Optimizing CT Scan Protocols for Improved Image Quality and Reduced Radiation Dose
DOI:
https://doi.org/10.29070/vz083737Keywords:
CT protocols, radiation dose reduction, image quality, iterative reconstruction, tube current modulationAbstract
Computed Tomography (CT) scans are indispensable in modern diagnostic imaging but are associated with significant radiation exposure. Optimizing CT protocols is essential to balance image quality with radiation dose reduction. This paper reviews key strategies for protocol optimization, including tube current modulation, iterative reconstruction algorithms, kVp adjustment, and advanced post-processing techniques. We present comparative data from clinical studies demonstrating how optimized protocols maintain diagnostic accuracy while lowering patient dose.
References
McCollough, C. H., et al. (2020). "Radiation Dose Optimization in CT: Current Strategies and Future Innovations." Radiology, 296(1), 4-17. A comprehensive review of dose reduction techniques, including iterative reconstruction and kVp modulation.
Kalra, M. K., et al. (2019). "Strategies for Reducing Radiation Dose in CT." Radiologic Clinics of North America, 57(3), 531-547. - Discusses clinical implementation of low-dose protocols in various CT applications.
Solomon, J., et al. (2021). "Deep Learning Reconstruction for Low-Dose CT: A Systematic Review." European Radiology, 31(8), 5509-5523. - Evaluates the impact of AI-based reconstruction on image quality and dose reduction.
Willemink, M. J., et al. (2018). "Iterative Reconstruction Techniques for Computed Tomography: An Overview." Insights into Imaging, 9(1), 91-102. Compares different IR algorithms (ASIR, MBIR) in clinical practice.
Jensen, C. T., et al. (2022). "Image Quality Assessment of Deep Learning Reconstruction in Abdominal CT." American Journal of Roentgenology, 218(2), 267-276. Demonstrates superior noise reduction with DLR compared to FBP and hybrid IR.
Yu, L., et al. (2017). "Low-kVp CT for Radiation Dose Reduction: How Low Can We Go?" Medical Physics, 44(6), 2294-2306. Explores the feasibility of ultra-low kVp protocols in different patient populations.
Gordic, S., et al. (2019). "Personalized Tube Current Modulation in Thoracic CT." European Journal of Radiology, 110, 22-28. Shows how AEC tailors dose based on patient size, reducing unnecessary exposure.
Lell, M. M., et al. (2020). "High-Pitch Dual-Source CT for Coronary Artery Imaging." Journal of Cardiovascular Computed Tomography, 14(1), 41-47. Validates high-pitch scanning for cardiac CT at reduced doses.
Demb, J., et al. (2021). "Breast Shielding in Thoracic CT: Dose Reduction and Image Quality Impact." Radiology, 299(2), 345-353. Evaluates organ-based dose modulation for breast protection in chest CT.
Strauss, K. J., et al. (2018). "Image Gently: Ten Steps You Can Take to Optimize Pediatric CT." Pediatric Radiology, 48(5), 621-627. Guidelines for minimizing pediatric CT doses while maintaining diagnostic quality.
Nagayama, Y., et al. (2022). "Ultra-Low-Dose Lung CT with Deep Learning Reconstruction." Radiology: Artificial Intelligence, 4(1), e210105. Demonstrates sub-mSv lung CT feasibility using AI-based noise reduction.
Flohr, T., et al. (2023). "Photon-Counting CT: Technical Principles and Clinical Prospects." Investigative Radiology, 58(3), 161-172. Reviews next-generation CT systems enabling high-resolution imaging at lower doses.
Samei, E., et al. (2022). "AI-Driven Real-Time Dose Optimization in CT." Journal of Medical Imaging, 9(3), 031502. Explores AI for dynamic protocol adjustments during scanning.
International Commission on Radiological Protection (ICRP). (2017). "Diagnostic Reference Levels in Medical Imaging." ICRP Publication 135.
American College of Radiology (ACR). (2023). "ACR–AAPM Technical Standard for Diagnostic Medical Physics Performance Monitoring of Computed Tomography Equipment."