The inclusion of digital bismuth germanate (BGO) detector material with the Omni Legend system reportedly more than doubles the sensitivity of older PET/CT devices, improves scan times, and enhances the detection of small lesions.
Emphasizing enhanced diagnostic capabilities, improved efficiency and artificial intelligence (AI)-enabled features, GE Healthcare introduced the Omni Legend positron emission tomography/computed tomography (PET/CT) platform at the 35th Annual Congress of the European Association of Nuclear Medicine (EANM) in Barcelona, Spain.
GE Healthcare said a key feature of the Omni Legend system is the enhanced sensitivity with the small crystal size of the digital bismuth germanate (BGO) detector. The PET/CT platform reportedly bolsters image quality with Precision DL, a deep neural network that facilitates improved qualitative accuracy and contrast-to-noise ratio.
“Sensitivity and image quality are everything in PET/CT. Omni Legend delivers on both — meeting all our image quality criteria for oncology and providing impressive sensitivity to image high-count tracers for cardiac and neuroimaging, which helps better inform patient diagnoses and monitoring,” noted John Kennedy, Ph.D., a chief physicist in the Nuclear Medicine Department at the Rambam Health Care Campus in northern Israel.
Dr. Kennedy said the Omni Legend PET/CT system enabled his facility to reduce dosing by 40 percent in comparison to previous PET/CT devices and perform up to 35 patient scans in a 9.5-hour shift.
The Omni Legend system also allows hands-free patient positioning with the AI-powered Auto Positioning feature, according to GE Healthcare.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.