การวิเคราะห์พื้นผิวโดยใช้เมทริกซ์การเกิดร่วมระดับสีเทาสำหรับการประเมินคุณภาพของภาพสมองระหว่างเครื่องเอกซเรย์คอมพิวเตอร์สองรุ่น

Sermsak  Saengphet; Sukanya  Muangjai; Thatsani  Bunmala; Saranya  Thongsawang; Rujikan  Chaisanam; Pichan Kaewpookum

Authors

Sermsak Saengphet Radiology Department, Samitivej Srinakarin Hospital, Suan Luang, Bangkok, 10250, Thailand
Sukanya Muangjai Radiology Department, Praram 9 Hospital, Huai Khwang, Bangkok, 10310, Thailand
Thatsani Bunmala Radiology Department, Ratchaburi Hospital, Mueang Ratchaburi, Ratchaburi, 70120, Thailand
Saranya Thongsawang Radiology Department, Phra Nakhon Si Ayutthaya Hospital, Mueang Phra Nakhon Si Ayutthaya, Phra Nakhon Si Ayutthaya, 13000, Thailand
Rujikan Chaisanam Department of Radiological Technology, Faculty of Science Ramkhamhaeng University, Bangkok, 10240, Thailand
Pichan Kaewpookum Department of Radiological Technology, Faculty of Science Ramkhamhaeng University, Bangkok, 10240, Thailand

Keywords:

CT Brain, Texture Analysis, Radiation Dose, Grey-level co-occurrence matrix, image quality, UltraiQ, ultrasound

Abstract

Background: The grey-level co-occurrence matrix (GLCM) technique is a widely used texture analysis method for quantitatively assessing CT image quality. Objective: This study aimed to evaluate the image quality of brain computed tomography (CT) scans obtained from two different Multi-Detector CT scanner models from different manufacturers using quantitative texture analysis to determine the optimal tube current (mA) for the experimental scanner achieving image quality equivalent to the standard scanner. Methods: This retrospective analytical study involved 146 patients. Images from the standard scanner (120 kV, 300 mA) were compared with images from the experimental scanner (120 kV, mA adjusted to 280, 300, 310, and 315, IR-B reconstruction). Grey-level co-occurrence matrix (GLCM) texture analysis was performed on regions of interest (ROI) in the skull base, brainstem, and parietal lobe. Mean, Contrast, Homogeneity, and Entropy were calculated for quantitative comparison. Results: The calculated texture feature values ranged from Mean Gray Level (66.24–183.25), Contrast (53.17–93.25), Homogeneity (0.14–0.24), and Entropy (4.24–6.48). Images from the experimental scanner set at 120 kV and 310 mA provided texture analysis values most similar to the standard images. Conclusion: The image quality of the CT scans from the experimental scanner optimized at 310 mA (using IR-B) is comparable to that of the standard scanner (300 mA). These findings establish the optimal tube current setting to maintain equivalent image quality, which is beneficial for routine image Quality Assurance (QA) across the hospital network.

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