8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects

      data-paper

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.

          Abstract

          Design Type(s) feature extraction objective • anatomical image analysis objective • data integration objective
          Measurement Type(s) brain blood vessel
          Technology Type(s) MRI-Based Angiogram
          Factor Type(s) age • ethnic group • sex
          Sample Characteristic(s) Homo sapiens • brain

          Machine-accessible metadata file describing the reported data (ISA-Tab format)

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: not found

          Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.

          This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Microsurgical anatomy of the middle cerebral artery.

            The microsurgical anatomy of the middle cerebral artery (MCA) was defined in 50 cerebral hemispheres. The MCA was divided into four segments: the M1 (sphenoidal) segment coursed posterior and parallel to the sphenoid ridge; the M2 (insular) segment lay on the insula; the M3 (opercular) segment coursed over the frontoparietal and temporal opercula; and the M4 (cortical) segment spread over the cortical surface. The Sylvian fissure was divided into a sphenoidal and an operculoinsular compartment. The M1 segment coursed in the sphenoidal compartment, and the M2 and M3 segments coursed in the operculoinsular compartment. The main trunk of the MCA divided in one of three ways; bifurcation (78% of hemispheres), trifurcation (12%), or division into multiple trunks (10%). The MCA's that bifurcated were divided into three groups: equal bifurcation (18%), inferior trunk dominant (32%), or superior trunk dominant (28%). The MCA territory was divided into 12 areas: orbitofrontal, prefrontal, precentral, central, anterior parietal, posterior parietal, angular, temporo-occipital, posterior temporal, middle temporal, anterior temporal, and temporopolar. The smallest cortical arteries arose at the anterior end and the largest one at the posterior end of the Sylvian fissure. The largest cortical arteries supplied the temporo-occipital and angular areas. The relationship of each of the cortical arteries to a number of external landmarks was reviewed in detail.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

              We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D medical data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.
                Bookmark

                Author and article information

                Contributors
                pauline.mouches@ucalgary.ca
                nils.forkert@ucalgary.ca
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                11 April 2019
                11 April 2019
                2019
                : 6
                : 29
                Affiliations
                [1 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Department of Radiology, , University of Calgary, ; Calgary, Canada
                [2 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Hotchkiss Brain Institute, , University of Calgary, ; Calgary, Canada
                Author information
                http://orcid.org/0000-0003-2556-3224
                Article
                34
                10.1038/s41597-019-0034-5
                6472360
                30975990
                85e921f8-bfed-4c5d-8938-171a465eb1aa
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                History
                : 31 August 2018
                : 5 March 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000038, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada);
                Funded by: FundRef https://doi.org/10.13039/501100001804, Canada Research Chairs (Chaires de recherche du Canada);
                Categories
                Data Descriptor
                Custom metadata
                © The Author(s) 2019

                magnetic resonance imaging,anatomy,diagnostic markers,cardiovascular diseases,brain imaging

                Comments

                Comment on this article