Life Cycle Analysis and Sustainability Opportunities in Radiology

References

• 1. The White House. Fact sheet: health sector leaders join Biden administration’s pledge to reduce greenhouse gas emissions 50% by 2030. https://www.whitehouse.gov/briefing-room/statements-releases/2022/06/30/fact-sheet-health-sector-leaders-join-biden-administrations-pledge-to-reduce-greenhouse-gas-emissions-50-by-2030/. Published June 30, 2022. Accessed September 30, 2024.
  Google Scholar
• 2. Heye T, Knoerl R, Wehrle T, et al. The energy consumption of radiology: energy- and cost-saving opportunities for CT and MRI operation. Radiology 2020;295(3):593–605.
  Google Scholar
• 3. Chua ALB, Amin R, Zhang J, Thiel CL, Gross JS. The environmental impact of interventional radiology: an evaluation of greenhouse gas emissions from an academic interventional radiology practice. J Vasc Interv Radiol 2021;32(6):907–915.e3.
  Medline Google Scholar
• 4. Brown M, Schoen JH, Gross J, Omary RA, Hanneman K. Climate Change and Radiology: Impetus for Change and a Toolkit for Action. Radiology 2023;307(4):e230229.
  Google Scholar
• 5. Thiel C, Vigil-Garcia M, Nande S, et al. Environmental Life Cycle Assessment of a U.S. Hospital-based Radiology Practice. Radiology 2024;313(2):e240398.
  Google Scholar
• 6. Büttner L, Posch H, Auer TA, et al. Switching off for future-Cost estimate and a simple approach to improving the ecological footprint of radiological departments. Eur J Radiol Open 2020;8:100320.
  Medline Google Scholar
• 7. Thrall JH, Brink JA, Zalis ME. The environmental, social, governance movement and radiology: opportunities and strategy. J Am Coll Radiol 2024;21(2):265–270.
  Medline Google Scholar
• 8. Toia GV, Rose SD, Brown Z, et al. Consumable Material Waste and Workflow Efficiency Comparison Between Multi-use Syringeless and Single-use Syringe-Based Injectors in Computed Tomography. Acad Radiol 2023;30(10):2340–2349.
  Medline Google Scholar
• 9. Yang A, Finkelstein M, Koo C, Doshi AH. Impact of deep learning image reconstruction methods on MRI throughput. Radiol Artif Intell 2024;6(3):e230181.
  Google Scholar
• 10. Kambadakone A. Artificial Intelligence and CT Image Reconstruction: Potential of a New Era in Radiation Dose Reduction. J Am Coll Radiol 2020;17(5):649–651.
  Medline Google Scholar