Evaluation of the Image Quality of Ultra-Low-Dose Paranasal Sinus Computed Tomography Scans
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2023
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Abstract
Objective: We aimed to investigate the image quality of paranasal sinus computed tomography (CT) scans obtained with the “Advanced intelligent Clear-IQ Engine” (AICE) software and ultra-low dose parameters in patients with prediagnosed rhinitis, sinusitis or nasal septum deviation.
Methods: The first 50 patients (31 men and 19 women, aged 18-70 years) who agreed to participate in our prospectively planned study were included in the study. Imaging of the patients was performed with a 160-slice multidetector CT device Canon Aquilion Prime SP (Canon Medical Systems, Otawara, Japan). Tube voltage of 80 kV and tube current of 40 mA were chosen. The effective radiation dose (ED) was calculated based on the “dose-length product” (DLP) information of each patient. Evaluation of images was made on reformat images made with “AiCE Bone” filter in the axial and coronal planes. All images were double-blindly evaluated by 2 radiologists out of 5 points (5 = very good; 1 = not diagnostic). The visibility of 17 bilateral important anatomical landmarks examined on paranasal sinus CT was scored (0 = not visible; 2 = completely visible). In the evaluation of images, the agreement between radiologists was evaluated with Kappa statistics.
Results: The median ED value of the CT protocol was 0.015±0.001 mSv [range 0.012-0.017 mSv]. The overall image quality of the images in our study was interpreted as 4 (good) or 3 (moderate) for all patients. Inter-research agreement was 92%. Most of the anatomical landmarks evaluated were completely visible. Six (bilateral of three) anatomical landmarks could be partially visible in some patients.
Conclusion: Combining the ultra-low dose CT protocol and the AiCE reconstruction algorithm makes it possible to achieve very low ED values in paranasal sinus CT scans and still obtain sufficient image quality in indications such as septum deviation and sinusitis.
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| Authors | Melih Akşamoğlu;Mehmet Sait Menzilcioğlu; |
| Journal | European Journal of Therapeutics |
| Year | 2023 |
| DOI |
1131
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