Energy-Efficient Usage of CT Scanners Through Mathematically Optimized Examination Scheduling.

Area of Science:

• Radiology and Medical Imaging
• Energy Management
• Sustainable Healthcare

Background:

• Medical imaging devices, particularly CT scanners, are significant energy consumers in healthcare settings.
• Reducing the environmental impact and operational costs of radiology departments is a growing priority.

Purpose of the Study:

• To estimate potential energy savings from optimized CT examination scheduling.
• To evaluate energy consumption reduction using both an ideal lower-bound and a clinically realistic model.

Main Methods:

• Retrospective analysis of 13,153 CT examinations and power consumption data from three scanners over 261 workdays.
• Optimization of CT examination scheduling using binary linear programming to minimize energy consumption.
• Evaluation of two scheduling models: a lower-bound model (LB) and a realistic model (R) with continuous routine scanner availability.

Main Results:

• The LB model demonstrated a 34.8% reduction in daily energy consumption compared to observed operation.
• The R model achieved an 11.0% reduction in daily energy consumption, translating to annual savings of 3,460 kWh.
• The R model projected annual cost savings of approximately $692 and a reduction of 998 kgCO2-equivalent emissions.

Conclusions:

• Optimization-based CT scheduling can substantially reduce energy consumption and costs without requiring new hardware.
• Even with realistic workflow constraints, significant sustainability and financial benefits are achievable.
• Automated, optimization-guided scheduling offers a practical strategy to lower the environmental footprint of radiology departments.