Non-contrast head CT-assisted cerebral CT perfusion imaging

Evan Harvey; Ke Li

doi:10.1117/12.2549712

16 March 2020 Non-contrast head CT-assisted cerebral CT perfusion imaging

Evan Harvey, Ke Li

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 1131236 (2020) https://doi.org/10.1117/12.2549712
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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%.

Citation Download Citation

Evan Harvey and Ke Li "Non-contrast head CT-assisted cerebral CT perfusion imaging", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131236 (16 March 2020); https://doi.org/10.1117/12.2549712

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