News | Computed Tomography (CT) | February 27, 2019

Canon Medical Introduces Deep Learning-Based CT Image Reconstruction

Advanced intelligent Clear IQ Engine delivers high image quality with high spatial resolution

Canon Medical Introduces Deep Learning-Based CT Image Reconstruction

February 27, 2019 — Canon Medical Systems recently introduced AiCE (Advanced intelligent Clear IQ Engine), a deep convolutional neural network (DCNN) image reconstruction technology for computed tomography (CT). AiCE uses deep learning technology to differentiate signal from noise so that it removes noise while it preserves true signal.

With the AiCE deep learning approach, the DCNN is trained in the factory using perfect high-quality target data from real patient datasets. This patient data is extensively processed with advanced model based iterative reconstruction (MBIR), which provides optimal image quality and improved spatial resolution.

Following training and validation, the AiCE DCNN is then implemented into the CT scanner that allows for reconstruction speeds fast enough for busy clinical environments.

Canon Medical is showcasing its AiCE technology at this year’s European Congress of Radiology (ECR), Feb. 27-March 3 in Vienna, Austria.

For more information: www.us.medical.canon

Related Content

Guerbet announced the launch of OptiProtect 3S, a new range of technical services for its injection solutions. OptiProtect 3S is designed to support imaging centers in the daily use and protection of their injection solutions.
News | Contrast Media Injectors | February 25, 2021
February 25, 2021 — Guerbet announced the launch of ...
icobrain cva allows the quantitative assessment of tissue perfusion by reporting the volume of core and perfusion lesion by quantifying Tmax abnormality and CBF abnormality together with the mismatch volume and ratio
News | Artificial Intelligence | February 23, 2021
February 23, 2021 — icometrix, world leader in imaging...
Examples of the imaging performance of XPCI-CT (b,e) compared to conventional specimen radiography (a,d) and benchmarked against histopathology (c,f). he top row focuses on the similarity between the XPCI-CT slice in (b) and the histological slice in (c). Arrow 1 indicates margin involvement, arrow 2 a variation in density in the internal structure of the tumour mass, arrow 3 tumour-induced inflammation. All this is confirmed by the histological slice in (c), and hardly visible in the conventional image in

Examples of the imaging performance of XPCI-CT (b,e) compared to conventional specimen radiography (a,d) and benchmarked against histopathology (c,f). he top row focuses on the similarity between the XPCI-CT slice in (b) and the histological slice in (c). Arrow 1 indicates margin involvement, arrow 2 a variation in density in the internal structure of the tumour mass, arrow 3 tumour-induced inflammation. All this is confirmed by the histological slice in (c), and hardly visible in the conventional image in (a). The bottom row focuses on the detection of small calcifications, a key feature in DCIS. These are undetectable in (d), detected in (e), enhanced in the maximum intensity projection (MIP) image at the bottom of (f), and confirmed by histopathology in the top part of (f). The scale bar [shown in (b) and (e)] is the same for all images apart from (f), which has its own scale. Red arrows in (e) and (f) indicate the microcalcifications. Image courtesy of Professor Alessandro Olivo

News | Breast Imaging | February 22, 2021
February 22, 2021 — A new X-ray imaging scanne
Dr Sahar Saleem placing the mummy in the CT scanner

Dr. Sahar Saleem placing the mummy in the CT scanner. Image courtesy of Sahar Saleem

News | Computed Tomography (CT) | February 22, 2021
February 22, 2021 — Modern medical technology is helping scholars tell a more nuanced story about the fate of an anci
GE Healthcare introduced its artificial intelligence (AI) automation features on its Voluson Swift ultrasound platform at the 2020 Radiological Society of North America (RSNA) virtual meeting. Features of this system include semi-automated contouring, auto identification of fetal anatomy and positioning on imaging. AI is seeing increasing integration in ultrasound systems from numerous vendors.

GE Healthcare introduced its artificial intelligence (AI) automation features on its Voluson Swift ultrasound platform at the 2020 Radiological Society of North America (RSNA) virtual meeting. Features of this system include semi-automated contouring, auto identification of fetal anatomy and positioning on imaging. AI is seeing increasing integration in ultrasound systems from numerous vendors.

Feature | Ultrasound Imaging | February 18, 2021 | By Dave Fornell, Editor
Recent advances in ultrasound image sy...
Example MR images from paediatric brain tumour patients. This first column shows T1-weighted images following the injection of gadolinium contrast agent. The second column shows T2-weighted images and the final column shows apparent diffusion coefficient maps calculated from diffusion-weighted images. (a–c) are taken from a patient with a Pilocytic Astrocytoma, (d–f) are from a patient with an Ependymoma and (g–i) were acquired from a patient with a Medulloblastoma.

Example MR images from paediatric brain tumour patients. This first column shows T1-weighted images following the injection of gadolinium contrast agent. The second column shows T2-weighted images and the final column shows apparent diffusion coefficient maps calculated from diffusion-weighted images. (ac) are taken from a patient with a Pilocytic Astrocytoma, (df) are from a patient with an Ependymoma and (gi) were acquired from a patient with a Medulloblastoma. Image courtesy of Nature Research Journal

News | Pediatric Imaging | February 17, 2021
February 17, 2021 — Diffusio...
The research collaboration agreement covers a joint clinical retrospective study on liver fibrosis severity in Non-Alcoholic Steato-Hepatitis (NASH) patients
News | Artificial Intelligence | February 10, 2021
February 10, 2021 — Median Technologies announced the company has signed a research collaboration agreement with the
Unhealthy lifestyles, various diseases, stress, and aging can all contribute to an imbalance between the production of ROS and the body's ability to reduce and eliminate them. The resulting excessive levels of ROS cause "oxidative stress".

Unhealthy lifestyles, various diseases, stress, and aging can all contribute to an imbalance between the production of ROS and the body's ability to reduce and eliminate them. The resulting excessive levels of ROS cause "oxidative stress". Graphic courtesy of National Institutes for Quantum and Radiological Science and Technology

News | Magnetic Resonance Imaging (MRI) | February 10, 2021
February 10, 2021 — Oxygen is essential for human life, but within the body, certain biological environmental conditi
Comparison of breast cancer mortality rates (red squares) and distant-stage breast cancer incidence rates from SEER9 (blue dots) and SEER18 (green dots) per 100,000 for white women aged, A, 20–39, B, 40–69, and, C, 70–79 years (3,7,8).

Comparison of breast cancer mortality rates (red squares) and distant-stage breast cancer incidence rates from SEER9 (blue dots) and SEER18 (green dots) per 100,000 for white women aged, A, 20–39, B, 40–69, and, C, 70–79 years (3,7,8). Image courtesy of Radiology 

News | Breast Imaging | February 10, 2021
February 10, 2021 — Breast cancer death rates have stopped declining for women in the U.S.