About this Research Topic
The goal of this Research Topic is to further improve the acquisition efficiency and reconstruction quality of multi-parameter imaging. The manuscript will mainly focus on three aspects. The novel acquisition strategy including fast sequence design and optimized subsampling pattern design with high-efficiency will be applied to speed up the imaging process via reducing information redundancy between multiple-parameter images; an efficient image reconstruction algorithm will be developed to further improve the quality of image through joint reconstruction of multi-parameter images, and AI-based diagnosis technologies will be combined with multi-parameter neuroimaging to improve the efficiency and accuracy of clinical diagnosis.
The scope of the manuscripts in this Research Topic covers, but are not limited to:
- New MRI contrast and biomarker for neuroimaging
- Novel sequence developments to further improve the speed and quality of multi-parameter MRI.
- Novel sub-sampling pattern design including AI-based methods, to further accelerate the sampling efficiency.
- Develop highly efficient reconstruction algorithms including deep learning techniques to reconstruct efficiently the multi-parameter images.
- Potential applications of multi-parameter MRI in clinical diagnosis and treatment.
- Suggestions and guidelines to improve clinical values of multi-parameter magnetic resonance neuroimaging.
Keywords: Multi-parameter MRI, Neuroimaging, Fast Imaging, Sequence Design, Deep Learning, Image Reconstruction
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.