MRI Techniques for Disease Detection

Dear Colleagues,

Magnetic Resonance Imaging (MRI) remains a gold standard for non-invasive disease detection, offering unparalleled soft tissue contrast, multi-parametric acquisition, and dynamic imaging capabilities. This Special Issue of Bioengineering aims to highlight recent breakthroughs in MRI methodologies that significantly improve diagnostic sensitivity, specificity, and reproducibility across a wide range of clinical applications.

The focus is twofold: advances in MRI physics and acquisition strategies (e.g., multi-shell diffusion MRI, MR fingerprinting, compressed sensing, and ultrafast sequences), and the rapidly growing integration of artificial intelligence (AI) across the imaging pipeline. Deep learning approaches, particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures, are now widely employed to accelerate acquisition (e.g., through AI-accelerated reconstruction), denoising, segmentation, anomaly detection, and radiomics analysis.

This Special Issue welcomes contributions that demonstrate the impact of AI-enhanced MRI on the detection and stratification of complex diseases such as Alzheimer's disease, multiple sclerosis, glioblastoma, prostate cancer, myocardial infarction, and cerebral stroke. For example, GANs are increasingly used to generate high-fidelity MR images from undersampled data, enabling significant scan time reduction without compromising diagnostic quality. Transformer models, leveraging attention mechanisms, are proving effective for capturing subtle spatial patterns and long-range dependencies in volumetric MRI data, improving lesion classification and disease staging.

We encourage submissions that explore the synergy between MRI physics, computational modeling, and AI—especially those including clinical validation, open datasets, and explainable AI frameworks. Topics may include deep learning-based segmentation of brain lesions, automated tumor grading using MRI-derived radiomics, AI-powered detection of microstructural abnormalities, and the integration of MRI with other modalities (e.g., PET, CT, EEG) through multi-modal AI fusion.

This Special Issue seeks to provide a comprehensive view of how next-generation MRI techniques, empowered by AI, are reshaping disease detection, enabling earlier intervention, and paving the way toward 5P diagnostics (Predictive, Preventive, Personalized, Participatory, Precise).

Prof. Dr. Christine Fernandez-Maloigne
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• MRI
• disease detection
• artificial intelligence
• deep learning