Advances in Functional and Structural MR Image Analysis
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 16
Special Issue Editor
Interests: fMRI; image analysis; statistical analysis; cancer; brain
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Thanks to technological developments in recent years, MR image analysis methods have made significant progress. For functional MRI (fMRI), different sequences have been proposed to acquire data in order to quantitatively measure signal changes. Quantitative MRI (qMRI) methods have been applied to cancer detection as well as in brain and whole-body MRI. The most used qMRI approaches include diffusion, perfusion, T1/T2/T2*, and proton maps. The methods used for the analysis of these images have a direct impact on the experimental design of cognitive, neuroscience, clinical diagnostic, and treatment studies.
With the recent advances in artificial intelligence, particularly deep learning methods, structural MRI analysis has made tremendous progress. For instance, U-net, Transformer, and Autocoder network structures have been suggested for MRI analysis, making it possible to develop the Segment Anything model. Numerous machine learning algorithms and statistical methods, including survival analysis, have been implemented to merge clinical and radiomic features from structural MRI for the prediction of clinical variables.
This Special Issue of the journal Diagnostics covers the methodologies for the analysis of functional and structural MR images, and its application to clinical studies. We invite researchers to share knowledge in this field so that patients can have better and more efficient treatment outcomes.
Dr. Xingfeng Li
Guest Editor
Manuscript Submission Information
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Keywords
- functional MRI
- structural MRI
- image analysis
- radiomics
- machine learning
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