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Brem S, Hoch MJ. Commentary: Resting State Functional Networks in Gliomas: Validation With Direct Electrical Stimulation Using a New Tool for Planning Brain Resections. Neurosurgery 2024:00006123-990000000-01215. [PMID: 38869302 DOI: 10.1227/neu.0000000000003065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Affiliation(s)
- Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Glioblastoma Translational Center of Excellence (TCE), Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael J Hoch
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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2
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Kamagata K, Andica C, Uchida W, Takabayashi K, Saito Y, Lukies M, Hagiwara A, Fujita S, Akashi T, Wada A, Hori M, Kamiya K, Zalesky A, Aoki S. Advancements in Diffusion MRI Tractography for Neurosurgery. Invest Radiol 2024; 59:13-25. [PMID: 37707839 DOI: 10.1097/rli.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
ABSTRACT Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.
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Affiliation(s)
- Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan (K.K., C.A., W.U., K.T., Y.S., A.H., S.F., T.A., A.W., S.A.); Faculty of Health Data Science, Juntendo University, Chiba, Japan (C.A., S.A.); Department of Radiology, Alfred Health, Melbourne, Victoria, Australia (M.L.); Department of Radiology, University of Tokyo, Tokyo, Japan (S.F.); Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan (M.H., K.K.); Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia (A.Z.); and Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia (A.Z.)
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3
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Meesters S, Landers M, Rutten GJ, Florack L. Subject-Specific Automatic Reconstruction of White Matter Tracts. J Digit Imaging 2023; 36:2648-2661. [PMID: 37537513 PMCID: PMC10584769 DOI: 10.1007/s10278-023-00883-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.
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Affiliation(s)
- Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Maud Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Luc Florack
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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4
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Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. Neuroimage 2023; 273:120086. [PMID: 37019346 PMCID: PMC10958986 DOI: 10.1016/j.neuroimage.2023.120086] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/02/2023] [Indexed: 04/05/2023] Open
Abstract
White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
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Affiliation(s)
- Yuqian Chen
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | | | - Tengfei Xue
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | - Yang Song
- The University of New South Wales, NSW, Australia
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5
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Mandal AS, Brem S, Suckling J. Brain network mapping and glioma pathophysiology. Brain Commun 2023; 5:fcad040. [PMID: 36895956 PMCID: PMC9989143 DOI: 10.1093/braincomms/fcad040] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/23/2022] [Accepted: 02/18/2023] [Indexed: 02/25/2023] Open
Abstract
Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.
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Affiliation(s)
- Ayan S Mandal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Steven Brem
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, PA 19104, USA
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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6
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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7
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Seow P, Hernowo AT, Narayanan V, Wong JHD, Bahuri NFA, Cham CY, Abdullah NA, Kadir KAA, Rahmat K, Ramli N. Neural Fiber Integrity in High- Versus Low-Grade Glioma using Probabilistic Fiber Tracking. Acad Radiol 2021; 28:1721-1732. [PMID: 33023809 DOI: 10.1016/j.acra.2020.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 02/02/2023]
Abstract
RATIONALE AND OBJECTIVES Gliomatous tumors are known to affect neural fiber integrity, either by displacement or destruction. The aim of this study is to investigate the integrity and distribution of the white matter tracts within and around the glioma regions using probabilistic fiber tracking. MATERIAL AND METHODS Forty-two glioma patients were subjected to MRI using a standard tumor protocol with diffusion tensor imaging (DTI). The tumor and peritumor regions were delineated using snake model with reference to structural and diffusion MRI. A preprocessing pipeline of the structural MRI image, DTI data, and tumor regions was implemented. Tractography was performed to delineate the white matter (WM) tracts in the selected tumor regions via probabilistic fiber tracking. DTI indices were investigated through comparative mapping of WM tracts and tumor regions in low-grade gliomas (LGG) and high-grade gliomas (HGG). RESULTS Significant differences were seen in the planar tensor (Cp) in peritumor regions; mean diffusivity, axial diffusivity and pure isotropic diffusion in solid-enhancing tumor regions; and fractional anisotropy, axial diffusivity, pure anisotropic diffusion (q), total magnitude of diffusion tensor (L), relative anisotropy, Cp and spherical tensor (Cs) in solid nonenhancing tumor regions for affected WM tracts. In most cases of HGG, the WM tracts were not completely destroyed, but found intact inside the tumor. DISCUSSION Probabilistic fiber tracking revealed the existence and distribution of WM tracts inside tumor core for both LGG and HGG groups. There were more DTI indices in the solid nonenhancing tumor region, which showed significant differences between LGG and HGG.
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Zhang L, Wang L, Xia H, Tan Y, Li C, Fang C. Connectomic mapping of brain-spinal cord neural networks: future directions in assessing spinal cord injury at rest. Neurosci Res 2021; 176:9-17. [PMID: 34699861 DOI: 10.1016/j.neures.2021.10.008] [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: 05/12/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 12/01/2022]
Abstract
Following spinal cord injury (SCI), the central nervous system undergoes significant reconstruction. The dynamic change in the interaction of the brain-spinal cord axis as well as in structure-function relations plays a vital role in the determination of neurological functions, which might have important clinical implications for the treatment and its efficacy evaluation of patients with SCI. Brain connectomes based on neuroimaging data is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. Importantly, increasing evidence shows that such resting-state signals can also be seen in the spinal cord. In the present review, we focus on the reconstruction of multi-level neural circuits after SCI. We also describe how the connectome concept could further our understanding of neuroplasticity after SCI. We propose that mapping the cortical-subcortical-spinal cord networks can provide novel insights into the pathologies of SCI.
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Affiliation(s)
- Lijian Zhang
- Postdoctoral Research Station of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China
| | - Luxuan Wang
- Department of Neurology, Affiliated Hospital of Hebei University, Hebei University, China
| | - Hechun Xia
- Department of Neurosurgery, General Hospital of Ningxia Medical University, Ningxia Medical University, China
| | - Yanli Tan
- Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China; Department of Pathology, Affiliated Hospital of Hebei University, Hebei University, China.
| | - Chunhui Li
- Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China.
| | - Chuan Fang
- Postdoctoral Research Station of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China.
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9
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Fang S, Liang Y, Li L, Wang L, Fan X, Wang Y, Jiang T. Tumor location-based classification of surgery-related language impairments in patients with glioma. J Neurooncol 2021; 155:143-152. [PMID: 34599481 DOI: 10.1007/s11060-021-03858-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/27/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Many patients with glioma experience surgery-related language impairment. This study developed a classification system to predict postoperative language prognosis. METHODS Sixty-eight patients were retrospectively reviewed. Based on their location, tumors were subtyped as follows: (I) inferior frontal lobe or precentral gyrus; (II) posterior central gyrus or supramarginal gyrus (above the lateral fissure level); (III) posterior region of the superior or middle temporal gyri or supramarginal gyrus (below the lateral fissure level); and (IV) insular lobe. The distance from the tumor to the superior longitudinal fasciculus/arcuate fasciculus was calculated. The recovery of language function was assessed using the Western Aphasia Battery before surgery, and a comprehensive language test was conducted on the day of surgery; 3, 7, and 14 days after surgery. Our follow-up information of was the comprehensive language test from telephone interviews in 3 months after surgery. RESULTS Thirty-three patients experienced transient language impairment within 1 week of surgery. Fourteen patients had permanent language impairment. Type II tumors, shorter distance from the tumor to the posterior superior longitudinal fasciculus/arcuate fasciculus, and isocitrate dehydrogenase mutations were risk factors for surgery-related language impairment. Regarding the presence or absence of permanent surgery-related language impairments, the cut-off distance between the tumor and posterior superior longitudinal fasciculus/arcuate fasciculus was 2.75 mm. CONCLUSIONS According to our classification, patients with type II tumors had the worst language prognosis and longest recovery time. Our classification, based on tumor location, can reliably predict postoperative language status and may be used to guide tumor resection.
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Affiliation(s)
- Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
| | - Yuchao Liang
- Beijing Neurosurgical Institute, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
| | - Lianwang Li
- Beijing Neurosurgical Institute, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
| | - Lei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China.
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China.
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China.
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119, the Western Road of the southern 4th Ring Road, Beijing, 100070, China.
- Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors Chinese Academy of Medical Sciences, Beijing, China.
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10
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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11
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Zoli M, Talozzi L, Martinoni M, Manners DN, Badaloni F, Testa C, Asioli S, Mitolo M, Bartiromo F, Rochat MJ, Fabbri VP, Sturiale C, Conti A, Lodi R, Mazzatenta D, Tonon C. From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures. Front Neurol 2021; 12:633209. [PMID: 33716935 PMCID: PMC7952864 DOI: 10.3389/fneur.2021.633209] [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: 11/24/2020] [Accepted: 01/26/2021] [Indexed: 01/09/2023] Open
Abstract
Background: Tractography has been widely adopted to improve brain gliomas' surgical planning and guide their resection. This study aimed to evaluate state-of-the-art of arcuate fasciculus (AF) tractography for surgical planning and explore the role of along-tract analyses in vivo for characterizing tumor histopathology. Methods: High angular resolution diffusion imaging (HARDI) images were acquired for nine patients with tumors located in or near language areas (age: 41 ± 14 years, mean ± standard deviation; five males) and 32 healthy volunteers (age: 39 ± 16 years; 16 males). Phonemic fluency task fMRI was acquired preoperatively for patients. AF tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. Along-tract analyses were performed, dividing the AF into 15 segments along the length of the tract defined using the Laplacian operator. For each AF segment, diffusion tensor imaging (DTI) measures were compared with those obtained in healthy controls (HCs). The hemispheric laterality index (LI) was calculated from language task fMRI activations in the frontal, parietal, and temporal lobe parcellations. Tumors were grouped into low/high grade (LG/HG). Results: Four tumors were LG gliomas (one dysembryoplastic neuroepithelial tumor and three glioma grade II) and five HG gliomas (two grade III and three grade IV). For LG tumors, gross total removal was achieved in all but one case, for HG in two patients. Tractography identified the AF trajectory in all cases. Four along-tract DTI measures potentially discriminated LG and HG tumor patients (false discovery rate < 0.1): the number of abnormal MD and RD segments, median AD, and MD measures. Both a higher number of abnormal AF segments and a higher AD and MD measures were associated with HG tumor patients. Moreover, correlations (unadjusted p < 0.05) were found between the parietal lobe LI and the DTI measures, which discriminated between LG and HG tumor patients. In particular, a more rightward parietal lobe activation (LI < 0) correlated with a higher number of abnormal MD segments (R = −0.732) and RD segments (R = −0.724). Conclusions: AF tractography allows to detect the course of the tract, favoring the safer-as-possible tumor resection. Our preliminary study shows that along-tract DTI metrics can provide useful information for differentiating LG and HG tumors during pre-surgical tumor characterization.
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Affiliation(s)
- Matteo Zoli
- Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Lia Talozzi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Matteo Martinoni
- Neurosurgery Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David N Manners
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Filippo Badaloni
- Neurosurgery Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Sofia Asioli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.,Anatomic Pathology Unit, Azienda USL di Bologna, Bologna, Italy
| | - Micaela Mitolo
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fiorina Bartiromo
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Magali Jane Rochat
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Viscardo Paolo Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Carmelo Sturiale
- Neurosurgery Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Alfredo Conti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.,Neurosurgery Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.,Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Diego Mazzatenta
- Pituitary Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.,Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
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12
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Henderson F, Abdullah KG, Verma R, Brem S. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus 2021; 48:E6. [PMID: 32006950 DOI: 10.3171/2019.11.focus19785] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 11/13/2019] [Indexed: 12/21/2022]
Abstract
The ability of diffusion tensor MRI to detect the preferential diffusion of water in cerebral white matter tracts enables neurosurgeons to noninvasively visualize the relationship of lesions to functional neural pathways. Although viewed as a research tool in its infancy, diffusion tractography has evolved into a neurosurgical tool with applications in glioma surgery that are enhanced by evolutions in crossing fiber visualization, edema correction, and automated tract identification. In this paper the current literature supporting the use of tractography in brain tumor surgery is summarized, highlighting important clinical studies on the application of diffusion tensor imaging (DTI) for preoperative planning of glioma resection, and risk assessment to analyze postoperative outcomes. The key methods of tractography in current practice and crucial white matter fiber bundles are summarized. After a review of the physical basis of DTI and post-DTI tractography, the authors discuss the methodologies with which to adapt DT image processing for surgical planning, as well as the potential of connectomic imaging to facilitate a network approach to oncofunctional optimization in glioma surgery.
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Affiliation(s)
- Fraser Henderson
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania.,3Department of Neurosurgery, The Medical University of South Carolina, Charleston, South Carolina; and
| | - Kalil G Abdullah
- 4Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ragini Verma
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania.,2DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Steven Brem
- 1Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania
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13
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Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation. Med Image Anal 2020; 65:101761. [PMID: 32622304 PMCID: PMC7483951 DOI: 10.1016/j.media.2020.101761] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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14
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Brem S, Henderson F. Commentary: 5-Aminolevulinic Acid and Contrast-Enhanced Ultrasound: The Combination of the 2 Techniques to Optimize the Extent of Resection in Glioblastoma Surgery. Neurosurgery 2020; 86:E541-E543. [PMID: 32186338 DOI: 10.1093/neuros/nyaa061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 01/30/2020] [Indexed: 12/20/2022] Open
Affiliation(s)
- Steven Brem
- Department of Neurosurgery, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Fraser Henderson
- Department of Neurological Surgery, Medical University of South Carolina, Charleston, South Carolina
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15
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Parker D, Ould Ismail AA, Wolf R, Brem S, Alexander S, Hodges W, Pasternak O, Caruyer E, Verma R. Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data. PLoS One 2020; 15:e0233645. [PMID: 32469944 PMCID: PMC7259683 DOI: 10.1371/journal.pone.0233645] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 05/10/2020] [Indexed: 12/19/2022] Open
Abstract
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
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Affiliation(s)
- Drew Parker
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Abdol Aziz Ould Ismail
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Ronald Wolf
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Wes Hodges
- Synaptive Medical Inc., Toronto, ON, Canada
| | - Ofer Pasternak
- Departments of Psychiatry & Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | | | - Ragini Verma
- DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- * E-mail: ,
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16
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Pati S, Singh A, Rathore S, Gastounioti A, Bergman M, Ngo P, Ha SM, Bounias D, Minock J, Murphy G, Li H, Bhattarai A, Wolf A, Sridaran P, Kalarot R, Akbari H, Sotiras A, Thakur SP, Verma R, Shinohara RT, Yushkevich P, Fan Y, Kontos D, Davatzikos C, Bakas S. The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2020; 11993:380-394. [PMID: 32754723 PMCID: PMC7402244 DOI: 10.1007/978-3-030-46643-5_38] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Phuc Ngo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitrios Bounias
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - James Minock
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Grayson Murphy
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amit Bhattarai
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Wolf
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Patmaa Sridaran
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ratheesh Kalarot
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, School of Medicine, Washington University in St. Louis, Saint Louis, MO, USA
| | - Siddhesh P Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab., University of Pennsylvania (PICSL), Philadelphia, PA, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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17
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Wende T, Hoffmann KT, Meixensberger J. Tractography in Neurosurgery: A Systematic Review of Current Applications. J Neurol Surg A Cent Eur Neurosurg 2020; 81:442-455. [PMID: 32176926 DOI: 10.1055/s-0039-1691823] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The ability to visualize the brain's fiber connections noninvasively in vivo is relatively young compared with other possibilities of functional magnetic resonance imaging. Although many studies showed tractography to be of promising value for neurosurgical care, the implications remain inconclusive. An overview of current applications is presented in this systematic review. A search was conducted for (("tractography" or "fiber tracking" or "fibre tracking") and "neurosurgery") that produced 751 results. We identified 260 relevant articles and added 20 more from other sources. Most publications concerned surgical planning for resection of tumors (n = 193) and vascular lesions (n = 15). Preoperative use of transcranial magnetic stimulation was discussed in 22 of these articles. Tractography in skull base surgery presents a special challenge (n = 29). Fewer publications evaluated traumatic brain injury (TBI) (n = 25) and spontaneous intracranial bleeding (n = 22). Twenty-three articles focused on tractography in pediatric neurosurgery. Most authors found tractography to be a valuable addition in neurosurgical care. The accuracy of the technique has increased over time. There are articles suggesting that tractography improves patient outcome after tumor resection. However, no reliable biomarkers have yet been described. The better rehabilitation potential after TBI and spontaneous intracranial bleeding compared with brain tumors offers an insight into the process of neurorehabilitation. Tractography and diffusion measurements in some studies showed a correlation with patient outcome that might help uncover the neuroanatomical principles of rehabilitation itself. Alternative corticofugal and cortico-cortical networks have been implicated in motor recovery after ischemic stroke, suggesting more complex mechanisms in neurorehabilitation that go beyond current models. Hence tractography may potentially be able to predict clinical deficits and rehabilitation potential, as well as finding possible explanations for neurologic disorders in retrospect. However, large variations of the results indicate a lack of data to establish robust diagnostical concepts at this point. Therefore, in vivo tractography should still be interpreted with caution and by experienced surgeons.
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Affiliation(s)
- Tim Wende
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Germany
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18
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Zhong Y, Huang Y, Ding H. Re-understanding of edema zone from the nerve fiber bundles: A narrative review. GLIOMA 2020. [DOI: 10.4103/glioma.glioma_28_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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19
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Henderson F, Brem S. Commentary: The Role of Laser Interstitial Thermal Therapy in Surgical Neuro-Oncology: Series of 100 Consecutive Patients. Neurosurgery 2019; 87:E101-E103. [DOI: 10.1093/neuros/nyz528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 10/04/2019] [Indexed: 11/12/2022] Open
Affiliation(s)
- Fraser Henderson
- Department of Neurological Surgery, Medical University of South Carolina, Charleston, South Carolina
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Steven Brem
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
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20
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Zhang F, Hoffmann N, Karayumak SC, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2019; 11766:599-608. [PMID: 32558816 DOI: 10.1007/978-3-030-32248-9_67] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classification accuracy of 90.99%. The method is evaluated on a test dataset of 374 dMRI scans from three independently acquired populations across health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art white matter tract segmentation methods. Experimental results show that our method obtains a highly consistent segmentation result, where over 99% of the fiber tracts are successfully detected across all subjects under study, most importantly, including patients with space occupying brain tumors. The proposed method leverages deep learning techniques and provides a much faster and more efficient tool for large data analysis than methods using traditional machine learning techniques.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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21
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Foster CH, Morone PJ, Cohen-Gadol A. Awake craniotomy in glioma surgery: is it necessary? J Neurosurg Sci 2018; 63:162-178. [PMID: 30259721 DOI: 10.23736/s0390-5616.18.04590-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
INTRODUCTION The awake craniotomy has evolved from its humble beginnings in ancient cultures to become one of the most eloquent modern neurosurgical procedures. The development of intraoperative mapping techniques like direct electrostimulation of the cortex and subcortical white matter have further argued for its place in the neurosurgeon's armamentarium. Yet the suitability of the awake craniotomy with intraoperative functional mapping (ACWM) to optimize oncofunctional balance after peri-eloquent glioma resection continues to be a topic of active investigation as new methods of intraoperative monitoring and some unfavorable outcome data question its necessity. EVIDENCE ACQUISITION The neurosurgery and anesthesiology literatures were scoured for English-language studies that analyzed or reviewed the ACWM or its components as applied to glioma surgery via the PubMed, ClinicalKey, and OvidMEDLINE® databases or via direct online searches of journal archives. EVIDENCE SYNTHESIS Information on background, conceptualization, standard techniques, and outcomes of the ACWM were provided and compared. We parceled the procedure into its components and qualitatively described positive and negative outcome data for each. Findings were presented in the context of each study without attempt at quantitative analysis or reconciliation of heterogeneity between studies. Certain illustrative studies were highlighted throughout the review. Overarching conclusions were drawn based on level of evidence, expert opinion, and predominate concordance of data across studies in the literature. CONCLUSIONS Most investigators and studies agree that the ACWM is the best currently available approach to optimize oncofunctional balance in this difficult-to-treat patient population. This qualitative review synthesizes the most currently available data on the topic to provide contemporaneous insight into how and why the ACWM has become a favorite operation of neurosurgeons worldwide for the resection of gliomas from eloquent brain.
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Affiliation(s)
- Chase H Foster
- Department of Neurological Surgery, George Washington University Hospital, Washington D.C., USA -
| | - Peter J Morone
- Department of Neurological Surgery, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA
| | - Aaron Cohen-Gadol
- Goodman Campbell Brain and Spine, Department of Neurological Surgery, Indiana University, Indianapolis, IN, USA
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22
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Gong S, Zhang F, Norton I, Essayed WI, Unadkat P, Rigolo L, Pasternak O, Rathi Y, Hou L, Golby AJ, O’Donnell LJ. Free water modeling of peritumoral edema using multi-fiber tractography: Application to tracking the arcuate fasciculus for neurosurgical planning. PLoS One 2018; 13:e0197056. [PMID: 29746544 PMCID: PMC5944935 DOI: 10.1371/journal.pone.0197056] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 04/25/2018] [Indexed: 12/13/2022] Open
Abstract
Purpose Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. Materials and methods We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. Results The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. Conclusion Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.
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Affiliation(s)
- Shun Gong
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Neurosurgery, Shanghai Institute of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
| | - Fan Zhang
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Isaiah Norton
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Walid I. Essayed
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Prashin Unadkat
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Laura Rigolo
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ofer Pasternak
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lijun Hou
- Department of Neurosurgery, Shanghai Institute of Neurosurgery, Shanghai Changzheng Hospital, Shanghai, China
| | - Alexandra J. Golby
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lauren J. O’Donnell
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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Esquenazi Y, Friedman E, Liu Z, Zhu JJ, Hsu S, Tandon N. The Survival Advantage of "Supratotal" Resection of Glioblastoma Using Selective Cortical Mapping and the Subpial Technique. Neurosurgery 2018; 81:275-288. [PMID: 28368547 DOI: 10.1093/neuros/nyw174] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2015] [Accepted: 08/12/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND A substantial body of evidence suggests that cytoreductive surgery is a prerequisite to prolonging survival in patients with glioblastoma (GBM). OBJECTIVE To evaluate the safety and impact of "supratotal" resections beyond the zone of enhancement seen on magnetic resonance imaging scans, using a subpial technique. METHODS We retrospectively evaluated 86 consecutive patients with primary GBM, managed by the senior author, using a subpial resection technique with or without carmustine (BCNU) wafer implantation. Multivariate Cox proportional hazards regression was used to analyze clinical, radiological, and outcome variables. Overall impacts of extent of resection (EOR) and BCNU wafer placement were compared using Kaplan-Meier survival analysis. RESULTS Mean patient age was 56 years. The median OS for the group was 18.1 months. Median OS for patients undergoing gross total, near-total, and subtotal resection were 54, 16.5, and 13.2 months, respectively. Patients undergoing near-total resection ( P = .05) or gross total resection ( P < .01) experienced statistically significant longer survival time than patients undergoing subtotal resection as well as patients undergoing ≥95% EOR ( P < .01) when compared to <95% EOR. The addition of BCNU wafers had no survival advantage. CONCLUSIONS The subpial technique extends the resection beyond the contrast enhancement and is associated with an overall survival beyond that seen in similar series where resection of the enhancement portion is performed. The effect of supratotal resection on survival exceeded the effects of age, Karnofsky performance score, and tumor volume. A prospective study would help to quantify the impact of the subpial technique on quality of life and survival as compared to a traditional resection limited to the enhancing tumor.
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Affiliation(s)
- Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery and Mischer Neuroscience Institute, Houston, Texas
| | - Elliott Friedman
- Department of Radiology, Medical School, University of Texas Health Science Center at Houston, Houston, Texas
| | - Zheyu Liu
- Department of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Jay-Jiguang Zhu
- Vivian L. Smith Department of Neurosurgery and Mischer Neuroscience Institute, Houston, Texas
| | - Sigmund Hsu
- Vivian L. Smith Department of Neurosurgery and Mischer Neuroscience Institute, Houston, Texas
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery and Mischer Neuroscience Institute, Houston, Texas
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Rathore S, Bakas S, Pati S, Akbari H, Kalarot R, Sridharan P, Rozycki M, Bergman M, Tunc B, Verma R, Bilello M, Davatzikos C. Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2018; 10670:133-145. [PMID: 29733087 DOI: 10.1007/978-3-319-75238-9_12] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.
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Affiliation(s)
- Saima Rathore
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ratheesh Kalarot
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Patmaa Sridharan
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Martin Rozycki
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Birkan Tunc
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
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25
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Davatzikos C, Rathore S, Bakas S, Pati S, Bergman M, Kalarot R, Sridharan P, Gastounioti A, Jahani N, Cohen E, Akbari H, Tunc B, Doshi J, Parker D, Hsieh M, Sotiras A, Li H, Ou Y, Doot RK, Bilello M, Fan Y, Shinohara RT, Yushkevich P, Verma R, Kontos D. Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J Med Imaging (Bellingham) 2018; 5:011018. [PMID: 29340286 PMCID: PMC5764116 DOI: 10.1117/1.jmi.5.1.011018] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 12/05/2017] [Indexed: 12/26/2022] Open
Abstract
The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.
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Affiliation(s)
- Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ratheesh Kalarot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Patmaa Sridharan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aimilia Gastounioti
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Nariman Jahani
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Eric Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Birkan Tunc
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Drew Parker
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michael Hsieh
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yangming Ou
- Massachusetts General Hospital, Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States
| | - Robert K. Doot
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics (CCEB), Department of Biostatistics, Epidemiology, and Informatics, Philadelphia, Pennsylvania, United States
| | - Paul Yushkevich
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), Philadelphia, Pennsylvania, United States
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Garyfallidis E, Côté MA, Rheault F, Sidhu J, Hau J, Petit L, Fortin D, Cunanne S, Descoteaux M. Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 2017; 170:283-295. [PMID: 28712994 DOI: 10.1016/j.neuroimage.2017.07.015] [Citation(s) in RCA: 149] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 06/06/2017] [Accepted: 07/10/2017] [Indexed: 11/17/2022] Open
Abstract
Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive knowledge of white matter anatomy. This virtual dissection often requires several inclusion and exclusion regions-of-interest that make it a process that is very hard to reproduce across experts. Having automated tools that can extract white matter bundles for tract-based studies of large numbers of people is of great interest for neuroscience and neurosurgical planning. The purpose of our proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform. This can help explore large tractograms from multiple persons directly in their native space. RecoBundles leverages latest state-of-the-art streamline-based registration and clustering to recognize and extract bundles using prior bundle models. RecoBundles uses bundle models as shape priors for detecting similar streamlines and bundles in tractograms. RecoBundles is 100% streamline-based, is efficient to work with millions of streamlines and, most importantly, is robust and adaptive to incomplete data and bundles with missing components. It is also robust to pathological brains with tumors and deformations. We evaluated our results using multiple bundles and showed that RecoBundles is in good agreement with the neuroanatomical experts and generally produced more dense bundles. Across all the different experiments reported in this paper, RecoBundles was able to identify the core parts of the bundles, independently from tractography type (deterministic or probabilistic) or size. Thus, RecoBundles can be a valuable method for exploring tractograms and facilitating tractometry studies.
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Affiliation(s)
- Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, School of Informatics and Computing, Indiana University, Bloomington, USA.
| | - Marc-Alexandre Côté
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada
| | - Janice Hau
- Brain Development Imaging Lab (BDIL), Department of Psychology, San Diego State University, USA; Groupe d' Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux, France
| | - Laurent Petit
- Groupe d' Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux, France
| | - David Fortin
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Stephen Cunanne
- Research Center on Aging and Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada
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27
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Amirhossein JS, Arya N, Mario G, Amir S. In Reply: Evaluation of Diffusion Tensor Imaging-Based Tractography of the Corticospinal Tract: A Correlative Study With Intraoperative Magnetic Resonance Imaging and Direct Electrical Subcortical Stimulation. Neurosurgery 2017; 81:E9-E10. [DOI: 10.1093/neuros/nyx083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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28
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Essayed WI, Zhang F, Unadkat P, Cosgrove GR, Golby AJ, O'Donnell LJ. White matter tractography for neurosurgical planning: A topography-based review of the current state of the art. Neuroimage Clin 2017; 15:659-672. [PMID: 28664037 PMCID: PMC5480983 DOI: 10.1016/j.nicl.2017.06.011] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 04/17/2017] [Accepted: 06/08/2017] [Indexed: 12/13/2022]
Abstract
We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.
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Affiliation(s)
- Walid I Essayed
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Prashin Unadkat
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - G Rees Cosgrove
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lauren J O'Donnell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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29
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O'Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, Albi A, Olubiyi O, Meola A, Essayed WI, Unadkat P, Ciris PA, Wells WM, Rathi Y, Westin CF, Golby AJ. Automated white matter fiber tract identification in patients with brain tumors. NEUROIMAGE-CLINICAL 2016; 13:138-153. [PMID: 27981029 PMCID: PMC5144756 DOI: 10.1016/j.nicl.2016.11.023] [Citation(s) in RCA: 90] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 10/13/2016] [Accepted: 11/22/2016] [Indexed: 01/06/2023]
Abstract
We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions. Spectral clustering machine learning approach for white matter tract identification Data-driven white matter parcellation learned from healthy subjects tractography White matter parcellation applied to 18 consecutive patients with brain tumors Arcuate fasciculus and corticospinal tracts identified in all patients All tracts within 3 mm of corresponding patient-specific functional activations
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Affiliation(s)
- Lauren J O'Donnell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yannick Suter
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland
| | - Laura Rigolo
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Pegah Kahali
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Isaiah Norton
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Angela Albi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Mind/Brain Sciences (CIMEC), University of Trento, Rovereto, Italy
| | - Olutayo Olubiyi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Antonio Meola
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Walid I Essayed
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Prashin Unadkat
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Pelin Aksit Ciris
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biomedical Engineering, Akdeniz University, Antalya, Turkey
| | - William M Wells
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Alexandra J Golby
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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