-sample comparison, controlling for the confounders of preoperative tumour volume and vicinity to the delicate vasculature of the insula, showed higher GTR rates in group T (77% vs. 69%; p = 0.025), particularly in patients with a priori intended GTR (87% vs. 78%; p = 0.003). This translates into a prolonged PFS in the same PSM subgroup (8.9 vs. 5.8 months; p = 0.03), with GTR representing the strongest predictor of PFS (p = 0.001) and OS (p = 0.0003) overall. Conclusion The benefit of TIT-aided GTR appears to overcome the drawbacks of potentially elevated motor functional risk in motor eloquent tumour localisation, leading to prolonged survival of patients with primary glioblastoma close to the CST.
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Affiliation(s)
- Carolin Weiss Lucas
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andrea Maria Faymonville
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Neurosurgery, University Hospital Mannheim, Mannheim, Germany
| | - Ricardo Loução
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Catharina Schroeter
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Charlotte Nettekoven
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | | | - Karl Josef Langen
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany.,JARA - BRAIN - Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Volker Neuschmelting
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Tobias Blau
- Department of Neurology, RWTH Aachen University, Aachen, Germany.,Institute of Neuropathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Hannah Neuschmelting
- Institute of Pathology and Neuropathology, University Hospital Essen, Essen, Germany
| | - Martin Hellmich
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martin Kocher
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany
| | - Christian Grefkes
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Julich, Juelich, Germany.,Institute for Medical Statistics and Computational Biology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roland Goldbrunner
- Department of General Neurosurgery, Center of Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Liu YR, He HH, Wu J. Differentiation of Human GBM From Non-GBM Brain Tissue With Polarization Imaging Technique. Front Oncol 2022; 12:863682. [PMID: 35574382 PMCID: PMC9095988 DOI: 10.3389/fonc.2022.863682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/22/2022] [Indexed: 12/01/2022] Open
Abstract
As for optical techniques, it is difficult for the 5-aminolevulinic (5-ALA) fluorescence guidance technique to completely detect glioma due to residual cells in the blind area and the dead angle of vision under microscopy. The purpose of this research is to characterize different microstructural information and optical properties of formalin-soaked unstained glioblastoma (GBM) and non-GBM tissue with the polarization imaging technique (PIT), and provide a novel method to detect GBM during surgery. In this paper, a 3×3 Mueller matrix polarization experimental system in backscattering mode was built to detect the GBM and non-GBM tissue bulk. The Mueller matrix decomposition and transformation parameters of GBM and non-GBM tissue were calculated and analyzed, and showed that parameters (1−Δ) and t are good indicators for distinguishing GBM from non-GBM tissues. Furthermore, the central moment coefficients (CMCs) of the frequency distribution histogram (FDH) were also calculated and used to distinguish the cancerous tissues. The results of the experiments confirmed the feasibility of PIT applied in the clinic to detect glioma, laying the foundation for the subsequent non-invasive, non-staining glioma detection.
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Affiliation(s)
- Yi-Rong Liu
- School of Medicine, Tsinghua University, Beijing, China.,Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Hong-Hui He
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Jian Wu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
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Riva M, Arcidiacono UA, Gambaretti M, Gay LG, Sciortino T, Rossi M, Conti Nibali M, Bello L. Intraoperative AIRO mobile computer tomography in frameless stereotactic procedures. Br J Neurosurg 2022; 36:527-531. [PMID: 35379051 DOI: 10.1080/02688697.2022.2057430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
BACKGROUND Multiple factors can affect the accuracy of neuronavigation, that is a relevant issue, particularly for frameless stereotactic procedures, where precision and optimal image-guidance is crucial for the surgical performance, workflow, and outcome. OBJECTIVE To investigate the impact of AIRO Mobile Computer Tomography in frameless stereotactic approaches. METHODS A retrospective study on 12 patients was performed. All the procedures were deployed using a frameless stereotactic technique, both for the collection of biopsy pathological specimens for diagnosis and insertion of drainage in the treatment of intracranial cystic lesions. RESULTS Twelve patients (eight males, four females) underwent the frameless stereotactic procedure. Mean age at surgery was 55 (±5 SE). The mean volume of the lesion was 23.85 cm3 (±3.13). Six diagnostic biopsies and six cyst drainages were performed. The mean trajectory length was 75.9 ± 11.8 mm. Three posterior fossa lesions (27%) were approached through a retro-sigmoidal burr-hole. A craniotomy for draining a haematoma was performed after detection with AIRO-CT. No permanent neurological dysfunction, in-hospital or 30-day mortality were recorded. CONCLUSION The AIRO-CT resulted feasible with a potential utility for stereotactic procedures. We showed how it could grant the efficacy of the stereotactic procedures reducing some technical and physical sources of inaccuracy, also enhancing safety and allowing prompt detection and management of intraoperative complications.
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Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.,IRCCS Humanitas Research Hospital, Milan, Italy
| | - Umberto A Arcidiacono
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Matteo Gambaretti
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Lorenzo G Gay
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Tommaso Sciortino
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Rossi
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Marco Conti Nibali
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
| | - Lorenzo Bello
- IRCCS Humanitas Research Hospital, Milan, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
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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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Akakuru OU, Zhang Z, Iqbal MZ, Zhu C, Zhang Y, Wu A. Chemotherapeutic nanomaterials in tumor boundary delineation: Prospects for effective tumor treatment. Acta Pharm Sin B 2022; 12:2640-2657. [PMID: 35755279 PMCID: PMC9214073 DOI: 10.1016/j.apsb.2022.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 12/14/2022] Open
Abstract
Accurately delineating tumor boundaries is key to predicting survival rates of cancer patients and assessing response of tumor microenvironment to various therapeutic techniques such as chemotherapy and radiotherapy. This review discusses various strategies that have been deployed to accurately delineate tumor boundaries with particular emphasis on the potential of chemotherapeutic nanomaterials in tumor boundary delineation. It also compiles the types of tumors that have been successfully delineated by currently available strategies. Finally, the challenges that still abound in accurate tumor boundary delineation are presented alongside possible perspective strategies to either ameliorate or solve the problems. It is expected that the information communicated herein will form the first compendious baseline information on tumor boundary delineation with chemotherapeutic nanomaterials and provide useful insights into future possible paths to advancing current available tumor boundary delineation approaches to achieve efficacious tumor therapy.
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Affiliation(s)
- Ozioma Udochukwu Akakuru
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
| | - Zhoujing Zhang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - M. Zubair Iqbal
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Chengjie Zhu
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
| | - Yewei Zhang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Aiguo Wu
- Cixi Institute of Biomedical Engineering, International Cooperation Base of Biomedical Materials Technology and Application, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, CAS, Ningbo 315201, China
- Corresponding author.
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10
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Lesage AC, Simmons A, Sen A, Singh S, Chen M, Cazoulat G, Weinberg JS, Brock KK. Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data. Phys Med Biol 2021; 66. [PMID: 34469879 DOI: 10.1088/1361-6560/ac22dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/01/2021] [Indexed: 11/11/2022]
Abstract
Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation.
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Affiliation(s)
- Anne-Cecile Lesage
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Alexis Simmons
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Anando Sen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Simran Singh
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Melissa Chen
- Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Jeffrey S Weinberg
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
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11
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Cazoulat G, Anderson BM, McCulloch MM, Rigaud B, Koay EJ, Brock KK. Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy. Med Phys 2021; 48:5935-5946. [PMID: 34390007 PMCID: PMC9132059 DOI: 10.1002/mp.15163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs). MATERIALS AND METHODS Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5 mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks. RESULTS The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r = 0.81 and r = 0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r = 0.99 and r = 0.64 for the lung and liver cases, respectively. CONCLUSION For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.
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Affiliation(s)
- Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Brian M Anderson
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Molly M McCulloch
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bastien Rigaud
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Eugene J Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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12
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Chekhonin IV, Batalov AI, Zakharova NE, Pogosbekyan EL, Nikitin PV, Bykanov AE, Pitskhelauri DI, Pronin IN. [Magnetic resonance relaxometry in high-grade glioma subregion assessment - neuroimaging and morphological correlates]. ZHURNAL VOPROSY NEĬROKHIRURGII IMENI N. N. BURDENKO 2021; 85:41-48. [PMID: 34463449 DOI: 10.17116/neiro20218504141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To analyze the differences of high-grade glioma subregions using magnetic resonance relaxometry with compilation of images (MAGiC) and arterial spin labeling (ASL), as well as to compare quantitative measurements of these techniques with morphological data. MATERIAL AND METHODS The study enrolled 35 patients with newly diagnosed supratentorial gliomas (23 - grade IV, 12 - grade III). We measured relaxometric values (T1, T2, proton density), tumor blood flow (TBF) in glioma subregions and normal-appearing brain matter. Neuronavigation was intraoperatively used to obtain tissue samples from active tumor growth zone, perifocal infiltrative edema zone and adjacent brain matter along surgical approach. RESULTS ASL perfusion revealed higher tumor blood flow (TBF) in active tumor growth region compared to perifocal infiltrative edema zone (p<0.01). Relaxometric values (T1, T2, proton density) in perifocal zone were higher (p<0.01) compared to adjacent intact white matter along surgical approach. However, there were no differences in TBF between these zones. Proton density in tumor-adjacent intact white matter was higher (p<0.01) compared to normal-appearing white matter in ipsilateral hemisphere. There was inverse correlation between T2 and TBF in active tumor growth zone (Spearman rank R= -0.58; p=0.0016). We found inverse correlation between T2 and Ki67 proliferative index and direct correlation between TBF and Ki67 in this zone. Nevertheless, these relationships were insignificant after multiple test adjustment. CONCLUSION Our study advocates for complementary power of ASL perfusion and MR relaxometry in assessment of high-grade brain glioma subregions. More malignant tumor zones tend to have higher TBF and shorter T2. Further investigation is needed to prove the capability of MAGiC to reveal foci of increased relaxometric values in tumor-adjacent normal-appearing white matter.
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Affiliation(s)
| | - A I Batalov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | | | - P V Nikitin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A E Bykanov
- Burdenko Neurosurgical Center, Moscow, Russia
| | | | - I N Pronin
- Burdenko Neurosurgical Center, Moscow, Russia
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13
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Riva M, Hiepe P, Frommert M, Divenuto I, Gay LG, Sciortino T, Nibali MC, Rossi M, Pessina F, Bello L. Intraoperative Computed Tomography and Finite Element Modelling for Multimodal Image Fusion in Brain Surgery. Oper Neurosurg (Hagerstown) 2021; 18:531-541. [PMID: 31342073 DOI: 10.1093/ons/opz196] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 04/16/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy. OBJECTIVE To evaluate the performance of an iCT-based fusion algorithm using clinical data. METHODS Ten patients with brain tumors were enrolled; preoperative MRI was acquired. The iCT was applied at the end of microsurgical resection. Elastic image fusion of the preoperative MRI to iCT data was performed by deformable fusion employing a biomechanical simulation based on a finite element model. Fusion accuracy was evaluated: the target registration error (TRE, mm) was measured for rigid and elastic fusion (Rf and Ef) and anatomical landmark pairs were divided into test and control structures according to distinct involvement by the brainshift. Intraoperative points describing the stereotactic position of the brain were also acquired and a qualitative evaluation of the adaptive morphing of the preoperative MRI was performed by 5 observers. RESULTS The mean TRE for control and test structures with Rf was 1.81 ± 1.52 and 5.53 ± 2.46 mm, respectively. No significant change was observed applying Ef to control structures; the test structures showed reduced TRE values of 3.34 ± 2.10 mm after Ef (P < .001). A 32% average gain (range 9%-54%) in accuracy of image registration was recorded. The morphed MRI showed robust matching with iCT scans and intraoperative stereotactic points. CONCLUSIONS The evaluated method increased the registration accuracy of preoperative MRI and iCT data. The iCT-based non-linear morphing of the preoperative MRI can potentially enhance the consistency of neuronavigation intraoperatively.
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Affiliation(s)
- Marco Riva
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy.,Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | | | | | - Ignazio Divenuto
- Unit of Neuroradiology, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Lorenzo G Gay
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Tommaso Sciortino
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Marco Conti Nibali
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Marco Rossi
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy
| | - Federico Pessina
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Department of Biomedical Sciences, Humanitas University, Rozzano, Italy
| | - Lorenzo Bello
- Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy.,Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy
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14
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Senova S, Lefaucheur JP, Brugières P, Ayache SS, Tazi S, Bapst B, Abhay K, Langeron O, Edakawa K, Palfi S, Bardel B. Case Report: Multimodal Functional and Structural Evaluation Combining Pre-operative nTMS Mapping and Neuroimaging With Intraoperative CT-Scan and Brain Shift Correction for Brain Tumor Surgical Resection. Front Hum Neurosci 2021; 15:646268. [PMID: 33716700 PMCID: PMC7947337 DOI: 10.3389/fnhum.2021.646268] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 02/08/2021] [Indexed: 01/11/2023] Open
Abstract
Background: Maximum safe resection of infiltrative brain tumors in eloquent area is the primary objective in surgical neuro-oncology. This goal can be achieved with direct electrical stimulation (DES) to perform a functional mapping of the brain in patients awake intraoperatively. When awake surgery is not possible, we propose a pipeline procedure that combines advanced techniques aiming at performing a dissection that respects the anatomo-functional connectivity of the peritumoral region. This procedure can benefit from intraoperative monitoring with computerized tomography scan (iCT-scan) and brain shift correction. Associated with this intraoperative monitoring, the additional value of preoperative investigation combining brain mapping by navigated transcranial magnetic stimulation (nTMS) with various neuroimaging modalities (tractography and resting state functional MRI) has not yet been reported. Case Report: A 42-year-old left-handed man had increased intracranial pressure (IICP), left hand muscle deficit, and dysarthria, related to an infiltrative tumor of the right frontal lobe with large mass effect and circumscribed contrast enhancement in motor and premotor cortical areas. Spectroscopy profile and intratumoral calcifications on CT-scan suggested an WHO grade III glioma, later confirmed by histology. The aforementioned surgical procedure was considered, since standard awake surgery was not appropriate for this patient. In preoperative time, nTMS mapping of motor function (deltoid, first interosseous, and tibialis anterior muscles) was performed, combined with magnetic resonance imaging (MRI)-based tractography reconstruction of 6 neural tracts (arcuate, corticospinal, inferior fronto-occipital, uncinate and superior and inferior longitudinal fasciculi) and resting-state functional MRI connectivity (rs-fMRI) of sensorimotor and language networks. In intraoperative time, DES mapping was performed with motor evoked response recording and tumor resection was optimized using non-rigid image transformation of the preoperative data (nTMS, tractography, and rs-fMRI) to iCT data. Image guidance was updated with correction for brain shift and tissue deformation using biomechanical modeling taking into account brain elastic properties. This correction was done at crucial surgical steps, i.e., when tumor bulged through the craniotomy after dura mater opening and when approaching the presumed eloquent brain regions. This procedure allowed a total resection of the tumor region with contrast enhancement as well as a complete regression of IICP and dysarthria. Hand paresis remained stable with no additional deficit. Postoperative nTMS mapping confirmed the good functional outcome. Conclusion: This case report and technical note highlights the value of preoperative functional evaluation by nTMS updated intraoperatively with correction of brain deformation by iCT. This multimodal approach may become the optimized technique of reference for patients with brain tumors in eloquent areas that are unsuitable for awake brain surgery.
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Affiliation(s)
- Suhan Senova
- Department of Neurosurgery, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Translational Psychiatry (Equipe 15), IMRB - INSERM U955, Univ Paris-Est Creteil, Creteil, France
| | - Jean-Pascal Lefaucheur
- Department of Clinical Neurophysiology, DMU FIxIT, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Excitabilite Nerveuse et Therapeutique, EA 4391, Univ Paris-Est Creteil, Creteil, France
| | - Pierre Brugières
- Department of Neuroradiology, DMU FIxIT, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France
| | - Samar S Ayache
- Department of Clinical Neurophysiology, DMU FIxIT, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Excitabilite Nerveuse et Therapeutique, EA 4391, Univ Paris-Est Creteil, Creteil, France
| | - Sanaa Tazi
- Department of Neurosurgery, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Translational Psychiatry (Equipe 15), IMRB - INSERM U955, Univ Paris-Est Creteil, Creteil, France
| | - Blanche Bapst
- Department of Neuroradiology, DMU FIxIT, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France
| | - Kou Abhay
- Department of Anesthesiology and Critical Care, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France
| | - Olivier Langeron
- Department of Anesthesiology and Critical Care, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Experimental Neuropathology Unit, Institut Pasteur, Paris, France
| | - Kohtaroh Edakawa
- Department of Neurosurgery, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Translational Psychiatry (Equipe 15), IMRB - INSERM U955, Univ Paris-Est Creteil, Creteil, France.,Department of Neurosurgery, Graduate School of Medicine Osaka University, Suita, Japan
| | - Stéphane Palfi
- Department of Neurosurgery, DMU CARe, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Translational Psychiatry (Equipe 15), IMRB - INSERM U955, Univ Paris-Est Creteil, Creteil, France
| | - Benjamin Bardel
- Department of Clinical Neurophysiology, DMU FIxIT, Henri Mondor University Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Creteil, France.,Excitabilite Nerveuse et Therapeutique, EA 4391, Univ Paris-Est Creteil, Creteil, France
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15
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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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16
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Alvarez P, Rouzé S, Miga MI, Payan Y, Dillenseger JL, Chabanas M. A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery. Med Image Anal 2021; 69:101983. [PMID: 33588119 DOI: 10.1016/j.media.2021.101983] [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: 04/28/2020] [Revised: 01/16/2021] [Accepted: 01/26/2021] [Indexed: 12/09/2022]
Abstract
The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.
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Affiliation(s)
- Pablo Alvarez
- Univ. Rennes 1, Inserm, LTSI - UMR 1099, Rennes F-35000, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France.
| | - Simon Rouzé
- Univ. Rennes 1, Inserm, LTSI - UMR 1099, Rennes F-35000, France; CHU Rennes, Department of Cardio-Thoracic and Vascular Surgery, Rennes F-35000, France.
| | - Michael I Miga
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yohan Payan
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France.
| | | | - Matthieu Chabanas
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France; Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, USA.
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17
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Schupper AJ, Yong RL, Hadjipanayis CG. The Neurosurgeon's Armamentarium for Gliomas: An Update on Intraoperative Technologies to Improve Extent of Resection. J Clin Med 2021; 10:jcm10020236. [PMID: 33440712 PMCID: PMC7826675 DOI: 10.3390/jcm10020236] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 12/18/2022] Open
Abstract
Maximal safe resection is the standard of care in the neurosurgical treatment of high-grade gliomas. To aid surgeons in the operating room, adjuvant techniques and technologies centered around improving intraoperative visualization of tumor tissue have been developed. In this review, we will discuss the most advanced technologies, specifically fluorescence-guided surgery, intraoperative imaging, neuromonitoring modalities, and microscopic imaging techniques. The goal of these technologies is to improve detection of tumor tissue beyond what conventional microsurgery has permitted. We describe the various advances, the current state of the literature that have tested the utility of the different adjuvants in clinical practice, and future directions for improving intraoperative technologies.
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18
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Granados A, Perez-Garcia F, Schweiger M, Vakharia V, Vos SB, Miserocchi A, McEvoy AW, Duncan JS, Sparks R, Ourselin S. A generative model of hyperelastic strain energy density functions for multiple tissue brain deformation. Int J Comput Assist Radiol Surg 2021; 16:141-150. [PMID: 33165705 PMCID: PMC7822772 DOI: 10.1007/s11548-020-02284-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 10/23/2020] [Indexed: 11/06/2022]
Abstract
PURPOSE Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation. METHODS For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images. RESULTS Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface. CONCLUSION Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model.
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Affiliation(s)
- Alejandro Granados
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | | | - Martin Schweiger
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Vejay Vakharia
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Sjoerd B Vos
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Anna Miserocchi
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Andrew W McEvoy
- National Hospital for Neurology and Neurosurgery, London, UK
| | - John S Duncan
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sébastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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19
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Orillac C, Stummer W, Orringer DA. Fluorescence Guidance and Intraoperative Adjuvants to Maximize Extent of Resection. Neurosurgery 2020; 89:727-736. [PMID: 33289518 DOI: 10.1093/neuros/nyaa475] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 08/23/2020] [Indexed: 12/27/2022] Open
Abstract
Safely maximizing extent of resection has become the central goal in glioma surgery. Especially in eloquent cortex, the goal of maximal resection is balanced with neurological risk. As new technologies emerge in the field of neurosurgery, the standards for maximal safe resection have been elevated. Fluorescence-guided surgery, intraoperative magnetic resonance imaging, and microscopic imaging methods are among the most well-validated tools available to enhance the level of accuracy and safety in glioma surgery. Each technology uses a different characteristic of glioma tissue to identify and differentiate tumor tissue from normal brain and is most effective in the context of anatomic, connectomic, and neurophysiologic context. While each tool is able to enhance resection, multiple modalities are often used in conjunction to achieve maximal safe resection. This paper reviews the mechanism and utility of the major adjuncts available for use in glioma surgery, especially in tumors within eloquent areas, and puts forth the foundation for a unified approach to how leverage currently available technology to ensure maximal safe resection.
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Affiliation(s)
- Cordelia Orillac
- Department of Neurosurgery, NYU Langone Health, New York, New York
| | - Walter Stummer
- Department of Neurosurgery, University Hospital Münster, Münster, Germany
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20
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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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21
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Li C, Fan X, Hong J, Roberts DW, Aronson JP, Paulsen KD. Model-Based Image Updating for Brain Shift in Deep Brain Stimulation Electrode Placement Surgery. IEEE Trans Biomed Eng 2020; 67:3542-3552. [PMID: 32340934 DOI: 10.1109/tbme.2020.2990669] [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: 11/07/2022]
Abstract
OBJECTIVE The efficacy of deep brain stimulation (DBS) depends on accurate placement of electrodes. Although stereotactic frames enable co-registration of image-based surgical planning and the operative field, the accuracy of electrode placement can be degraded by intra-operative brain shift. In this study, we adapted a biomechanical model to estimate whole brain displacements from which we deformed preoperative CT (preCT) to generate an updated CT (uCT) that compensates for brain shift. METHODS We drove the deformation model using displacement data derived from deformation in the frontal cortical surface that occurred during the DBS intervention. We evaluated 15 patients, retrospectively, who underwent bilateral DBS surgery, and assessed the accuracy of uCT in terms of target registration error (TRE) relative to a CT acquired post-placement (postCT). We further divided subjects into large (Group L) and small (Group S) deformation groups based on a TRE threshold of 1.6mm. Anterior commissure (AC), posterior commissure (PC) and pineal gland (PG) were identified on preCT and postCT and used to quantify TREs in preCT and uCT. RESULTS In the group of large brain deformation, average TREs for uCT were 1.11 ± 0.13 and 1.07 ± 0.38 mm at AC and PC, respectively, compared to 1.85 ± 0.17 and 0.92 ± 0.52 mm for preCT. The model updating approach improved AC localization but did not alter TREs at PC. CONCLUSION This preliminary study suggests that our image updating method may compensate for brain shift around surgical targets of importance during DBS surgery, although further investigation is warranted before conclusive evidence will be available. SIGNIFICANCE With further development and evaluation, our model-based image updating method using intraoperative sparse data may compensate for brain shift in DBS surgery efficiently, and have utility in updating targeting coordinates.
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22
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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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23
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Yang X, Lin Y. Surgical resection of glioma involving eloquent brain areas: Tumor boundary, functional boundary, and plasticity consideration. GLIOMA 2020. [DOI: 10.4103/glioma.glioma_16_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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24
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Haouchine N, Juvekar P, Golby A, Frisken S. Predicted Microscopic Cortical Brain Images for Optimal Craniotomy Positioning and Visualization. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2020; 9:407-413. [PMID: 34676151 DOI: 10.1080/21681163.2020.1834874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
During a craniotomy, the skull is opened to allow surgeons to have access to the brain and perform the procedure. The position and size of this opening are chosen in a way to avoid critical structures, such as vessels, and facilitate the access to tumors. Planning the operation is done based on pre-operative images and does not account for intra-operative surgical events. We present a novel image-guided neurosurgical system to optimize the craniotomy opening. Using physics-based modeling we define a cortical deformation map that estimates the displacement field at candidate craniotomy locations. This deformation map is coupled with an image analogy algorithm that produces realistic synthetic images that can be used to predict both the geometry and the appearance of the brain surface before opening the skull. These images account for cortical vessel deformations that may occur after opening the skull and is rendered in a way that increases the surgeon's understanding and assimilation. Our method was tested retrospectively on patients data showing good results and demonstrating the feasibility of practical use of our system.
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Affiliation(s)
- Nazim Haouchine
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Pariskhit Juvekar
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Alexandra Golby
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah Frisken
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
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25
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Miller K, Joldes GR, Bourantas G, Warfield S, Hyde DE, Kikinis R, Wittek A. Biomechanical modeling and computer simulation of the brain during neurosurgery. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3250. [PMID: 31400252 PMCID: PMC6785376 DOI: 10.1002/cnm.3250] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/28/2019] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
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Affiliation(s)
- K. Miller
- Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - G. R. Joldes
- Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - G. Bourantas
- Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - S.K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital and Harvard Medical School, 300 Longwood Avenue, Boston MA 02115
| | - D. E. Hyde
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital and Harvard Medical School, 300 Longwood Avenue, Boston MA 02115
| | - R. Kikinis
- Surgical Planning Laboratory, Brigham and Women’s Hospital and Harvard Medical School, 45 Francis St, Boston, MA 02115
- Medical Image Computing, University of Bremen, Germany
- Fraunhofer MEVIS, Bremen, Germany
| | - A. Wittek
- Intelligent Systems for Medicine Laboratory, Department of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
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26
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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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27
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Reliability of intraoperative ultrasound in detecting tumor residual after brain diffuse glioma surgery: a systematic review and meta-analysis. Neurosurg Rev 2019; 43:1221-1233. [PMID: 31410683 DOI: 10.1007/s10143-019-01160-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/28/2019] [Accepted: 08/05/2019] [Indexed: 12/11/2022]
Abstract
Intraoperative ultrasonography (iUS) is considered an accurate, safe, and cost-effective tool to estimate the extent of resection of both high-grade (HGG) and low-grade (DLGG) diffuse gliomas (DGs). However, it is currently missing an evidence-based assessment of iUS diagnostic accuracy in DGs surgery. The objective of review is to perform a systematic review and meta-analysis of the diagnostic performance of iUS in detecting tumor residue after DGs resection. A comprehensive literature search for studies published through October 2018 was performed according to PRISMA-DTA and STARD 2015 guidelines, using the following algorithm: ("ultrasound" OR "ultrasonography" OR "ultra-so*" OR "echo*" OR "eco*") AND ("brain" OR "nervous") AND ("tumor" OR "tumour" OR "lesion" OR "mass" OR "glio*" OR "GBM") AND ("surgery" OR "surgical" OR "microsurg*" OR "neurosurg*"). Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), and diagnostic odds ratio (DOR) of iUS in DGs were calculated. A subgroup analysis for HGGs and DLGGs was also conducted. Thirteen studies were included in the systematic review (665 DGs). Ten articles (409 DGs) were selected for the meta-analysis with the following results: sensitivity 72.2%, specificity 93.5%, LR- 0.29, LR+ 3, and DOR 9.67. Heterogeneity among studies was non-significant. Subgroup analysis demonstrates a better diagnostic performance of iUS for DLGGs compared with HGGs. iUS is an effective technique in assessing DGs resection. No significant differences are seen regarding iUS modality and transducer characteristics. Its diagnostic performance is higher in DLGGs than HGGs and could be worsened by previous treatments, surgical artifacts, and small tumor residue volumes.
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28
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Adagolodjo Y, Goffin L, De Mathelin M, Courtecuisse H. Robotic Insertion of Flexible Needle in Deformable Structures Using Inverse Finite-Element Simulation. IEEE T ROBOT 2019. [DOI: 10.1109/tro.2019.2897858] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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29
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Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR. Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection. Front Oncol 2019; 9:426. [PMID: 31192130 PMCID: PMC6549594 DOI: 10.3389/fonc.2019.00426] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
In the treatment of brain tumors, surgical intervention remains a common and effective therapeutic option. Recent advances in neuroimaging have provided neurosurgeons with new tools to overcome the challenge of differentiating healthy tissue from tumor-infiltrated tissue, with the aim of increasing the likelihood of maximizing the extent of resection volume while minimizing injury to functionally important regions. Novel applications of diffusion tensor imaging (DTI), and DTI-derived tractography (DDT) have demonstrated that preoperative, non-invasive mapping of eloquent cortical regions and functionally relevant white matter tracts (WMT) is critical during surgical planning to reduce postoperative deficits, which can decrease quality of life and overall survival. In this review, we summarize the latest developments of applying DTI and tractography in the context of resective surgery and highlight its utility within each stage of the neurosurgical workflow: preoperative planning and intraoperative management to improve postoperative outcomes.
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Affiliation(s)
- Jamie D Costabile
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Elsa Alaswad
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Shawn D'Souza
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - John A Thompson
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
| | - D Ryan Ormond
- Department of Neurosurgery, School of Medicine, University of Colorado, Aurora, CO, United States
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30
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Ganau M, Ligarotti GK, Apostolopoulos V. Real-time intraoperative ultrasound in brain surgery: neuronavigation and use of contrast-enhanced image fusion. Quant Imaging Med Surg 2019; 9:350-358. [PMID: 31032183 DOI: 10.21037/qims.2019.03.06] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Mario Ganau
- Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Gianfranco K Ligarotti
- Department of Neurosurgery, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
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31
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Iversen DH, Wein W, Lindseth F, Unsgård G, Reinertsen I. Automatic Intraoperative Correction of Brain Shift for Accurate Neuronavigation. World Neurosurg 2018; 120:e1071-e1078. [DOI: 10.1016/j.wneu.2018.09.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/30/2018] [Accepted: 09/02/2018] [Indexed: 11/29/2022]
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32
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Conti Nibali M, Rossi M, Sciortino T, Riva M, Gay LG, Pessina F, Bello L. Preoperative surgical planning of glioma: limitations and reliability of fMRI and DTI tractography. J Neurosurg Sci 2018; 63:127-134. [PMID: 30290696 DOI: 10.23736/s0390-5616.18.04597-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Brain mapping techniques (intraoperative neurophysiology and neuropsychology) represent the gold standard in glioma surgery, and particularly in glioma resection. Since the introduction of MRI in the clinical practice, several advanced applications have been developed, like functional MRI (fMRI) and diffusion imaging-based tractography (DTI), which both have an application in glioma surgery. fMRI allows to identify cortical areas related to a specific function, DTI allows to reconstruct a model of the sub-cortical connectivity. This paper describes the clinical application of fMRI and DTI, enlightening sensitivity and specificity in comparison to gold standard and underlining their limitations in surgical decision making.
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Affiliation(s)
- Marco Conti Nibali
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy -
| | - Marco Rossi
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy
| | - Tommaso Sciortino
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy
| | - Marco Riva
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy.,Department of Medical Biotechnology and Translational Medicine, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy
| | - Lorenzo G Gay
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy
| | - Federico Pessina
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Lorenzo Bello
- Unit of Neurosurgical Oncology, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, IRCCS, University of Milan, Milan, Italy
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Léger É, Reyes J, Drouin S, Collins DL, Popa T, Kersten-Oertel M. Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system. Healthc Technol Lett 2018; 5:137-142. [PMID: 30800320 PMCID: PMC6372086 DOI: 10.1049/htl.2018.5063] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 08/20/2018] [Indexed: 01/02/2023] Open
Abstract
In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors' method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.
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Affiliation(s)
- Étienne Léger
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
| | - Jonatan Reyes
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
| | - Simon Drouin
- Department of Biomedical Engineering, McGill University, Montréal, Canada
| | - D. Louis Collins
- Department of Biomedical Engineering, McGill University, Montréal, Canada
| | - Tiberiu Popa
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
- PERFORM Centre, Concordia University, Montréal, Canada
| | - Marta Kersten-Oertel
- Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
- PERFORM Centre, Concordia University, Montréal, Canada
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34
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Machado I, Toews M, Luo J, Unadkat P, Essayed W, George E, Teodoro P, Carvalho H, Martins J, Golland P, Pieper S, Frisken S, Golby A, Wells W. Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching. Int J Comput Assist Radiol Surg 2018; 13:1525-1538. [PMID: 29869321 PMCID: PMC6151276 DOI: 10.1007/s11548-018-1786-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/03/2018] [Indexed: 12/19/2022]
Abstract
PURPOSE The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. METHODS A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. RESULTS Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. CONCLUSIONS This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.
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Affiliation(s)
- Inês Machado
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA.
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal.
| | - Matthew Toews
- École de Technologie Superieure, 1100 Notre-Dame St W, Montreal, QC, H3C 1K3, Canada
| | - Jie Luo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
- Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan
| | - Prashin Unadkat
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
| | - Walid Essayed
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
| | - Elizabeth George
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
| | - Pedro Teodoro
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Herculano Carvalho
- Department of Neurosurgery, CHLN, Hospital de Santa Maria, Avenida Professor Egas Moniz, 1649-035, Lisbon, Portugal
| | - Jorge Martins
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA
| | - Steve Pieper
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
- Isomics, Inc., 55 Kirkland St, Cambridge, MA, 02138, USA
| | - Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
| | - Alexandra Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
| | - William Wells
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, MA, 02115, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA
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Bui HP, Tomar S, Courtecuisse H, Audette M, Cotin S, Bordas SPA. Controlling the error on target motion through real-time mesh adaptation: Applications to deep brain stimulation. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2958. [PMID: 29314783 DOI: 10.1002/cnm.2958] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 12/07/2017] [Accepted: 12/27/2017] [Indexed: 06/07/2023]
Abstract
An error-controlled mesh refinement procedure for needle insertion simulations is presented. As an example, the procedure is applied for simulations of electrode implantation for deep brain stimulation. We take into account the brain shift phenomena occurring when a craniotomy is performed. We observe that the error in the computation of the displacement and stress fields is localised around the needle tip and the needle shaft during needle insertion simulation. By suitably and adaptively refining the mesh in this region, our approach enables to control, and thus to reduce, the error whilst maintaining a coarser mesh in other parts of the domain. Through academic and practical examples we demonstrate that our adaptive approach, as compared with a uniform coarse mesh, increases the accuracy of the displacement and stress fields around the needle shaft and, while for a given accuracy, saves computational time with respect to a uniform finer mesh. This facilitates real-time simulations. The proposed methodology has direct implications in increasing the accuracy, and controlling the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anaesthesia, or cryotherapy. Moreover, the proposed approach can be helpful in the development of robotic surgeries because the simulation taking place in the control loop of a robot needs to be accurate, and to occur in real time.
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Affiliation(s)
- Huu Phuoc Bui
- Institute of Computational Engineering, University of Luxembourg, Faculty of Sciences Communication and Technology, Luxembourg
| | - Satyendra Tomar
- Institute of Computational Engineering, University of Luxembourg, Faculty of Sciences Communication and Technology, Luxembourg
| | | | - Michel Audette
- Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, USA
| | | | - Stéphane P A Bordas
- Institute of Computational Engineering, University of Luxembourg, Faculty of Sciences Communication and Technology, Luxembourg
- Institute of Mechanics and Advanced Materials, School of Engineering, Cardiff University, UK
- Intelligent Systems for Medicine Laboratory, University of Western Australia, Perth, Australia
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Luo M, Frisken SF, Weis JA, Clements LW, Unadkat P, Thompson RC, Golby AJ, Miga MI. Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery. J Med Imaging (Bellingham) 2017; 4:035003. [PMID: 28924573 PMCID: PMC5596210 DOI: 10.1117/1.jmi.4.3.035003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 08/21/2017] [Indexed: 11/14/2022] Open
Abstract
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of [Formula: see text] tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift ([Formula: see text], [Formula: see text] measurements per case), the alignment error due to brain shift reduced from [Formula: see text] to [Formula: see text], representing [Formula: see text] correction. These first steps toward validation are promising for model-based strategies.
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Affiliation(s)
- Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Sarah F. Frisken
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Jared A. Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States
| | - Logan W. Clements
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Prashin Unadkat
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Reid C. Thompson
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
| | - Alexandra J. Golby
- Brigham and Women’s Hospital, Department of Radiology, Boston, Massachusetts, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University, Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
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