Purpose 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. Materials and methods 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. Results 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. Conclusion DLIR-based optimized protocols were associated with improved environmental metrics, supporting their potential contribution to more sustainable radiology practices in high-volume settings. Clinical relevance statement 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. Key Points <UnorderedList Mark="Bullet"> <ItemContent> DLIR was associated with the implementation of lower tube voltage and reduced ICM dose, supporting more sustainable CT imaging based on protocol adaptations. </ItemContent> <ItemContent> 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. </ItemContent> <ItemContent> Optimization of contrast media dosing with deep learning-based image reconstruction contributed meaningfully to environmental benefits. </ItemContent> </UnorderedList> Graphical Abstract

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

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2026年6月24日

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Paolo Niccolò Franco,
Cesare Maino,
Davide Gandola,
Cammillo Talei Franzesi,
Rocco Corso,
Elena De Ponti,
Davide Ippolito

摘要

Purpose

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.

Materials and methods

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.

Results

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.

Conclusion

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

Clinical relevance statement

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.

Key Points

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.

Graphical Abstract