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Nowinski WL. Taxonomy of Acute Stroke: Imaging, Processing, and Treatment. Diagnostics (Basel) 2024; 14:1057. [PMID: 38786355 PMCID: PMC11119045 DOI: 10.3390/diagnostics14101057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.
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Affiliation(s)
- Wieslaw L Nowinski
- Sano Centre for Computational Personalised Medicine, Czarnowiejska 36, 30-054 Krakow, Poland
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2
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Conti A, Gambadauro NM, Mantovani P, Picciano CP, Rosetti V, Magnani M, Lucerna S, Tuleasca C, Cortelli P, Giannini G. A Brief History of Stereotactic Atlases: Their Evolution and Importance in Stereotactic Neurosurgery. Brain Sci 2023; 13:brainsci13050830. [PMID: 37239302 DOI: 10.3390/brainsci13050830] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/05/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023] Open
Abstract
Following the recent acquisition of unprecedented anatomical details through state-of-the-art neuroimaging, stereotactic procedures such as microelectrode recording (MER) or deep brain stimulation (DBS) can now rely on direct and accurately individualized topographic targeting. Nevertheless, both modern brain atlases derived from appropriate histological techniques involving post-mortem studies of human brain tissue and the methods based on neuroimaging and functional information represent a valuable tool to avoid targeting errors due to imaging artifacts or insufficient anatomical details. Hence, they have thus far been considered a reference guide for functional neurosurgical procedures by neuroscientists and neurosurgeons. In fact, brain atlases, ranging from the ones based on histology and histochemistry to the probabilistic ones grounded on data derived from large clinical databases, are the result of a long and inspiring journey made possible thanks to genial intuitions of great minds in the field of neurosurgery and to the technical advancement of neuroimaging and computational science. The aim of this text is to review the principal characteristics highlighting the milestones of their evolution.
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Affiliation(s)
- Alfredo Conti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Nicola Maria Gambadauro
- Stroke Unit- Barking, Havering and Redbrige University Hospitals NHS Trust, Queen's Hospital, Rom Valley Way, London RM7 0AG, UK
| | - Paolo Mantovani
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Canio Pietro Picciano
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Vittoria Rosetti
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Marcello Magnani
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Sebastiano Lucerna
- Department of Neurosurgery, AOU "G. Martino", Via Consolare Valeria 1, 98125 Messina, Italy
| | - Constantin Tuleasca
- Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
- Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL), Rue du Bugnon 21 CH-1011, 1015 Lausanne, Switzerland
- Ecole Polytechnique Fédérale de Lausanne (EPFL, LTS-5), Rte Cantonale, 1015 Lausanne, Switzerland
| | - Pietro Cortelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
| | - Giulia Giannini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Via Altura 3, 40123 Bologna, Italy
- Dipartimento di Biomorfologia e. Scienze Neuromotorie (DIBINEM), Alma Mater Studiorum Università di Bologna, Via Altura 3, 40123 Bologna, Italy
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Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas. Neuroinformatics 2021; 20:405-426. [PMID: 34825350 PMCID: PMC9546954 DOI: 10.1007/s12021-021-09555-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.
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Nowinski WL, Walecki J, Półtorak-Szymczak G, Sklinda K, Mruk B. Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods. PeerJ 2021; 8:e10444. [PMID: 33391867 PMCID: PMC7759129 DOI: 10.7717/peerj.10444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/07/2020] [Indexed: 01/01/2023] Open
Abstract
Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Warsaw, Poland
| | - Jerzy Walecki
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Gabriela Półtorak-Szymczak
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Katarzyna Sklinda
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
| | - Bartosz Mruk
- Department of Radiology and Diagnostic Imaging, Center of Postgraduate Medical Education, Warsaw, Poland
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Abstract
Stroke is a leading cause of death and a major cause of permanent disability. Its management is demanding because of variety of protocols, imaging modalities, pulse sequences, hemodynamic maps, criteria for treatment, and time constraints to promptly evaluate and treat. To cope with some of these issues, we propose novel, patented solutions in stroke management by employing multiple brain atlases for diagnosis, treatment, and prediction. Numerous and diverse CT and MRI scans are used: ARIC cohort, ischemic and hemorrhagic stroke CT cases, MRI cases with multiple pulse sequences, and 128 stroke CT patients, each with 170 variables and one year follow-up. The method employs brain atlases of anatomy, blood supply territories, and probabilistic stroke atlas. It rapidly maps an atlas to scan and provides atlas-assisted scan processing. Atlas-to-scan mapping is application-dependent and handles three types of regions of interest (ROIs): atlas-defined ROIs, atlas-quantified ROIs, and ROIs creating an atlas. An ROI is defined by atlas-guided anatomy or scan-derived pathology. The atlas defines ROI or quantifies it. A brain atlas potential has been illustrated in four atlas-assisted applications for stroke occurrence prediction and screening, rapid and automatic stroke diagnosis in emergency room, quantitative decision support in thrombolysis in ischemic stroke, and stroke outcome prediction and treatment assessment. The use of brain atlases in stroke has many potential advantages, including rapid processing, automated and robust handling, wide range of applications, and quantitative assessment. Further work is needed to enhance the developed prototypes, clinically validate proposed solutions, and introduce them to clinical practice.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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Abstract
We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paull II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski in Warsaw, Poland
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7
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Abstract
Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski in Warsaw, Poland
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Hajimani E, Ruano MG, Ruano AE. An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:109-123. [PMID: 28688480 DOI: 10.1016/j.cmpb.2017.05.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 04/28/2017] [Accepted: 05/19/2017] [Indexed: 06/07/2023]
Abstract
OBJECTIVE This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. METHODS For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. RESULTS Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%).
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Affiliation(s)
- Elmira Hajimani
- Faculty of Science and Technology, University of Algarve, Faro, Portugal.
| | - M G Ruano
- Faculty of Science and Technology, University of Algarve, Faro, Portugal and CISUC, University of Coimbra, Portugal.
| | - A E Ruano
- Faculty of Science and Technology, University of Algarve, Faro, Portugal and IDMEC, Instituto Superior Técnico, University of Lisbon, Portugal.
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Nowinski WL. Usefulness of brain atlases in neuroradiology: Current status and future potential. Neuroradiol J 2016; 29:260-8. [PMID: 27154190 DOI: 10.1177/1971400916648338] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Human brain atlases, although prevalent in medical education and stereotactic and functional neurosurgery, are not yet applied practically in neuroradiology. In a step towards introducing brain atlases to neuroradiology, we discuss nine different situations of potential atlas use: (1) to support interpretation of brain scans with clearly visible structures (to increase confidence of non-neuroradiologists); (2) to delineate and label scans of low anatomical content (with indiscernible or poorly visible anatomy); (3) to assist in generating the structured report; (4) to assist in interpreting small deep lesions, since an atlas's anatomical parcellation is higher than that of the interpreted scan; (5) to approximate distorted due to pathology (and unknown to the interpreter) anatomy and label it; (6) to cope with data explosion; (7) to assist in the interpretation of functional scans (to label the activation foci with the underlying anatomy and Brodmann's areas); (8) to support ischemic stroke image handling by means of atlases of anatomy and blood supply territories; and (9) to communicate image interpretation results (diagnosis) to others. The usefulness of the atlas for automatic structure identification, localisation, delineation, labelling and quantification, as well as for reporting and communication, potentially increases the interpreter's efficiency and confidence, as well as expedites image interpretation.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, Cardinal Stefan Wyszynski University, Poland
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Toward the holistic, reference, and extendable atlas of the human brain, head, and neck. Brain Inform 2015; 2:65-76. [PMID: 27747483 PMCID: PMC4883147 DOI: 10.1007/s40708-015-0012-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 01/29/2015] [Indexed: 12/16/2022] Open
Abstract
Despite numerous efforts, a fairly complete (holistic) anatomical model of the whole, normal, adult human brain, which is required as the reference in brain studies and clinical applications, has not yet been constructed. Our ultimate objective is to build this kind of atlas from advanced in vivo imaging. This work presents the taxonomy of our currently developed brain atlases and addresses the design, content, functionality, and current results in the holistic atlas development as well as atlas usefulness and future directions. We have developed to date 35 commercial brain atlases (along with numerous research prototypes), licensed to 63 companies and institutions, and made available to medical societies, organizations, medical schools, and individuals. These atlases have been applied in education, research, and clinical applications. Hundreds of thousands of patients have been treated by using our atlases. Based on this experience, the first version of the holistic and reference atlas of the brain, head, and neck has been developed and made available. The atlas has been created from multispectral 3 and 7 Tesla and high-resolution CT in vivo scans. It is fully 3D, scalable, interactive, and highly detailed with about 3,000 labeled components. This atlas forms a foundation for the development of a multi-level molecular, cellular, anatomical, physiological, and behavioral brain atlas platform.
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Stoel BC, Marquering HA, Staring M, Beenen LF, Slump CH, Roos YB, Majoie CB. Automated brain computed tomographic densitometry of early ischemic changes in acute stroke. J Med Imaging (Bellingham) 2015; 2:014004. [PMID: 26158082 PMCID: PMC4478844 DOI: 10.1117/1.jmi.2.1.014004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 03/03/2015] [Indexed: 11/14/2022] Open
Abstract
The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS ([Formula: see text] and [Formula: see text], respectively) and with follow-up mRS ([Formula: see text] and [Formula: see text]), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences [Formula: see text] point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.
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Affiliation(s)
- Berend C. Stoel
- Leiden University Medical Center, Division of Image Processing, Department of Radiology, Albinusdreef 2 Leiden 2333 AA, The Netherlands
| | - Henk A. Marquering
- Academic Medical Center, Department of Radiology, Amsterdam, The Netherlands
- Academic Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Marius Staring
- Leiden University Medical Center, Division of Image Processing, Department of Radiology, Albinusdreef 2 Leiden 2333 AA, The Netherlands
| | - Ludo F. Beenen
- Academic Medical Center, Department of Radiology, Amsterdam, The Netherlands
| | - Cornelis H. Slump
- University of Twente, MIRA Institute for Biomedical Technology and Technical Medicine, Enschede, The Netherlands
| | - Yvo B. Roos
- Academic Medical Center, Department of Neurology, Amsterdam, The Netherlands
| | - Charles B. Majoie
- Academic Medical Center, Department of Radiology, Amsterdam, The Netherlands
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Automated interhemispheric surface extraction in T1-weighted MRI using intensity and symmetry information. J Neurosci Methods 2014; 222:97-105. [PMID: 24239903 DOI: 10.1016/j.jneumeth.2013.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Revised: 11/04/2013] [Accepted: 11/05/2013] [Indexed: 11/21/2022]
Abstract
BACKGROUND Localizing the human interhemispheric region is of interest in image analysis mainly because it can be used for hemisphere separation and as a preprocessing step for interhemispheric structure localization. Many existing methods focus on only one of these applications. NEW METHOD Here a new Intensity and Symmetry based Interhemispheric Surface extraction method (ISIS) that enables both applications is presented. A combination of voxel intensity and local symmetry is used to optimize a surface from T1-weighted MRI. RESULTS ISIS was evaluated in regard to cerebral hemisphere separation using manual segmentations. It was also evaluated in regard to being a preprocessing step for interhemispheric structure localization using manually placed landmarks. COMPARISON WITH EXISTING METHODS Results were compared to cerebral hemisphere separations by BrainVisa and Freesurfer as well as to a midsagittal plane (MSP) extraction method. ISIS had less misclassified voxels than BrainVisa (ISIS: 0.119±0.114%, BrainVisa: 0.138±0.084%, p=0.020). Freesurfer had less misclassified voxels than ISIS for one dataset (ISIS: 0.063±0.056%, Freesurfer: 0.049±0.044%, p=0.019), but failed to produce usable results for another. Total voxel distance from all manual landmarks did not differ significantly between ISIS and the MSP method (ISIS: 4.00±1.88, MSP: 4.47±4.97). CONCLUSIONS ISIS was found successful in both cerebral hemisphere separation and as a preprocessing step for interhemispheric structure localization. It needs no time consuming preprocessing and extracts the interhemispheric surface in less than 30 s.
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Toews M, Wells WM. Efficient and robust model-to-image alignment using 3D scale-invariant features. Med Image Anal 2013; 17:271-82. [PMID: 23265799 PMCID: PMC3606671 DOI: 10.1016/j.media.2012.11.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2012] [Revised: 10/23/2012] [Accepted: 11/06/2012] [Indexed: 11/19/2022]
Abstract
This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down.
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Affiliation(s)
- Matthew Toews
- Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - William M. Wells
- Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Building 32 32 Vassar Street Cambridge, MA 02139, USA
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14
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Automatic segmentation of ventricular cerebrospinal fluid from ischemic stroke CT images. Neuroinformatics 2012; 10:159-72. [PMID: 22125015 DOI: 10.1007/s12021-011-9135-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Accurate segmentation of ventricular cerebrospinal fluid (CSF) regions in stroke CT images is important in assessing stroke patients. Manual segmentation is subjective, time consuming and error prone. There are currently no methods dedicated to extracting ventricular CSF regions in stroke CT images. 102 ischemic stroke CT scans (slice thickness between 3 and 6 mm, voxel size in the axial plane between 0.390 and 0.498 mm) were acquired. An automated template-based algorithm is proposed to extract ventricular CSF regions which accounts for the presence of ischemic infarct regions, image noise, and variations in orientation. First, template VT(2) is registered to the scan using landmark-based piecewise linear scaling and then template VT(1) is used to further refine the registration by partial segmentation of the fourth ventricle. A region of interest (ROI) is found using the registered VT(2). Automated thresholding is then applied to the ROI and the artifacts are removed in the final phase. Sensitivity, dice similarity coefficient, volume error, conformity and sensibility of segmentation results were 0.74 ± 0.12, 0.8 ± 0.09, 0.16 ± 0.11, 0.45 ± 0.39, 0.88 ± 0.09, respectively. The processing time for a 512 × 512 × 30 CT scan takes less than 30 s on a 2.49 GHz dual core processor PC with 4 GB RAM. Experiments with clinical stroke CT scans showed that the proposed algorithm can generate acceptable results in the presence of noise, size variations and orientation differences of ventricular systems and in the presence of ischemic infarcts.
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15
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Nowinski WL, Chua BC, Yang GL, Qian GY. Three-dimensional interactive and stereotactic human brain atlas of white matter tracts. Neuroinformatics 2012; 10:33-55. [PMID: 21505883 DOI: 10.1007/s12021-011-9118-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a human brain atlas of white matter (WM) tracts containing 40 major tracts, which is three-dimensional (3D), segmented, labeled, interactive, stereotactic and correlated to structure and vasculature. We consider: (1) WM accuracy by correlating WM tracts to underlying neuroanatomy and quantifying them; (2) balance between realism and completeness by processing a sequence of track volumes generated for various parameters with the increasing track number to enable a tract "shape convergence". MPRAGE and DTI in 64 directions of the same subject were acquired on 3 Tesla. The method has three steps: DTI-MPRAGE registration, 3D tract generation from DTI, to WM reconstruction from MPRAGE to parcellation into 17 components. 82 track volumes were generated for a wide spectrum of parameter values: Fractional Anisotropy threshold in [0.0125, 0.55] and trajectory angle lower than 45°, 60°, 65°, 70°, 75°, 80°, 85°, 90°. For each tract, a sequence of track volumes was processed to create/edit contours delineating this tract to achieve its shape convergence. The parcellated tracts were grouped into commissures, associations, projections and posterior fossa tracts, and labeled following Terminologia Anatomica. To facilitate that, a dedicated tract editor is developed which processes multiple track volumes, handles tracts in three representations (tracks, contours, envelopes); provides editing/visualization simultaneously on axial, coronal, sagittal planes; enables tract labeling and coloring; and provides numerous tools (track counting, smoothing and length thresholding; representation conversion and saving; structural atlas support). A stereotactic tract atlas along with parcellated WM was developed to explore in real-time any individual tract or their groups along with surrounding neuroanatomy.
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Affiliation(s)
- Wieslaw L Nowinski
- Biomedical Imaging Lab, Agency for Science Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore.
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Volkau I, Puspitasari F, Ng TT, Bhanu Prakash KN, Gupta V, Nowinski WL. A Simple and Fast Method of 3D Registration and Statistical Landmark Localization for Sparse Multi-Modal/Time-Series Neuroimages Based on Cortex Ellipse Fitting. Neuroradiol J 2012; 25:98-111. [PMID: 24028883 DOI: 10.1177/197140091202500114] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2011] [Accepted: 12/24/2011] [Indexed: 11/16/2022] Open
Abstract
Existing methods of neuroimage registration typically require high quality scans and are time-consuming. We propose a simple and fast method which allows intra-patient multi-modal and time-series neuroimage registration as well as landmark identification (including commissures and superior/inferior brain landmarks) for sparse data. The method is based on elliptical approximation of the brain cortical surface in the vicinity of the midsagittal plane (MSP). Scan registration is performed by a 3D affine transformation based on parameters of the cortex elliptical fit and by aligning the MSPs. The landmarks are computed using a statistical localization method based on analysis of 53 structural scans without detectable pathology. The method is illustrated for multi-modal registration, analysis of hemorrhagic stroke time series, and ischemic stroke follow ups, as well as for localization of hardly visible or not discernible landmarks in sparse neuroimages. The method also enables a statistical localization of landmarks in sparse morphological/non-morphological images, where landmark points may be invisible.
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Affiliation(s)
- I Volkau
- Biomedical Imaging Laboratory, Agency for Science Technology and Research; Singapore -
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Use of normative distribution of gray to white matter ratio in orthogonal planes in human brain studies and computer-assisted neuroradiology. Int J Comput Assist Radiol Surg 2010; 6:489-505. [PMID: 21161415 DOI: 10.1007/s11548-010-0538-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2010] [Accepted: 10/26/2010] [Indexed: 10/18/2022]
Abstract
PURPOSE Although the brain has been extensively studied, relationships of gray (GM) to white (WM) matters in individual sections as typically acquired and read radiologically have not yet been examined. A novel GM/WM-based approach with a compact whole brain representation is introduced and applied to study the brain and perform neuroimage processing. METHODS The gray to white matter ratio GWR defined as GM/(GM+WM) was calculated for 3T T1-weighted axial, coronal, and sagittal sections of 75 normal subjects. The mean (normative) GWR curves were employed to describe the normal brain and quantify aging and to illustrate pathology detection and characterization. RESULTS The mean GWR curves characterize the normal brain by only six, neuroanatomy-related numbers. The regions with a significant GWR decline with age surround the ventricular system. The GWR decline rate in males is higher (-0.17%/year) than females (-0.14%/year); moreover, males show a significantly higher decline in middle to elder group. The GWR decline from young (≤25 years) to middle (26-40 years) age group (males/females -0.31%/-0.34%/year) is significantly higher than that from middle to elder (>40 years) group (males/females -0.13/-0.07%/year). CONCLUSION The GWR-based analysis is useful to characterize normal brain, determine significant regions of interest, and quantify healthy aging. It has potential applications in brain compression, comparison, morphometry, normalization, and detecting and quantifying pathologies, which open new avenues in computer-assisted neuroradiology from screening to large brain databases searching.
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Automatic model-guided segmentation of the human brain ventricular system from CT images. Acad Radiol 2010; 17:718-26. [PMID: 20457415 DOI: 10.1016/j.acra.2010.02.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2009] [Revised: 02/10/2010] [Accepted: 02/19/2010] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size variations of the ventricular system, no automatic method is presently available. The authors propose a model-guided method for the automated segmentation of the ventricular system. MATERIALS AND METHODS Fifty CT scans of patients with strokes at different sites were collected for this study. Given a brain CT image, its ventricular system was segmented in five steps: (1) a predefined volumetric model was registered (or deformed) onto the image; (2) according to the deformed model, eight regions of interest were automatically specified; (3) the intensity threshold of cerebrospinal fluid was calculated in a region of interest and used to segment all regions of cerebrospinal fluid from the entire brain volume; (4) each ventricle was segmented in its specified region of interest; and (5) intraventricular calcification regions were identified to refine the ventricular segmentation. RESULTS Compared to ground truths provided by experts, the segmentation results of this method achieved an average overlap ratio of 85% for the entire ventricular system. On a desktop personal computer with a dual-core central processing unit running at 2.13 GHz, about 10 seconds were required to analyze each data set. CONCLUSION Experiments with clinical CT images showed that the proposed method can generate acceptable results in the presence of image noise, large shape, and size variations of the ventricular system, and therefore it is potentially useful for the quantitative interpretation of CT images in neurodiagnosis and neurosurgery.
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Fu Y, Gao W, Xiao Y, Liu J. A framework for automatic construction of 3D PDM from segmented volumetric neuroradiological data sets. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2010; 97:199-210. [PMID: 19631401 DOI: 10.1016/j.cmpb.2009.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2008] [Revised: 10/24/2008] [Accepted: 06/18/2009] [Indexed: 05/28/2023]
Abstract
3D point distribution model (PDM) of subcortical structures can be applied in medical image analysis by providing priori-knowledge. However, accurate shape representation and point correspondence are still challenging for building 3D PDM. This paper presents a novel framework for the automated construction of 3D PDMs from a set of segmented volumetric images. First, a template shape is generated according to the spatial overlap. Then the corresponding landmarks among shapes are automatically identified by a novel hierarchical global-to-local approach, which combines iterative closest point based global registration and active surface model based local deformation to transform the template shape to all other shapes. Finally, a 3D PDM is constructed. Experiment results on four subcortical structures show that the proposed method is able to construct 3D PDMs with a high quality in compactness, generalization and specificity, and more efficient and effective than the state-of-art methods such as MDL and SPHARM.
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Affiliation(s)
- Yili Fu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150080 Harbin, Heilongjiang, China
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Puspitasari F, Volkau I, Ambrosius W, Nowinski WL. Robust calculation of the midsagittal plane in CT scans using the Kullback-Leibler's measure. Int J Comput Assist Radiol Surg 2009; 4:535-47. [PMID: 20033330 DOI: 10.1007/s11548-009-0366-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2008] [Accepted: 05/14/2009] [Indexed: 10/20/2022]
Abstract
OBJECTIVE The identification of the interhemispheric fissure (IF) is important in clinical applications for brain landmark identification, registration, symmetry assessment, and pathology detection. The IF is usually approximated by the midsagittal plane (MSP) separating the brain into two hemispheres. We present a fast accurate, automatic, and robust algorithm for finding the MSP for CT scans acquired in emergency room (ER) with a large slice thickness, high partial volume effect, and substantial head tilt. MATERIALS AND METHODS An earlier algorithm for MSP identification from MRI using the Kullback-Leibler's measure was extended for CT by estimating patient's head orientation using model fitting, image processing, and atlas-based techniques. The new algorithm was validated on 208 clinical scans acquired mainly in the ER with slice thickness ranging from 1.5 to 6 mm and severe head tilt. RESULTS The algorithm worked robustly for all 208 cases. An angular discrepancy (degrees) and maximum distance (mm) between the calculated MSP and ground truth have the mean value (SD) 0.0258 degrees (0.9541 degrees) and 0.1472 (0.7373) mm, respectively. In average, the algorithm takes 10 s to process of a typical CT case. CONCLUSION The proposed algorithm is robust to head rotation, and correctly identifies the MSP for a standard clinical CT scan with a large slice thickness. It has been applied in our several CT stroke CAD systems.
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Affiliation(s)
- Fiftarina Puspitasari
- Biomedical Imaging Lab, Agency for Science, Technology and Research, Matrix, Singapore, Singapore.
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Sim K, Yang GL, Loh D, Poon LY, Sitoh YY, Verma S, Keefe R, Collinson S, Chong SA, Heckers S, Nowinski W, Pantelis C. White matter abnormalities and neurocognitive deficits associated with the passivity phenomenon in schizophrenia: a diffusion tensor imaging study. Psychiatry Res 2009; 172:121-7. [PMID: 19297135 DOI: 10.1016/j.pscychresns.2009.02.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2008] [Revised: 02/04/2009] [Accepted: 02/10/2009] [Indexed: 11/19/2022]
Abstract
The passivity phenomenon is a distressing Schneiderian first rank symptom in patients with schizophrenia. Based on extant data of functional and structural cerebral changes underlying passivity, we sought to examine cerebral white matter integrity in our subjects. We hypothesised that the passivity phenomenon would be associated with white matter changes in specific cortical (frontal, parietal cortices, and cingulate gyrus) and subcortical regions (thalamus and basal ganglia) and correlated with relevant neurocognitive deficits, compared with characteristics in those without the passivity phenomenon. Thirty-six subjects (11 with passivity and 25 without passivity) with schizophrenia were compared with 32 age-, gender- and handedness-matched healthy controls using diffusion tensor imaging. Neuropsychological testing was administered. Patients with passivity were associated with increased fractional anisotropy within the frontal cortex, cingulate gyrus, and basal ganglia and decreased fractional anisotropy within the thalamus when compared with patients without passivity. Within patients with passivity, fractional anisotropy in the frontal cortex correlated with the age of onset of illness and neurocognitive deficits related to attention and executive functioning. The findings suggest distributed involvement of cortical and subcortical regions underlying passivity and support the notion of neural network models underlying specific psychiatric symptoms such as passivity.
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Affiliation(s)
- Kang Sim
- Department of General Psychiatry, Woodbridge Hospital, Institute of Mental Health, 10, Buangkok View, 539747 Singapore.
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Towards construction of an ideal stereotactic brain atlas. Acta Neurochir (Wien) 2008; 150:1-13; discussion 13-4. [PMID: 18030414 DOI: 10.1007/s00701-007-1270-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2006] [Accepted: 04/24/2007] [Indexed: 10/22/2022]
Abstract
BACKGROUND The role of the brain atlas is changing in many aspects with the advancements in stereotactic and functional neurosurgery. Therefore, there is a critical need to construct a new atlas. This paper addresses the definition and construction of an atlas, ideal (in our opinion) for stereotactic and functional neurosurgery. The essence of the new atlas is not only its population-based structural and functional content, but also its continuous "self-updatability" with the new clinical results obtained. METHOD The ideal atlas defined here contains four major components: brain models, knowledge database, tools, and clinical results. Towards its creation, a multi-atlas is proposed. The construction of the initial version of the multi-atlas is detailed with the probabilistic functional atlas (PFA), interpolated Talairach-Tournoux atlas, and enhanced Schaltenbrand-Wahren atlas. These atlases are put in a spatial register by matching their AC-PC distances and heights of the thalamus; the Schaltenbrand coronal and sagittal microseries are scaled laterally to match the target structure centroids with the locations of the best targets of the PFA. FINDINGS Construction of an initial version of the ideal stereotactic atlas is feasible at present from the available resources. To achieve that, our three atlases (PFA, Talairach and Schaltenbrand) are enhanced and combined together. A single lateral scaling factor per target structure is feasible to co-register the Schaltenbrand atlas with PFA in four situations (compensated against the third ventricle, non-compensated, bilateral, and non-bilateral). The STN has to be stretched by 18% more than the VIM on the Schaltenbrand coronal microseries, and the VIM has to be compressed by 13% less than the STN on the Schaltenbrand sagittal microseries. CONCLUSION The new multi-atlas can potentially be more useful than the currently employed atlases and will facilitate further development of the ideal atlas for stereotactic and functional neurosurgery.
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Nowinski WL. From research to clinical practice: lessons learnt from the Cerefy brain atlases. Int J Comput Assist Radiol Surg 2007. [DOI: 10.1007/s11548-007-0132-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Mac Donald CL, Dikranian K, Song SK, Bayly PV, Holtzman DM, Brody DL. Detection of traumatic axonal injury with diffusion tensor imaging in a mouse model of traumatic brain injury. Exp Neurol 2007; 205:116-31. [PMID: 17368446 PMCID: PMC1995439 DOI: 10.1016/j.expneurol.2007.01.035] [Citation(s) in RCA: 225] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2006] [Revised: 01/22/2007] [Accepted: 01/23/2007] [Indexed: 12/31/2022]
Abstract
Traumatic axonal injury (TAI) is thought to be a major contributor to cognitive dysfunction following traumatic brain injury (TBI), however TAI is difficult to diagnose or characterize non-invasively. Diffusion tensor imaging (DTI) has shown promise in detecting TAI, but direct comparison to histologically-confirmed axonal injury has not been performed. In the current study, mice were imaged with DTI, subjected to a moderate cortical controlled impact injury, and re-imaged 4-6 h and 24 h post-injury. Axonal injury was detected by amyloid beta precursor protein (APP) and neurofilament immunohistochemistry in pericontusional white matter tracts. The severity of axonal injury was quantified using stereological methods from APP stained histological sections. Two DTI parameters--axial diffusivity and relative anisotropy--were significantly reduced in the injured, pericontusional corpus callosum and external capsule, while no significant changes were seen with conventional MRI in these regions. The contusion was easily detectable on all MRI sequences. Significant correlations were found between changes in relative anisotropy and the density of APP stained axons across mice and across subregions spanning the spatial gradient of injury. The predictive value of DTI was tested using a region with DTI changes (hippocampal commissure) and a region without DTI changes (anterior commissure). Consistent with DTI predictions, there was histological detection of axonal injury in the hippocampal commissure and none in the anterior commissure. These results demonstrate that DTI is able to detect axonal injury, and support the hypothesis that DTI may be more sensitive than conventional imaging methods for this purpose.
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Affiliation(s)
- C L Mac Donald
- Department of Biomedical Engineering, Washington University, One Brookings Drive, Campus Box 1097, St. Louis, MO 63110, USA
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Nowinski WL, Qian G, Kirgaval Nagaraja BP, Thirunavuukarasuu A, Hu Q, Ivanov N, Parimal AS, Runge VM, Beauchamp NJ. Analysis of ischemic stroke MR images by means of brain atlases of anatomy and blood supply territories. Acad Radiol 2006; 13:1025-34. [PMID: 16843856 DOI: 10.1016/j.acra.2006.05.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2006] [Revised: 05/24/2006] [Accepted: 05/25/2006] [Indexed: 10/24/2022]
Abstract
RATIONALE AND OBJECTIVES A method for atlas-assisted analysis of stroke magnetic resonance images that is a part of a stroke computer-assisted diagnosis system supporting rapid and quantitative checking of thrombolysis conditions is presented. MATERIALS AND METHODS Two brain atlases are used for analysis: atlas of anatomy (AA) and atlas of blood supply territories (BSTs). To map these atlases onto scans, two methods are used at present: (1) fast Talairach transformation and (2) midsagittal plane and brain's bounding box matching. After atlas-to-scan mapping, both atlases are superimposed onto the studied images and can be used to get their underlying anatomy and BSTs. To speed up the process of analysis, the system automatically analyzes entire regions occupied by the infarct and penumbra. RESULTS By using both atlases, the system calculates the following values for each infarct and penumbra region: (1) names of all anatomic structures and BSTs within the region, (2) volumes of occupancy for each structure and territory, and (3) percentages of occupancy for each structure and territory. In addition, the system calculates the infarct-middle cerebral artery (MCA) territory ratio for diffusion-weighted images and the penumbra-MCA territory ratio for perfusion images. Atlas-assisted analysis is fast, and calculations take less than 10 seconds. CONCLUSION This method potentially facilitates and speeds up stroke data analysis, as well as supports decision making.
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Affiliation(s)
- Wieslaw L Nowinski
- Biomedical Imaging Lab, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore 138671.
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Hu Q, Nowinski WL. Radiological Symmetry of Brain and Head Images: Comparison and Applications. Int J Comput Assist Radiol Surg 2006. [DOI: 10.1007/s11548-006-0039-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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