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Aquilion one 640

Manufactured by Canon
Sourced in Japan

The Aquilion ONE 640 is a computed tomography (CT) scanner manufactured by Canon. It is a 640-slice CT system capable of acquiring high-resolution images in a single rotation. The Aquilion ONE 640 is designed to provide rapid, high-quality imaging for a variety of clinical applications.

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2 protocols using aquilion one 640

1

Preoperative Abdominal Imaging Protocol

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Preoperative abdominal scans were performed with a 320-channel scanner (Aquilion ONE 640; Canon Medical Systems, Otawara, Japan) and a 256-channel scanner (Brilliance 128; Philips Medical Systems, Amsterdam, the Netherlands). The scanning conditions were as follows, tube current 250 mA, tube voltage 120 kV, layer thickness 3 mm, interlayer spacing 3 mm, FOV 500 mm × 500 mm, and matrix 512 × 512. All patients underwent plain scanning followed by enhanced scanning with a contrast agent (iopromide solution: iodine concentration, 300 mg/mL). The total amount of contrasting agent was calculated as 1.5 mL/kg body weight. The elbow vein was injected using a high-pressure syringe at a flow rate of 2.5–3.5 mL/s. The arterial and venous phases were scanned 25–30 s and 60–70 s after contrast injection, respectively.
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2

Detailed CT Imaging Protocol for Tumor Segmentation

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The imaging was conducted using a 320-channel scanner (Aquilion ONE 640; Canon Medical Systems) and a 256-channel scanner (Brilliance 128; Philips Medical Systems). The imaging parameters employed were as follows: tube current ranged from 80 to 230 mA, tube voltage was set at 120 kV, slice thickness was between 1 and 3 mm, field of view (FOV) was 500 mm, and detector pitch spanned 0.75–1.172 mm. Each patient received 80–100 mL of a non-ionic iodinated contrast agent (300 mgI/mL) at a flow rate of 2.5-3.0 mL/s. The average imaging delays stood at 30–40 s for the arterial phase and 65–70 s for the portal venous phase.
To facilitate the training of the segmentation model, a total of 117 arterial phase CT images were manually annotated and employed as the training dataset. To ensure the accuracy and reliability of the manual annotations, we implemented a multi-check method. Each image was independently annotated by two doctors, who would then compare and discuss their annotation results to resolve any discrepancies. Additionally, we employed the Intraclass Correlation Coefficient (ICC) for evaluation (> 0.85). Notably, the annotators remained blinded to the histopathology results during this procedure. The ROI relating to the tumor were manually delineated on each image slice using 3D Slicer (version 4.11) [18 (link)].
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