Harnessing the enormous computational power of a Deep Convolutional Neural Network (DCNN), AiCE Deep Learning Reconstruction (DLR) 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 DLR overcomes the challenges (image appearance and/or reconstruction speed) in clinical adoption.
AiCE DLR features:
Listen to Eliot Siegel, MD, FSIIM, FACR, Professor and Vice Chair Research Informatics, University of Maryland School of Medicine, Chief of Radiology and Nuclear Medicine, Veterans Affairs Maryland Healthcare System
Body, Lung and Cardiac
**Aquilion ONE GenesisListen to Andrew D. Smith, MD, PhD,Vice Chair of Clinical Research, Chief of Abdominal Imaging, Department of Radiology, The University of Alabama at Birmingham, explain the real-world applications of Artificial Intelligence.
Volumetric cardiac CTA was performed with AiCE cardiac. It demonstrates proximal LAD stent and mixed plaques in the RCA.
Listen to Jeannie Yu MD, FACC, FSCCT discuss AiCE DLR, the next level in image reconstruction and resolution recovery.
Chest, abdomen and pelvis CT with iodinated contrast scanned and reconstructed with AiCE Body.
Our partnership with NVIDIA® has allowed Canon Medical Systems to make significant advances in artificial intelligence (AI) and data-intensive deep learning techniques in the healthcare sector. The AiCE Server enables the processing of large volumes of medical data while simultaneously coordinating a variety of patient information.