Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.
| Autor: | Mese I; Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, Istanbul, Turkey., Altintas Taslicay C; Department of Radiology, MD Anderson Cancer Center, Houston, TX, USA., Sivrioglu AK; Department of Radiology, Liv Hospital Vadistanbul, Istanbul, Turkey. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Acta radiologica (Stockholm, Sweden : 1987) [Acta Radiol] 2024 Feb; Vol. 65 (2), pp. 159-166. Date of Electronic Publication: 2023 Dec 25. |
| DOI: | 10.1177/02841851231217995 |
| Abstrakt: | This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management. However, data requirements, computational resources, and regulatory and ethical concerns remain challenges that need to be addressed to fully realize the potential of this technology. The fusion of photon-counting CT and deep learning algorithms is poised to revolutionize medical imaging and transform patient care. Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Databáze: | MEDLINE |
| Externí odkaz: |
|