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Proceedings Paper

A new interactive visual-aided decision-making supporting tool to predict severity of 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 interactive decision-making support tool to predict severity of AIS prior to thrombectomy using CT perfusion imaging protocol. CT image data of 30 AIS patients with LVO assessed radiologically for their eligibility to undergo mechanical thrombectomy were retrospectively collected and analyzed in this study. First, a computer-aided scheme automatically categorizes images into multiple sequences followed by indexing each slice to specified brain location. Next, consecutive mapping is used for accurate brain region segmentation from skull. The brain is then split into left and right hemispheres, followed by detecting blood in each hemisphere. Additionally, visual tools including segmentation, blood correction, select sequence and index analyzer are implemented for deeper analysis. Last, comparison between blood-volume in each hemisphere over the sequences is made to observe wash-in and wash-out rate of blood flow to assess the extent of damaged and “at risk” brain tissue. By integrating computer-aided 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 extent of AIS.

Paper Details

Date Published: 28 February 2020
PDF: 9 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113171V (28 February 2020); doi: 10.1117/12.2549614
Show Author Affiliations
Gopichandh Danala, The Univ. of Oklahoma (United States)
Sai Kiran Reddy Maryada, The Univ. of Oklahoma (United States)
Morteza Heidari, The Univ. of Oklahoma (United States)
Bappaditya Ray, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Masoom Desai, The Univ. of Oklahoma Health Sciences Ctr. (United States)
Bin Zheng, The Univ. of Oklahoma (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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