Magnetic Resonance Imaging: Mathematical Foundations and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 30
Special Issue Editor
Special Issue Information
Dear Colleagues,
Magnetic Resonance Imaging (MRI) is one of the most powerful and versatile non-invasive imaging modalities used in clinical and biomedical research. With its ability to acquire high-resolution anatomical, functional, and molecular information, MRI has enabled numerous breakthroughs in disease diagnosis, treatment monitoring, and physiological investigations.
The mathematical modeling and computational strategies behind MRI have been central to these advancements. In particular, recent progress in image reconstruction, multi-contrast and dynamic imaging, quantitative parameter estimation, and motion correction has increasingly relied on sophisticated mathematical models and algorithms. These developments highlight the pivotal role of mathematical innovation in enhancing image quality, accelerating acquisition, and improving the robustness of MRI systems, establishing this field as a dynamic and rapidly evolving area of interdisciplinary research.
We are pleased to invite you to contribute to this Special Issue, “Magnetic Resonance Imaging: Mathematical Foundations and Applications”, which aims to showcase recent theoretical and methodological advances in the mathematical aspects of MRI. The focus of this Special Issue aligns with the Applied Mathematics section of the Mathematics journal, emphasizing the development, analysis, and implementation of mathematical tools with clear relevance to imaging science.
This Special Issue seeks to collect high-quality original research and review articles that illustrate how mathematical modeling and computational analysis contribute to the advancement of MRI. We especially welcome contributions that bridge physical principles and computational methods, including those incorporating artificial intelligence (AI). Submissions introducing novel mathematical frameworks, providing rigorous theoretical analysis, or demonstrating practical improvements in imaging quality, acquisition speed, or robustness are particularly encouraged.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:
- Inverse problems and optimization techniques in MRI
- Compressed sensing and sparse reconstruction methods
- k-space data completion: methods and theory
- Data-and model-driven deep learning methods for MRI
- Diffusion models and generative methods in MRI
- Motion artifact correction and dynamic imaging
- Uncertainty quantification and robust reconstruction
- Mathematical modeling of MRI signal formation and contrast mechanisms
I look forward to receiving your contributions.
Dr. Zhuoxu Cui
Guest Editor
Manuscript Submission Information
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Keywords
- magnetic resonance imaging
- inverse problems
- compressed sensing
- k-space
- deep learning
- image reconstruction
- motion correction
- imaging physics
- optimization
- mathematical modeling
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