Monday, November 27 | 12:15 p.m.-12:45 p.m. | QS110-ED-MOA2 | Lakeside, QS Community, Station 2
A Swiss team will describe how it successfully implemented an initiative to standardize CT scanning protocols and optimize radiation dose.There's currently a lack of patient- and indication-based diagnostic reference levels (DRLs) for CT scans, and those that do exist are solely based on anatomy -- such as the abdomen or chest. In addition, there are huge differences among countries, according to Dr. Hugues Brat of Institut de Radiologie de Sion (IRS), Groupe 3R. Furthermore, different diagnoses -- such as a kidney stone and a liver tumor -- don't necessarily require the same image quality and dose level.
IRS, a multicenter private radiology group and teleradiology provider, sought to implement a dose excellence program for several reasons, including the need to standardize CT protocols to guarantee uniform image quality. In addition, the organization's obligation to respect the "as low as reasonably achievable" (ALARA) principles was challenged by the lack of standards for indication- and body mass index-based protocols, Brat said.
"Finally, a quality improvement project defining new patient-based CT protocols without impairing image quality needs a high number of patients scanned in the same way in order to be statistically reliable," Brat said. "This is feasible with standardized protocols in a multicenter group."
Thanks to the dose excellence program, the group can now deliver "the right dose for the right diagnosis" based on clinical indication and patient habitus -- without compromising diagnostic image quality, he said.
"This was achievable with automatic dose data collection software and phantom tests during optimization, as well as a clear road map, leadership, teamwork, regular communication, continuous dose monitoring, commitment, and partnership with stakeholders such as industry and medical physics experts," Brat said.
Learn how to implement your own dose excellence program by visiting this poster in the Lakeside Learning Center.



















![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&fit=crop&h=112&q=70&w=112)

