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Leveraging deep learning for improving parameter extraction from perfusion MR images: A narrative review
Elisa Scalco1, Giovanna Rizzo2, Nicola Bertolino3
1Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Segrate, Italy.
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.
Insights
Deep learning (DL) enhances perfusion MRI by improving parameter extraction speed and accuracy for techniques like DCE, DSC, ASL, and IVIM. This AI integration promises to overcome traditional limitations, boosting diagnostic capabilities and personalized treatment planning.
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.
