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

BACKGROUND: Perfusion magnetic resonance imaging (MRI) is a non-invasive technique essential for assessing tissue microcirculation and perfusion dynamics. Various perfusion MRI techniques like Dynamic Contrast-Enhanced (DCE), Dynamic Susceptibility Contrast (DSC), Arterial Spin Labeling (ASL), and Intravoxel Incoherent Motion (IVIM) provide critical insights into physiological and pathological processes. However, traditional methods for quantifying perfusion parameters are time-consuming, often prone to variability, and limited by noise and complex tissue dynamics. Recent advancements in artificial intelligence (AI), particularly in deep learning (DL), offer potential solutions to these challenges. DL algorithms can process large datasets efficiently, providing faster, more accurate parameter extraction with reduced subjectivity. AIM: This paper reviews the state-of-the-art DL-based techniques applied to perfusion MRI, considering DCE, DSC, ASL and IVIM acquisitions, focusing on their advantages, challenges, and potential clinical applications. MAIN FINDINGS: DL-driven methods promise significant improvements over conventional approaches, addressing limitations like noise, manual intervention, and inter-observer variability. Deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) are particularly valuable in handling spatial and temporal data, enhancing image quality, and facilitating precise parameter extraction. CONCLUSIONS: These innovations could revolutionize diagnostic accuracy and treatment planning, offering a new frontier in perfusion MRI by integrating DL with traditional imaging methods. As the demand for precise, efficient imaging grows, DL's role in perfusion MRI could significantly improve clinical outcomes, making personalized treatment a more realistic goal.