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International Journal for Multiscale Computational Engineering
Jacob Fish (open in a new tab) Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, New York 10027, USA
J. Tinsley Oden (open in a new tab) Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Somnath Ghosh (open in a new tab) Departments of Civil & Systems Engineering, Mechanical Engineering, and Material Science Engineering, Johns Hopkins University, Baltimore, MD, USA
Arif Masud (open in a new tab) Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 3129E Newmark Civil Engineering Laboratory, MC-250, Urbana, Illinois 61801-2352, USA
Klaus Hackl (open in a new tab) Institute of Mechanics of Materials, Ruhr-University Bochum, Bochum, 44721, Germany
Karel Matous (open in a new tab) Department of Aerospace and Mechanical Engineering, Center for Shock Wave-Processing of Advanced Reactive Materials, University of Notre Dame, Notre Dame, Indiana 46556, USA
Thomas J.R. Hughes (open in a new tab) Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, 201 East 24th Street, C0200, Austin, TX 78712-1229, USA
Caglar Oskay (open in a new tab) Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA
Tamar Schlick (open in a new tab) Department of Chemistry, New York University, New York, New York 10003, USA; Courant Institute of Mathematical Sciences, New York University, New York, New York, 10012, USA; NYU-ECNU Center for Computational Chemistry, NYU Shanghai, China
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.4 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.3 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 2.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00034 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

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MACROSCOPIC MUSCULAR MODELING BASED ON IN VIVO 4D RADIOLOGY

pages 131-142
DOI: 10.1615/IntJMultCompEng.2011002392
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RÉSUMÉ

Because of muscular deformation and movement, standard radiology provides only a snapshot of a probably never recurring situation. The scope of this project is dynamic rendering of muscular structures, starting from 4D radiology, namely, 3D plus time, to macroscopic visualization and simulation based thereon. As full realtime 4D MRI is still beyond the technical possibilities for most human muscles, we follow kind of multilevel approach. The first step is the analysis of muscular tissue of cadaveric preparations where validation can be performed by direct comparison. Second, nearly static, but living muscular tissue is studied based on standard 3D MRI. The first step toward time dependency is an ex post composed series of static MRIs where the muscle goes back to relaxed position between the acquisition steps. This is followed by so-called “quasi-continuous” acquisition where, although not in real time, the muscle does not go back to its original state, but however remains in a stretched position during acquisition. The final goal is full real-time data acquisition. The radiological acquisition is followed by highly detailed image processing, segmentation, and visualization where the deforming muscular tissue is subjected to direct volume rendering with special transfer functions. The applied methods are demonstrated for the flexion of a human ankle joint and deforming human upper arm musculature. The visualization techniques proved to be well suited for capturing dynamics, but additional radiological research is strongly needed. The area of application of 4D modeling ranges from biomechanics to medical diagnosis and therapy of muscular disorders.

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