Multi detector ct scanners

Manufactured by GE Healthcare

Sourced in United States

Multi-detector CT scanners are imaging devices that use multiple X-ray detectors to acquire comprehensive cross-sectional images of the body. They capture detailed, high-resolution data that can be used for a variety of diagnostic and clinical applications.

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3 protocols using multi detector ct scanners

Supine and Prone CT Imaging

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CT image acquisition was obtained in the supine and prone positions using multi-detector CT scanners (GE Healthcare, Waukesha, WI). Scanning technique consisted of 1.25–2.5 mm collimation with a technique of 25 to 100 mAs (or with tube current modulation), 120 KV_p, and 1–2 mm reconstruction interval. Any decubitus series obtained after the standard supine and prone series were not included for analysis in this study.

Patrick J.L., Bakke J.R., Bannas P., Kim D.H., Lubner M.G, & Pickhardt P.J. (2015). Objective Volumetric Comparison of Room Air versus Carbon Dioxide for Colonic Distention at Screening CT Colonography. Abdominal imaging, 40(2), 231-236.

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Multi-Detector CT Imaging Protocol

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All examinations were performed on multi-detector CT scanners (GE Healthcare, USA; United imaging, China). The scanning ranged from the top of the skull to the base. The scanning parameters were: tube voltage 120 kV, tube current 200–360 mA, field of view 320 mm, matrix 512×512, and layer thickness 5 mm.

Xiong X., Wang J., Ke J., Hong R., Jiang S., Ye J, & Hu C. (2023). Radiomics-based intracranial thrombus features on preoperative noncontrast CT predicts successful recanalization of mechanical thrombectomy in acute ischemic stroke. Quantitative Imaging in Medicine and Surgery, 13(2), 682-694.

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Retrospective CT Imaging of Abdominal Patients

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A dataset of 200 patients who underwent an abdominal CT examination between January 2017 and December 2019 was collected retrospectively. The abdominal CTs were performed using multi-detector CT scanners (General Electric, Waukesha, WI, USA) at our institution. Patients were scanned in supine position, head first and with arms over the head. The field of view was adapted to the patient’s body habitus.

Hemke R., Buckless C.G., Tsao A., Wang B, & Torriani M. (2019). Deep learning for automated segmentation of pelvic muscles, fat and bone from CT studies for body composition assessment. Skeletal radiology, 49(3), 387-395.

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