March 23, 2020 — Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T magnetic resonance imaging (MR) system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medical’s CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction.
AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. Capabilities include:
- High quality images: AiCE allows for the differentiation of true signal from noise through deep learning innovation to match the spatial resolution and low-noise properties of advanced scanning and reconstruction, while maintaining the true structure of the anatomy.
- Seamless integration into routine practice: AiCE is the world’s first fully integrated deep learning reconstruction for MR, and is built directly into the scan protocols for seamless workflow.
- Preferred AiCE MR image quality over non-AI reconstruction images: Following a blinded image comparison of AiCE DLR reconstructed images with images reconstructed with conventional methods without AiCE, physicians stated preference for AiCE images for clarity and resolution.
“AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medical’s leadership and commitment to innovation in diagnostic imaging,” said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. “With the expansion of this unique DLR method across modalities and into MR, we’re elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before.”
For more information: www.us.medical.canon