Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume...

To evaluate the environmental impact associated with CT scanners equipped with deep-learning-based image reconstruction (DLIR) compared with scanners equipped with hybrid-iterative reconstruction (HIR), focusing on electricity consumption, carbon dioxide equivalent (CO₂e) emissions, and iodinated contrast media (ICM) utilization in a high-volume tertiary referral center.

In this retrospective single-center study, environmental data were collected over an 18-month period from four CT scanners: two using HIR (Group 1) and two using DLIR (Group 2), including body CT examinations. DLIR-based protocols were implemented with reduced tube voltage (80-100 kV vs 120 kV) and optimized ICM doses. Electricity consumption, CO₂e emissions, and ICM utilization were quantified and compared between groups. Environmental outcomes were analyzed at the scanner level and normalized per examination.

A total of 42,300 examinations were analyzed (23,096 in Group 1; 19,204 in Group 2). Electricity consumption was 123,000 kWh for Group 1 and 66,927 kWh for Group 2, corresponding to 30.75 and 16.73 tons of CO₂e emissions, respectively. At the scanner level, this represented a reduction of 28,037 kWh and 7.01 tons of CO₂e per scanner (4.67 tons/year). DLIR-based protocols were associated with an ICM saving of 434 L over 18 months, corresponding to 4.47 tons of avoided CO₂e emissions and 60,730 L of water preserved. Combined CO₂e emissions from electricity and ICM were 49.62 tons in Group 1 and 29.10 tons in Group 2.

DLIR-based optimized protocols were associated with improved environmental metrics, supporting their potential contribution to more sustainable radiology practices in high-volume settings.

Deep learning-based image reconstruction enables routine body CT protocols with lower tube voltage and reduced ICM dose, supporting a clinically feasible transition toward more sustainable CT practice in high-volume imaging workflows.

DLIR was associated with the implementation of lower tube voltage and reduced ICM dose, supporting more sustainable CT imaging based on protocol adaptations. In a high-volume tertiary referral center, deep learning-based image reconstruction was associated with a substantial reduction in electricity consumption and overall CO₂-equivalent emissions compared with hybrid iterative reconstruction. Optimization of contrast media dosing with deep learning-based image reconstruction contributed meaningfully to environmental benefits.