Sunday, November 29 | 10:55 a.m.-11:05 a.m. | SSA19-02 | Room S403B
In this talk, researchers will discuss their efforts to compare dose efficiency between an investigational photon-counting CT scanner and conventional CT."CT scanners with photon-counting detectors have a number of theoretical advantages over current CT scanners, which have energy-integrating detectors," Cynthia McCollough, PhD, from the Mayo Clinic in Rochester, MN, told AuntMinnie.com. "One is that photon-counting scanners can reject electronic noise, which negatively affects image quality more and more as dose decreases. The other is that the lower energy photons -- which carry more information about iodine -- are not weighted equally with energy-integrating detectors; there the high-energy photons -- which have little iodine information -- are counted the most strongly."
There is no such weighting with photon-counting detectors, which, in fact, can be used to weight those iodine-information-carrying low-energy photons most strongly.
In their study, Munich-based presenter Ralph Gutjahr from Siemens Healthcare and colleagues from the Mayo Clinic hypothesized that the iodine contrast-to-noise ratio should be higher for the CT detector based on photon counting. A research scanner was installed last year at Mayo as part of a U.S. National Institutes of Health (NIH) bioengineering grant.
"The scanner we have won't be commercialized, but what we learn from it may open the door to a commercial system in the future," McCollough said.
The scanner yielded increased contrast-to-noise values for a given dose, the study team will report.
![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=100&q=70&w=100)
![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)
