About this Research Topic
X-ray CT has revolutionized the diagnosis and treatment of varying diseases across the world. Advances in CT imaging have been steadily driven by interest and needs in both clinical and research settings in the past 50 years. To date, technical innovations and new applications have been emerging in many areas of the CT field, including but not limited to optimization of radiation dose and scan protocols, detector techniques, image reconstruction/processing methods, contrast media, diagnostics, and radiation therapy.
The objective of this Research Topic is to bring together research that marks the emerging technologies and applications in the field of clinical CT imaging.
Manuscripts submitted to this Research Topic shall be under one of the following themes. Other related research topics may be considered as suitable on a case-by-case basis.
1. Radiation dose reduction
Suitable topics include but are not limited to the following examples: management and optimization of CT scan and reconstruction protocols for dose reduction; development and validation of academic and commercial low-dose CT denoising and image reconstruction methods; clinical comparison of acceptable low-dose limit across different CT scanner models.
2. CT image artifact correction
Suitable topics include but are not limited to the following examples: optimization and validation of CT scan and reconstruction protocols for artifact reduction; technical development and validation of academic or commercial CT artifact correction methods; automatic artifact detection and / or correction.
3. X-ray detector techniques
Suitable topics include but not limited to the following examples: characterization of objective image quality of new detector techniques; clinical validation of diagnostic image quality of new detector techniques; technical development of new photon-counting-detector techniques.
4. X-ray sources and CT systems
Suitable topics include but not limited to the following examples: development of new x-ray source for clinical CT; phase contrast and dark field imaging; new CT imaging system geometry and projection data acquisition schemes
5. Contrast media (e.g., new contrast materials, contrast dose reduction)
Suitable topics include but not limited to the following examples: case report of new contrast media; optimization and clinical evaluation of contras media delivery protocols; methods of contrast media dose reduction; dual- and multiple-contrast media enhanced CT.
6. Image-guided interventional procedure or radiation therapy
Suitable topics include but not limited to the following examples: lesion contouring; organ segmentation; tumor ablation procedure; new cone-beam CT (C-arm, O-arm, etc.) imaging techniques in the interventional suite.
7. Deep learning / machine learning / radiomics
Suitable topics include but not limited to the following examples: development and validation of deep- and machine-learning methods for lesion detection, contouring, and segmentation; new clinical applications of existing deep- and machine-learning methods; image quality improvement with new deep- and machine-learning methods in spectral / Cerebrovascular and cerebral perfusion / Cardiovascular and myocardial perfusion CT exams; assessment of robustness and reproducibility of radiomic features across varying scan and reconstruction protocols and scanner models.
For validation-oriented manuscripts, both phantom-based and clinical multi-reader multi-case (MRMC) evaluation studies will be suitable. The types of submissions should comply with the article types permitted by Frontiers in Radiology.
The objective of this Research Topic is to bring together research that marks the emerging technologies and applications in the field of clinical CT imaging.
Manuscripts submitted to this Research Topic shall be under one of the following themes. Other related research topics may be considered as suitable on a case-by-case basis.
1. Radiation dose reduction
Suitable topics include but are not limited to the following examples: management and optimization of CT scan and reconstruction protocols for dose reduction; development and validation of academic and commercial low-dose CT denoising and image reconstruction methods; clinical comparison of acceptable low-dose limit across different CT scanner models.
2. CT image artifact correction
Suitable topics include but are not limited to the following examples: optimization and validation of CT scan and reconstruction protocols for artifact reduction; technical development and validation of academic or commercial CT artifact correction methods; automatic artifact detection and / or correction.
3. X-ray detector techniques
Suitable topics include but not limited to the following examples: characterization of objective image quality of new detector techniques; clinical validation of diagnostic image quality of new detector techniques; technical development of new photon-counting-detector techniques.
4. X-ray sources and CT systems
Suitable topics include but not limited to the following examples: development of new x-ray source for clinical CT; phase contrast and dark field imaging; new CT imaging system geometry and projection data acquisition schemes
5. Contrast media (e.g., new contrast materials, contrast dose reduction)
Suitable topics include but not limited to the following examples: case report of new contrast media; optimization and clinical evaluation of contras media delivery protocols; methods of contrast media dose reduction; dual- and multiple-contrast media enhanced CT.
6. Image-guided interventional procedure or radiation therapy
Suitable topics include but not limited to the following examples: lesion contouring; organ segmentation; tumor ablation procedure; new cone-beam CT (C-arm, O-arm, etc.) imaging techniques in the interventional suite.
7. Deep learning / machine learning / radiomics
Suitable topics include but not limited to the following examples: development and validation of deep- and machine-learning methods for lesion detection, contouring, and segmentation; new clinical applications of existing deep- and machine-learning methods; image quality improvement with new deep- and machine-learning methods in spectral / Cerebrovascular and cerebral perfusion / Cardiovascular and myocardial perfusion CT exams; assessment of robustness and reproducibility of radiomic features across varying scan and reconstruction protocols and scanner models.
For validation-oriented manuscripts, both phantom-based and clinical multi-reader multi-case (MRMC) evaluation studies will be suitable. The types of submissions should comply with the article types permitted by Frontiers in Radiology.
Keywords: Spectral CT, Radiomics, X-ray computed tomography, Photon-counting-detector CT, Phase-contrast CT, Image guided radiation therapy, CT-guided interventional procedure, Contrast media, Artificial intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
