Sharp, clear and distinct images. At low dose.

Harnessing the enormous computational power of a Deep Convolutional Neural Network (DCNN), Advanced intelligent Clear-IQ Engine (AiCE) is trained to differentiate signal from noise, so that the algorithm can suppress noise while enhancing signal. Because it is trained with advanced MBIR, it exhibits high spatial resolution. But unlike MBIR, AiCE deep learning reconstruction overcomes the challenges (image appearance and/or reconstruction speed) in clinical adoption.

AiCE deep learning reconstruction features:
  • Our best low-contrast resolution, ever. 1,3
  • Dose neutral industry-leading ultra-high resolution2
  • Improved low-contrast detectability, noise and spatial resolution relative to hybrid iterative reconstruction
  • Image noise texture more similar to FBP compared to MBIR reconstruction3
  • Fast reconstruction
  • Easy workflow

1 1.5mm @ 0.3%, 22 mGy
2 Aquilion Precision, Dose neutral between ultra-high resolution mode with AiCE and normal resolution mode with hybrid iterative reconstruction
3 Aquilion ONE / GENESIS Edition

Redefining the balance of IQ, speed and dose.

Fast reconstruction speed:
  • 3-5x faster than MBIR1
 
High image quality:
  • Improved spatial resolution compared to AIDR 3D
  • Improved low contrast detectability compared to AIDR 3D
  • Image noise appearance more similar to filtered back projection1

1 As compared to MBIR, only applicable to AiCE on Aquilion ONE / GENESIS Edition

Low Contrast Detectability*

Body, Lung and Cardiac
Object SizeCTDIvol
3 mm at 0.3%5.3 mGy
2 mm at 0.3%10.5 mGy
1.5mm at 0.3%22.6 mGy
Scan Parameters
10mm with AiCE Body
Phantom
CTP344, Phantom Labs

*Aquilion ONE / GENESIS Edition

Whitepaper

AiCE Deep Learning Reconstruction:
Bringing the power of Ultra-High Resolution CT to routine imaging



Kirsten Boedeker, PhD, DABR
Senior Manager, Medical Physics
Canon Medical Systems Corporation

Click to read

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Authors: Motonori Akagi et al.
Journal: European Radiology
Published: 11/04/2019
Copyright: European Society of Radiology 2019

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