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Developing a computer-aided image analysis and visualization tool to predict region-specific brain tissue “at risk” for developing acute ischemic stroke
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Paper Abstract

Advent of advanced imaging technology and better neuro-interventional equipment have resulted in timely diagnosis and effective treatment for acute ischemic stroke (AIS) due to large vessel occlusion (LVO). However, objective clinicoradiologic correlate to identify appropriate candidates and their respective clinical outcome is largely unknown. The purpose of the study is to develop and test a new computer-aided detection algorithm to quantify region-specific AIS and “at risk” brain volumes prior to thrombectomy using CT perfusion imaging protocol. Fourteen patients with LVO related AIS and assessed radiologically for their eligibility to undergo mechanical thrombectomy was retrospectively analyzed for the study. First, the scheme automatically categorizes images into multiple series of scans acquired from a section of brain. Each image in series is labeled to a specified brain location. Next, image segmentation is performed to separate brain region from skull. The brain is then split into left and right hemispheres, followed by detecting amount of blood in each hemisphere. Last, comparison between amount of blood in each hemisphere over the series of scans is made to observe the wash-in and wash-out rate of blood to assess the extent of already damaged and “at risk” brain tissue. By integrating the scheme into a user graphic interface, the study builds a unique image feature analysis and visualization tool to observe and quantify the delayed or reduced blood flow (brain “at risk” to develop AIS) in the corresponding hemisphere, which has potential to assist radiologists to quickly visualize and more accurately assess the extent of AIS.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530M (15 March 2019); doi: 10.1117/12.2512850
Show Author Affiliations
Gopichandh Danala, The Univ. of Oklahoma (United States)
Morteza Heidari, The Univ. of Oklahoma (United States)
Faranak Aghaei, The Univ. of Oklahoma (United States)
Bappaditya Ray, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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