Virtual monoenergetic images by spectral detector computed tomography may improve image quality and diagnostic ability for ischemic lesions in acute ischemic stroke

Abstract

Background Acute ischemic lesions are challenging to detect by conventional computed tomography (CT). Virtual monoenergetic images may improve detection rates by increased tissue contrast. Purpose To compare the ability to detect ischemic lesions of virtual monoenergetic with conventional images in patients with acute stroke. Material and Methods We included consecutive patients at our center that underwent brain CT in a spectral scanner for suspicion of acute stroke, onset <12 h, with or without (negative controls) a confirmed cortical ischemic lesion in the initial scan or a follow-up CT or magnetic resonance imaging. Attenuation was measured in predefined areas in ischemic gray (guided by follow-up exams), normal gray, and white matter in conventional images and retrieved in spectral diagrams for the same locations in monoenergetic series at 40–200 keV. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Visual assessment of diagnostic measures was performed by independent review by two neuroradiologists blinded to reconstruction details. Results In total, 29 patients were included (January 2018 to July 2019). SNR was higher in virtual monoenergetic compared to conventional images, significantly at 60–150 keV. CNR between ischemic gray and normal white matter was higher in monoenergetic images at 40–70 keV compared to conventional images. Virtual monoenergetic images received higher scores in overall image quality. The sensitivity for diagnosing acute ischemia was 93% and 97%, respectively, for the reviewers, compared to 55% of the original report based on conventional images. Conclusion Virtual monoenergetic reconstructions of spectral CIs may improve image quality and diagnostic ability in stroke assessment.

Metal artifact reduction by virtual monoenergetic reconstructions from spectral brain CT

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Imaging of hypodense gliotic lesions in photon counting computed tomography using virtual monoenergetic images

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