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Advanced intelligent clear iq engine

Manufactured by Canon

The Advanced Intelligent Clear IQ Engine is a core technology developed by Canon for its lab equipment. It is designed to enhance image quality by applying advanced image processing algorithms to optimize clarity, sharpness, and noise reduction in digital images.

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3 protocols using advanced intelligent clear iq engine

1

Three-dimensional Time-of-flight MRA Imaging

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Three-dimensional time-of-flight MRA imaging was performed using a 1.5 T MRI unit (Vantage Orian; Canon Medical Systems). The imaging parameters for obtaining the MRA images were as follows: repetition time, 21 ms; echo time, 6.8 ms; flip angle, 20 degrees; number of averages, 1; field of view, 200 × 200 mm; acquisition matrix, 384 × 208; pixel bandwidth, 122 Hz; pixel size, 0.2604 mm; slice thickness, 1.1 mm; slice interval, 0.55 mm; parallel reduction factor, 3; and receive coil, Atlas Head Neck (16 channel). MRA source images were reconstructed with and without the DLR technique (Advanced Intelligent Clear IQ Engine; Canon Medical Systems) (DLR images and non-DLR images, respectively) [18 (link)].
All the MRA source images were anonymized and exported from the picture archiving and communication system in Digital Imaging and Communications in Medicine format. Maximum intensity projection (MIP) images with coronal and axial views were generated using ImageJ software (https://imagej.nih.gov/ij/) from the source MRA images.
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2

Comprehensive Cardiac CT Angiography Protocol

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All CT was obtained using a 320-MDCT volume scanner (Aquilion ONE PRISM, Canon Medical Systems Corporation). Tube voltage was 100 kVp and automatic tube current modulation was 80–550 mA. The gantry rotation time was 0.28 s; the detector collimation was 130 x 0.5 mm; the field of view was 320 mm; the matrix was 512 x 512; slice thickness was 0.5 mm; and the pitch was 0.813 and with helical scanning. Automatic bolus-tracking program (SUREStart, Canon Medical Systems Corporation) in the aortic arch (trigger threshold was 250 Hounsfield unit [HU]) was used for the scanning. A single bolus of 80 mL iodinated contrast medium (Iobitridol 350 mg, Guerbet, Aulnay-sous-Bois, France) was injected through the antecubital vein with a flow rate of 4.5 mL/s followed by a 30 mL saline with the same flow rate via a dual-head power injector (Stellant, MedRAD Inc., Warrendale, USA). The image was reconstructed with deep learning reconstruction with body sharp option (AiCE, Advanced Intelligent Clear IQ Engine, Canon Medical Systems Corporation). The CT angiography images were sent to a workstation for analysis (Vitrea, Vital, Minneapolis, USA).
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3

Deep Learning for Image Denoising

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The noise reduction in this study was performed using a commercial DLR tool (Advanced Intelligent Clear-IQ Engine, Canon Medical Systems), the details of which have been reported.15 (link)
The DLR has three layers, and the architecture is shown in Fig. 1. The DLR derives 49 components with a fixed 7 × 7 discrete cosine transform (DCT) basis. After the separation of the zero-frequency component of the DCT and the other 48 high-frequency components, soft shrinkage was applied to the later components. In the feature conversion layer, 3 × 3 convolution and soft shrinkage were applied 22 times to the 48 high-frequency components, and the zero-frequency component of the DCT went through a separate collateral pathway. In the final image-generation layer, the high-pass component and the bypassed zero-frequency component were combined to generate denoised images by deconvolution using a 7 × 7 DCT kernel. The main idea underlying this DLR method is the selective processing of the high-frequency component, which is expected to allow noise removal while preserving the image contrast and detailed structure.
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