Leveraging deep learning for improving parameter extraction from perfusion MR images: A narrative review

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Biomedical Engineering

Background:

• Perfusion MRI assesses tissue microcirculation using techniques like DCE, DSC, ASL, and IVIM.
• Traditional perfusion MRI parameter quantification is slow, variable, and affected by noise.
• Deep learning (DL) offers AI-driven solutions for efficient and accurate perfusion MRI analysis.

Purpose of the Study:

• Review state-of-the-art DL techniques in perfusion MRI (DCE, DSC, ASL, IVIM).
• Focus on advantages, challenges, and clinical applications of DL in perfusion MRI.
• Highlight DL's potential to address limitations of conventional methods.

Main Methods:

• Review of DL techniques including CNNs, RNNs, and GANs applied to perfusion MRI.
• Analysis of DL's capability in handling spatial-temporal data and image enhancement.
• Evaluation of DL for precise perfusion parameter extraction.

Main Results:

• DL methods significantly improve upon conventional approaches by reducing noise and inter-observer variability.
• DL algorithms enhance image quality and enable more precise extraction of perfusion parameters.
• Specific DL architectures like CNNs, RNNs, and GANs are effective for perfusion MRI data.

Conclusions:

• DL integration in perfusion MRI represents a significant advancement in diagnostic accuracy and treatment planning.
• DL-based perfusion MRI offers a path towards more personalized medicine.
• The growing demand for precise imaging positions DL as crucial for improving clinical outcomes.