Over the past decades, many MRI parameters have been investigated and shown to have the capability or potential to provide anatomical, microstructural, functional, or neurochemical information for disease diagnosis and treatment. These parameters include but not limited to, T1, T2 and T2* relaxation times, isotropic and anisotropic diffusivities, flow, chemical shifts, thermal and magnetic susceptibility shifts, and chemical exchange phenomena reporting metabolism and pH. Multi-parameter measurements are less affected by systematic biases and interpretation biases, which improve the data reproducibility and precision. However, the application of the multiparameter MRI approach is hindered in the clinic due to its long acquisition time and sensitivity to motion. Although a variety of technologies, from the fast sequence design to the efficient reconstruction algorithm development, have been used solely or jointly to improve the acquisition speed of multi-parameter imaging, many efforts are still needed to fundamentally solve the problem for practical clinical applications. Fast multi-parameter imaging sequence design, highly efficient reconstruction algorithm development and the AI-based diagnosis with fast multi-parameter neuroimaging will be the main content of this 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.
Over the past decades, many MRI parameters have been investigated and shown to have the capability or potential to provide anatomical, microstructural, functional, or neurochemical information for disease diagnosis and treatment. These parameters include but not limited to, T1, T2 and T2* relaxation times, isotropic and anisotropic diffusivities, flow, chemical shifts, thermal and magnetic susceptibility shifts, and chemical exchange phenomena reporting metabolism and pH. Multi-parameter measurements are less affected by systematic biases and interpretation biases, which improve the data reproducibility and precision. However, the application of the multiparameter MRI approach is hindered in the clinic due to its long acquisition time and sensitivity to motion. Although a variety of technologies, from the fast sequence design to the efficient reconstruction algorithm development, have been used solely or jointly to improve the acquisition speed of multi-parameter imaging, many efforts are still needed to fundamentally solve the problem for practical clinical applications. Fast multi-parameter imaging sequence design, highly efficient reconstruction algorithm development and the AI-based diagnosis with fast multi-parameter neuroimaging will be the main content of this 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.
