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Proceedings Paper

Non-contrast head CT-assisted cerebral CT perfusion imaging
Author(s): Evan Harvey; Ke Li
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Paper Abstract

Cerebral CT perfusion (CTP) imaging is widely used to guide the selection of acute ischemic stroke patients who are most likely to benefit from endovascular reperfusion therapy, as parametric CTP maps can assist physicians to identify the existence of hypoperfused but salvageable tissue upon reperfusion, and to differentiate this tissue from irreversibly damaged tissue for which reperfusion is futile. CTP is usually performed together with a noncontrast head CT (NCCT) for ruling out intracranial hemorrhage. Even for post stroke treatment follow-up and other neurological diseases, a CTP is often prescribed together with a NCCT to provide more comprehensive and mutually complementary diagnostic information. Despite these clinical utilities, the current CTP imaging technology still faces a major challenge: the poor contrast-to-noise ratio (CNR) of parametric CTP maps that hinders reliable stroke diagnosis for individual patients. Previous works have demonstrated that CNRs of some CTP maps such as cerebral blood volume (CBV) are predominately determined by the baseline CT images, and thus reducing the baseline image noise is very effective in improving the CNRs of CBV maps. The purpose of this work is to present a method that utilizes the high quality NCCT image to reduce the noise of baseline images and improve the detectability of ischemic core in CBV maps. The proposed method was evaluated using an in vivo canine subject where by utilizing the NCCT image the CNR was improved by 55%.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131236 (16 March 2020); doi: 10.1117/12.2549712
Show Author Affiliations
Evan Harvey, Univ. of Wisconsin School of Medicine and Public Health (United States)
Ke Li, Univ. of Wisconsin School of Medicine and Public Health (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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