Belgian imaging software developer Qaelum has launched a new module of Dose - Enterprise Protocol Management at RSNA 2019 in Chicago. The vendor-neutral module is designed to centrally manage and monitor protocols of CT scanners.
The Enterprise Protocol Management module permits protocol standardization so that users can define protocol structure, key settings of a scanner, contrast details, and a corresponding RadLex code.
The module harmonizes protocols, connecting individual protocols of different scanners to master protocols. Protocols are updated and distributed into the work environment to reach technologists working with individual scanners.
The module offers protocol monitoring whereby protocols are monitored to identify any additions, removals, or alterations in individual scanner protocols. Protocol files can be retrieved and analyzed depending on the local situation. The module also allows for changes in protocols in individual CT scanners during an examination, such that a notification is generated, assisting in the justification of an inappropriate dosage.
Overall usage of the scanner protocols can be assessed, and statistics can be produced on the usage of the protocols. The module also contains predefined rules to pinpoint protocols that have not been employed recently or applied to a different population, such as adult versus pediatric, than the population specified by the master protocol.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
