Medical imaging, encompassing modalities such as MRI, CT, and PET, has become a vital tool in oncology diagnosis and treatment, providing crucial anatomical and functional information. Traditional descriptive semi-quantitative assessment of tumors heavily relies on the spatiotemporal resolution and image quality of medical imaging. Therefore, the development of fast imaging techniques is of utmost importance for MRI, CT, and PET.

The advancements in signal processing, medical image reconstruction, deep learning, artificial intelligence, radiomics, and multi-modality imaging have opened new avenues for the development of rapid and robust, high-throughput quantitative imaging techniques. These techniques hold great potential for enhancing tumor diagnosis, treatment surveillance, and prognosis in clinical settings.

Our goal is to facilitate the translation of emerging fast medical imaging research into clinical practice. Therefore, we particularly encourage submissions that address the robustness, reproducibility, and generalization of advanced medical imaging approaches.

We are interested in topics including but not limited to:
• Fast medical imaging
• Image reconstruction
• CT, CBCT and Multi-spectral CT reconstruction
• PET imaging and image reconstruction
• MRI image acquisition and reconstruction
• Imaging biomarkers
• Oncology imaging
• Deep learning medical imaging
• AI-powered 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.

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Frontiers in Radiology

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