Literature DB >> 19147886

Survey of the visual exploration and analysis of perfusion data.

Bernhard Preim1, Steffen Oeltze, Matej Mlejnek, Eduard Gröeller, Anja Hennemuth, Sarah Behrens.   

Abstract

Dynamic contrast-enhanced image data (perfusion data) are used to characterize regional tissue perfusion. Perfusion data consist of a sequence of images, acquired after a contrast agent bolus is applied. Perfusion data are used for diagnostic purposes in oncology, ischemic stroke assessment or myocardial ischemia. The diagnostic evaluation of perfusion data is challenging, since the data is complex and exhibits various artifacts, e.g., motion artifacts. We provide an overview on existing methods to analyze, and visualize CT and MR perfusion data. The integrated visualization of several 2D parameter maps, the 3D visualization of parameter volumes and exploration techniques are discussed. An essential aspect in the diagnosis of perfusion data is the correlation between perfusion data and derived time-intensity curves as well as with other image data, in particular with high resolution morphologic image data. We discuss visualization support with respect to the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis and the diagnosis of coronary heart disease.

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Year:  2009        PMID: 19147886     DOI: 10.1109/TVCG.2008.95

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  2 in total

1.  Dynamic infrared thermography (DIRT) for assessment of skin blood perfusion in cranioplasty: a proof of concept for qualitative comparison with the standard indocyanine green video angiography (ICGA).

Authors:  P Rathmann; C Chalopin; D Halama; P Giri; J Meixensberger; D Lindner
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-11-15       Impact factor: 2.924

2.  Intracoronary Transplantation of Mesenchymal Stem Cells with Overexpressed Integrin-Linked Kinase Improves Cardiac Function in Porcine Myocardial Infarction.

Authors:  Dan Mu; Xin-Lin Zhang; Jun Xie; Hui-Hua Yuan; Kun Wang; Wei Huang; Guan-Nan Li; Jian-Rong Lu; Li-Juan Mao; Lian Wang; Le Cheng; Xiao-Li Mai; Jun Yang; Chuan-Shuai Tian; Li-Na Kang; Rong Gu; Bin Zhu; Biao Xu
Journal:  Sci Rep       Date:  2016-01-11       Impact factor: 4.379

  2 in total

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