Medical imaging, particularly within modalities such as MRI, CT, and PET, serves as a critical component in the process of diagnosing and treating oncological conditions. The field relies heavily on the spatiotemporal resolution and image quality to offer accurate anatomical and functional information. However, traditional descriptive semi-quantitative assessment is often hindered by the time-intensive nature of these techniques. Consequently, there is a pressing need for the development of fast imaging techniques to improve efficiency in MRI, CT, and PET.
Recent strides in signal processing, image reconstruction, deep learning, artificial intelligence, radiomics, and multi-modality imaging have illuminated new pathways for the creation of rapid, high-throughput quantitative imaging techniques. These innovative techniques promise to significantly enhance the accuracy and effectiveness of tumor diagnosis, treatment monitoring, and prognostic evaluations in clinical applications, but they necessitate rigorous testing for robustness and reliability.
This Research Topic aims to bridge the gap between groundbreaking research in fast medical imaging and its integration into clinical practice. The main objectives include encouraging studies that focus on the robustness, reproducibility, and generalization of these advanced imaging methodologies, thereby ensuring their applicability and utility across diverse clinical scenarios.
To gather further insights in the realm of fast medical imaging development, we welcome articles addressing, but not limited to, the following themes:
• Fast medical imaging
• Image reconstruction innovations
• CT, CBCT and Multi-spectral CT reconstruction methods
• Advances in PET imaging and image reconstruction
• MRI image acquisition and reconstruction technologies
• Evolution of Imaging biomarkers
• Cutting-edge oncology imaging approaches
• Deep learning applications in medical imaging
• AI-powered solutions in medical imaging
We would like to acknowledge Dr. Zhifeng Chen from Monash University for organizing this remarkable issue through their role as Topic Coordinator. Dr. Chen's current research interests encompass various areas such as Inverse Problems, Image Processing, Medical Imaging, Image Reconstruction, Magnetic Resonance Imaging (MRI), Computational Imaging, Quantitative MRI, Deep Learning Image Reconstruction and Processing, DCE-MRI, and MR Angiography.
Medical imaging, particularly within modalities such as MRI, CT, and PET, serves as a critical component in the process of diagnosing and treating oncological conditions. The field relies heavily on the spatiotemporal resolution and image quality to offer accurate anatomical and functional information. However, traditional descriptive semi-quantitative assessment is often hindered by the time-intensive nature of these techniques. Consequently, there is a pressing need for the development of fast imaging techniques to improve efficiency in MRI, CT, and PET.
Recent strides in signal processing, image reconstruction, deep learning, artificial intelligence, radiomics, and multi-modality imaging have illuminated new pathways for the creation of rapid, high-throughput quantitative imaging techniques. These innovative techniques promise to significantly enhance the accuracy and effectiveness of tumor diagnosis, treatment monitoring, and prognostic evaluations in clinical applications, but they necessitate rigorous testing for robustness and reliability.
This Research Topic aims to bridge the gap between groundbreaking research in fast medical imaging and its integration into clinical practice. The main objectives include encouraging studies that focus on the robustness, reproducibility, and generalization of these advanced imaging methodologies, thereby ensuring their applicability and utility across diverse clinical scenarios.
To gather further insights in the realm of fast medical imaging development, we welcome articles addressing, but not limited to, the following themes:
• Fast medical imaging
• Image reconstruction innovations
• CT, CBCT and Multi-spectral CT reconstruction methods
• Advances in PET imaging and image reconstruction
• MRI image acquisition and reconstruction technologies
• Evolution of Imaging biomarkers
• Cutting-edge oncology imaging approaches
• Deep learning applications in medical imaging
• AI-powered solutions in medical imaging
We would like to acknowledge Dr. Zhifeng Chen from Monash University for organizing this remarkable issue through their role as Topic Coordinator. Dr. Chen's current research interests encompass various areas such as Inverse Problems, Image Processing, Medical Imaging, Image Reconstruction, Magnetic Resonance Imaging (MRI), Computational Imaging, Quantitative MRI, Deep Learning Image Reconstruction and Processing, DCE-MRI, and MR Angiography.