GE Healthcare has launched the Omni PET/CT platform and Omni Legend system at the annual meeting of the European Association of Nuclear Medicine (EANM) in Barcelona, Spain.
The product contains a novel type of digital bismuth germanate (dBGO) detector material with a small crystal size that delivers more than two times the sensitivity of prior digital scanners, enabling faster total scan times and improved small lesion detectability, according to the vendor.
GE states that Omni Legend 32 cm increases small lesion detectability by 16% on average and up to 20%, as compared with the Discovery MI 25 cm with matched scan time/injected dose, as demonstrated in phantom testing using a model observer with 4 mm lesions.
The system uses an artificial intelligence (AI)-based Auto Positioning Camera and Precision DL, a deep-learning image processing software, and offers the capability to scan beyond FDG with short-life tracers for cardiac and neuroimaging that can enable different procedures such as the diagnostics portion of theranostics imaging, the company added.
Also included in the scanner is the Q.Clear PET image reconstruction software and MotionFree, GE's respiratory motion correction technology. Q.Clear reportedly helps to ensure reliable quantification, while MotionFree can correct respiratory motion artifacts for all patient types, the company adds.
Other notable features are a fast data quality assurance process for streamlined calibration, simplified protocol selection on the gantry touchscreen and a new user interface, and better patient positioning as a result of AI-based Auto Positioning that automatically centers the patient.
Impact on workflow
The launch of the new scanner comes in response to customer concerns over cost management and operational efficiency, GE continued.
At the Rambam Health Care Campus in Haifa, Israel, the Omni Legend has already helped to increase patient throughput by more than a third thanks to the system's shorter scan times -- achieving up to 35 patient scans during a 9.5-hour shift -- and reduce dose by 40% versus the previous equipment that was installed, stated John Kennedy, PhD, chief physicist in the Nuclear Medicine Department.
The 35th annual congress of the EANM runs from 15 to 19 October.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
