Advanced intelligent Clear-IQ Engine (AiCE)
A new era of clarity has begun
AiCE is the world's first MR Deep Learning reconstruction technology, producting stunning MR images that are exceptionally detailed and with the low-noise properties you might expect of a high SNR image. With AiCE now expanding across a broad range of anatomies, contrast and applications, you no longer need to compromise between resolution and speed.Integrated Intelligence
Sharp, clear and distinct images. See through the noise.
Harnessing the enormous computational power of a Deep Convolutional Neural Network (DCNN), Advanced intelligent Clear-IQ Engine (AiCE) is trained to restore low SNR MR data to match the properties of high SNR images.
AiCE features:
AiCE features:
- Intelligently removes noise
- Results in increased SNR
- Increased Resolution
- Enhanced anatomical detail
Intelligently remove noise with AiCE
3D imaging with AiCE
3D imaging in MR allows you to see images from a different perspective however can generate more noise as there is more data acquisition required. AiCE supports 3D imaging superbly by removing noise from 3D images to maximize your diagnostic opportunities. Now you can see sharp, clear and distinct images at 3D, another big leap forward in MRI.
AiCE can help improve consistency of calculated images
ADC map with AiCE
Quantitative images like ADC can be a very important indicator in disease diagnosis. Noise reduction with AiCE can decrease the standard deviation values, resulting in more reliable quantitative images.
Diffusion Tensor Imaging can visualise white matter fiber tracts by utilising diffusion anisotropy. In images with significant noise, diffusion anisotropy cannot be indicated clearly, however with AiCE the image quality can be improved.
Customer Voice
Deep Learning Reconstruction Customer Experience
Shigeki Aoki, M.D.
Department of Radiology, Juntendo University
Herbert Kressel, M.D.
Harvard Medical School
Shigeki Aoki, M.D.
Department of Radiology, Juntendo University
Herbert Kressel, M.D.
Harvard Medical School
Hear about how two leaders of Radiology in Japan and USA evaluate Canon’s DLR technology:
Professor Aoki:
“I am very much looking forward to denoised images, convenient organ segmentation, and in the future classifying images…”
Professor Kressel:
“I have been very impressed with the images that I see, and in my opinion improving image quality is always useful for diagnosis”
Professor Aoki:
“I am very much looking forward to denoised images, convenient organ segmentation, and in the future classifying images…”
Professor Kressel:
“I have been very impressed with the images that I see, and in my opinion improving image quality is always useful for diagnosis”
Vincent Dousset, M.D., Ph.D. (PU-PH), Thomas Tourdias, M.D., Ph.D.
University of Bordeaux and University Hospital of Bordeaux
University of Bordeaux and University Hospital of Bordeaux
Hear about how the doctors from Bordeaux University Hospital evaluate their experience of Canon’s DLR technology:
Professor Dousset:
“The first application, and this is revolutionary in medical imaging history, is that we no longer have to improve the signal or the spatial resolution because the DLR will allow us to correct afterwards what couldn’t be corrected at the beginning.”
Professor Tourdias:
“We realised that by pushing the 3T machine, adding the DLR and comparing the images, we were able to achieve a similar result to what we could achieve with 7 Tesla.”
Professor Dousset:
“The first application, and this is revolutionary in medical imaging history, is that we no longer have to improve the signal or the spatial resolution because the DLR will allow us to correct afterwards what couldn’t be corrected at the beginning.”
Professor Tourdias:
“We realised that by pushing the 3T machine, adding the DLR and comparing the images, we were able to achieve a similar result to what we could achieve with 7 Tesla.”
Quantitative images like ADC can be a very important indicator in disease diagnosis. Noise reduction with AiCE can decrease the standard deviation values, resulting in more reliable quantitative images.
White papers
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© Canon Medical Systems ANZ Pty Limited.
© Canon Medical Systems ANZ Pty Limited.