Building on its advanced image reconstruction technologies, Canon Medical’s recently 510(k)-cleared deep convolutional neural network (DCNN) image reconstruction technology is ushering in a new era for CT. Canon Medical’s Advanced Intelligent Clear-IQ Engine (AiCE) uses a deep learning algorithm to differentiate signal from noise so that it can suppress noise while enhancing signal. The algorithm forges a new frontier for CT image reconstruction with its ability to learn from the high image quality of Model Based Iterative Reconstruction (MBIR) to reconstruct CT images with improved spatial resolution and low contrast detectability*.
With AiCE’s deep learning approach, thousands of features learned during training help to differentiate signal from noise for improved resolution. AiCE applies a pre-trained DCNN to enhance spatial resolution while simultaneously reducing noise with reconstruction speeds fast enough for busy clinical environments. More information available here and booth #1933 during RSNA.
* Compared to AIDR 3D
Canon Medical’s Aquilion™ Precision, the first Ultra-High Resolution CT system that includes AiCE Deep Learning Reconstruction and delivers twice the resolution of today’s CT systems, will be showcased at this year’s Radiological Society of North America (RSNA) annual meeting. The system can resolve anatomy as small as 150 microns and 50 lp/cm* is designed to provide more than twice the resolution of today’s CT systems. The Aquilion Precision CT system also features detector channels that are only 0.25 mm thick. This, combined with substantial improvements in scintillator quantum efficiency, detector circuitry and other DAS components, results in a dose-efficient detector with ultra-high resolution capabilities. More information available here and booth #1933 during RSNA.
* Reference value, MTF 0%