Innovative AI Approaches in Quantitative MRI: From Image Enhancement to Biomarker Estimation

Magnetic Resonance Imaging (MRI) stands as a cornerstone in modern medical diagnostics, providing insightful visualizations of anatomical structures and physiological processes within living tissues. Recent technological breakthroughs in MRI scanners have resulted in diverse imaging sequences that deliver an abundance of parametric maps, crucial for obtaining quantitative insights. Despite this progress, the challenge remains to effectively utilize this information. The fusion of Artificial Intelligence (AI) with MRI represents a transformative pathway to extract quantitative biomarkers with enhanced accuracy and reproducibility, shaping a new era in diagnostic imaging.

This Research Topic aims to delve into the integration of AI technologies with medical imaging to advance the field of quantitative MRI. By leveraging innovative AI methodologies such as transformer AI, generative AI, and graph neural networks, the goal is to transcend traditional limits and promote advancements in MRI diagnostics. The objective is to explore how these cutting-edge AI strategies can revolutionize MRI data acquisition and interpretation, contributing significantly to medical research and clinical practice.

To gather further insights in the evolving field of quantitative MRI through AI, we welcome articles addressing, but not limited to, the following themes:

• Optimization of acquisition protocols using AI to enhance image quality and efficiency.
• Application of novel AI technologies in image reconstruction and post-processing.
• AI-driven parameter estimation for accurate depiction of biological and pathological changes.
• Integration of AI with radiomics and radiogenomics for improved correlation of imaging features with genetic/clinical data.
• Exploration of AI in multimodal imaging fusion for comprehensive tissue pathophysiology analysis.

This Research Topic endeavors to bolster the impact of quantitative imaging through the integration of innovative AI techniques. Our aim is to encourage multidisciplinary collaboration that will foster innovation and facilitate the translation of AI-driven quantitative MRI advancements into practical, real-world medical applications.

Topic Editor Nicola Bertolino is employed by Charles River Laboratories. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review

Keywords: Artificial Intelligence, Machine learning, Neural Networks, MRI, Quantitative MRI, Parameter Estimation, Image Enhancement, Image Reconstruction, Protocol Optimization, Translational Research

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.