- AI
- Molecular Imaging
- CT
- X-Ray
- Ultrasound
- MRI
- Facility Management
- Mammography
Canon's Cartesion Prime is a Digital PET/CT Solution Fit for the Times
Canon's latest entry into the PET/CT market is a digital, air-cooled system that provides customizable solutions for a range of clients.
As the country--and the world--continues to grapple with the ongoing COVID pandemic, the imaging industry is looking to re-find its footing in an evolving landscape where customized solutions are key to success.
Enter Canon Medical's Cartesion Prime digital PET/CT, introduced in 2019, which provides a high-performance, adaptable system for today's evolving imaging technology. Optimizing both workflow and image quality via advanced silicon photomultiplier design with one-to-one coupling, fast time-of-flight and deep learning reconstruction technology, “this advanced technology has led to image quality improvements, while optimizing dose efficiency to reduce patient risk and speeding up acquisition time for improved throughput," said Tim Nicholson, managing director, Molecular Imaging Business Unit, Canon Medical Systems USA.
In an interview with Diagnostic Imaging, Nicholson expanded on the needs of the industry and how Canon is answering the call.
Study Assesses Potential of Seven-Minute AI-Enhanced 3T MRI of the Shoulder
February 20th 2025Researchers found that the use of seven-minute threefold parallel imaging-accelerated deep learning 3T MRI had 89 percent sensitivity for supraspinatus-infraspinatus tendon tears and 93 percent sensitivity for superior labral tears.
Study Explores Impact of Insurance on Treatment and Referrals for Patients with Uterine Fibroids
February 19th 2025Women with uterine fibroids and Medicaid coverage are significantly more likely to be treated with uterine artery embolization than those with commercial insurance, according to newly published research.
Study: AI Boosts Ultrasound AUC for Predicting Thyroid Malignancy Risk by 34 Percent Over TI-RADS
February 17th 2025In a study involving assessment of over 1,000 thyroid nodules, researchers found the machine learning model led to substantial increases in sensitivity and specificity for estimating the risk of thyroid malignancy over traditional TI-RADS and guidelines from the American Thyroid Association.
