Thursday, December 5 | 11:20 a.m.-11: 30 a.m. | SSQ04-06 | Room S405AB
Low-dose images reconstructed using advanced iterative reconstruction beat standard-dose chest CT images using a first-generation iterative reconstruction scheme, according to German radiologists who subjected cadavers to multiple scans to determine the lowest possible exposure.Searching for the lowest possible dose is ideally done by scanning the same body region multiple times using different dose levels, but multiple scans raise ethical challenges, leading most investigators to turn to phantoms, according to Dr. Stefan Wirth from Ludwig Maximilian University of Munich.
However, using cadavers is a better choice because they make the best possible phantoms and enable multiple scans to be performed without ethical questions.
"The purpose of this study was to explore the lower end of possible dose rate reduction, while keeping image quality at least stable/diagnostic with the help of fully iterative algorithm modeling at different dose levels in contrast-enhanced MDCT of the chest," Wirth wrote in an email to AuntMinnie.com.
In the study of 11 cadavers, model-based iterative reconstruction (MBIR, GE Healthcare) reduced dose by 82% compared with an earlier reconstruction algorithm, adaptive statistical iterative reconstruction (ASIR, GE), leading to submillisievert chest scans for all cadavers without loss of image quality.
![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)