The cumulative effect of the exposure to CT imaging compounds the need to achieve the lowest per-scan patient dose possible. While low-dose CT imaging techniques exist, there is often a compromise that must be made in both image quality and cost. Lower-dose studies have higher image noise, which results in lower quality images, which are more difficult to analyze. Iterative Reconstruction (IR) is still the most used technique for improving the quality of lower-dose imaging studies. However, IR can take substantially longer to process images and is prone to producing images with a waxy or blurry appearance if applied too strongly on low-dose studies. Deep Learning CT Processing (DLCP) is the next generation in CT image noise reduction techniques. DLCP not only improves CT images acquired during typically low-dose procedures like lung screening and pediatric exams, but it also presents the ability to significantly reduce radiation exposure for other higher-risk categories such as oncology and obese patients. In addition, it can improve the image quality of ultra-low-dose abdominal, cardiac, and brain scans. Radailogy presents PixelShine by AlgoMedica in order to assist to the reduction of CT radiation dose and from now on to substantially improve image quality in low dose CT studies.

Doctors from all over the world have recently been able to upload X-ray examinations or other medical documents to and have them examined with the help of artificial intelligence (AI). Either as an initial finding or as a second opinion to confirm or expand their own diagnosis. “What we are doing with Radailogy is a world first,” explains Gerd Schueller, founder of the Zug-based company Emergency Radiology Schueller, which launched the platform in October (see article Wirtschaft/Zuger Zeitung 29.12.2020).