AI Article Synopsis
- Spectral computed tomography (CT) is an advanced version of conventional CT that enhances imaging capabilities through dual-energy and photon-counting methods.
- These methods offer benefits like improved image quality, better material analysis, and enhanced feature measurement compared to traditional CT.
- Despite these advancements, spectral CT faces challenges like data and image artifacts, leading to a growing use of machine learning techniques to resolve these issues in clinical settings.
Article Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10927029 | PMC |
| http://dx.doi.org/10.1109/trpms.2023.3314131 | DOI Listing |
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