The Quantum Resonant Magnetic Analyzer is a Hi-tech innovation project, which is related to medical, bio-informatics, electronic engineering, etc. It is based on quantum medical, and scientifically analyzes the human cell`s weak magnetic field collected by advanced electronic device. The analyzer can work out the customer`s health situation and main problem. According to the checking result, the analyzer can figure out the reasonable treatment recommendation. The quantum resonant magnetic analyzer is the individualized guide of comprehensive healthy consulting and updated healthy sciences, and its characteristics and advantages are comprehensive, non-invasive, practical, simple, quick, economical and easy to popularize. With the deep research and scientific development, the analyzer will make great contributions to human health project, and it has broad developing and applying prospect.
This study was aimed to evaluate the image quality and lacunar lesion detection of thin-slice brain computed tomography (CT) images with different reconstruction algorithms, including filtered back projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) by comparison of routine slice images with FBP reconstruction. Sixty-one patients underwent noncontrast brain CT and images were reconstructed with a routine slice of 5.0 mm by FBP and thin slice of 1.0 mm by IMR, HIR, and FBP algorithms, respectively. Objective analyses included CT attenuation, noise, artifacts index of posterior cranial fossa, and contrast-to-noise ratio (CNR). Subjective analyses were performed according to overall image quality using a 5-point scale [1 (unacceptable) to 5 (excellent)]. In addition, lacunar lesion detection was compared in images with different reconstruction settings among 26 patients with lacunar lesions, with magnetic resonance imaging (MRI) as reference.
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