Energy-sensitive X-ray computed tomography is a very active topic of research in biomedical, clinical, industrial, and security fields. Some of them have changed and benefited our lives greatly, such as dual-energy multi-slice CT in hospitals and dual-energy security CT in airports. Some emerging techniques may soon enter our lives, such as photon-counting spectral CT and dual-MeV-energy container CT. Other techniques, such as X-ray fluorescence CT and inverse Compton scattering X-ray source imaging, are playing important roles in pre-clinical studies and awaiting innovations for wider application.
Over the past decade, there has been steady and great progress in developments of quasi-monochromatic X-ray source, single-photon counting detector, new imaging systems, reconstruction algorithms, material recognition, molecular imaging with various probes, as well as deep learning techniques. Energy-sensitive imaging is becoming the future of clinical X-ray systems, where people are working towards better image quality and lower doses delivered to the patients. Meanwhile, people are also searching for novel applications that can be enabled by new technologies. Compared to MRI, SPECT, and PET, X-ray CT currently has great deficiencies in functional imaging due to its low sensitivity of biomarkers. Energy-sensitive CT, X-ray fluorescence CT, novel molecular contrast agents, and deep learning techniques are expected to increase its sensitivity, making it a good complement to other modalities and enabling new tools to study the biomedical process.
This Research Topic will focus on the current development of energy-sensitive X-ray computed tomography imaging. Manuscript submissions in the related areas are welcome. Potential topics include, but are not limited to:
- Novel applications
- Quasi-monochromatic X-ray source
- Energy-sensitive detector
- System modeling
- Image reconstruction
- Image processing and analysis
- Deep learning techniques
- Contrast agents including nanoparticles, other molecular probes
- Novel systems
Energy-sensitive X-ray computed tomography is a very active topic of research in biomedical, clinical, industrial, and security fields. Some of them have changed and benefited our lives greatly, such as dual-energy multi-slice CT in hospitals and dual-energy security CT in airports. Some emerging techniques may soon enter our lives, such as photon-counting spectral CT and dual-MeV-energy container CT. Other techniques, such as X-ray fluorescence CT and inverse Compton scattering X-ray source imaging, are playing important roles in pre-clinical studies and awaiting innovations for wider application.
Over the past decade, there has been steady and great progress in developments of quasi-monochromatic X-ray source, single-photon counting detector, new imaging systems, reconstruction algorithms, material recognition, molecular imaging with various probes, as well as deep learning techniques. Energy-sensitive imaging is becoming the future of clinical X-ray systems, where people are working towards better image quality and lower doses delivered to the patients. Meanwhile, people are also searching for novel applications that can be enabled by new technologies. Compared to MRI, SPECT, and PET, X-ray CT currently has great deficiencies in functional imaging due to its low sensitivity of biomarkers. Energy-sensitive CT, X-ray fluorescence CT, novel molecular contrast agents, and deep learning techniques are expected to increase its sensitivity, making it a good complement to other modalities and enabling new tools to study the biomedical process.
This Research Topic will focus on the current development of energy-sensitive X-ray computed tomography imaging. Manuscript submissions in the related areas are welcome. Potential topics include, but are not limited to:
- Novel applications
- Quasi-monochromatic X-ray source
- Energy-sensitive detector
- System modeling
- Image reconstruction
- Image processing and analysis
- Deep learning techniques
- Contrast agents including nanoparticles, other molecular probes
- Novel systems
