Recognizing and Minimizing Artifacts at Dual-Energy CT.
Anushri ParakhChansik AnSimon LennartzPrabhakar Shantha RajiahBenjamin M YehF Joseph SimeoneDushyant V SahaniAvinash R KambadakonePublished in: Radiographics : a review publication of the Radiological Society of North America, Inc (2021)
Dual-energy CT (DECT) is an exciting innovation in CT technology with profound capabilities to improve diagnosis and add value to patient care. Significant advances in this technology over the past decade have improved our ability to successfully adopt DECT into the clinical routine. To enable effective use of DECT, one must be aware of the pitfalls and artifacts related to this technology. Understanding the underlying technical basis of artifacts and the strategies to mitigate them requires optimization of scan protocols and parameters. The ability of radiologists and technologists to anticipate their occurrence and provide recommendations for proper selection of patients, intravenous and oral contrast media, and scan acquisition parameters is key to obtaining good-quality DECT images. In addition, choosing appropriate reconstruction algorithms such as image kernel, postprocessing parameters, and appropriate display settings is critical for preventing quantitative and qualitative interpretive errors. Therefore, knowledge of the appearances of these artifacts is essential to prevent errors and allows maximization of the potential of DECT. In this review article, the authors aim to provide a comprehensive and practical overview of possible artifacts that may be encountered at DECT across all currently available commercial clinical platforms. They also provide a pictorial overview of the diagnostic pitfalls and outline strategies for mitigating or preventing the occurrence of artifacts, when possible. The broadening scope of DECT applications necessitates up-to-date familiarity with these technologies to realize their full diagnostic potential.
Keyphrases
- image quality
- dual energy
- computed tomography
- contrast enhanced
- deep learning
- risk assessment
- healthcare
- newly diagnosed
- machine learning
- end stage renal disease
- magnetic resonance imaging
- ejection fraction
- systematic review
- clinical practice
- patient safety
- quality improvement
- human health
- high dose
- chronic kidney disease
- cone beam
- prognostic factors
- high resolution
- intellectual disability
- magnetic resonance
- convolutional neural network
- emergency department
- patient reported outcomes
- electronic health record
- optical coherence tomography