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Arterial Spin Labeling Perfusion MRI Signal Processing Through Traditional Methods and Machine Learning
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Room 1163, Baltimore, MD 20201, USA.
Abstract:
Arterial spin labeling (ASL) perfusion MRI is a non-invasive technique for quantifying and mapping cerebral blood flow (CBF). Depending on the tissue signal change after magnetically labeled arterial blood enters the brain tissue, ASL MRI signal can be affected by several factors, including the volume of arrived arterial blood, signal decay of labeled blood, physiological fluctuations of the brain and CBF, and head motion, etc. Some of them can be controlled using sophisticated state-of-art ASL MRI sequences, but the others can only be resolved with post-processing strategies. Over the decades, various post-processing methods have been proposed in the literature, and many post processing software packages have been released. This self-contained review provides a brief introduction to ASL MRI, recommendations for typical ASL MRI data acquisition protocols, an overview of the ASL data processing pipeline, and an introduction to typical methods used at each step in the pipeline. Although the main focus is on traditional heuristic model-based methods, a brief introduction to recent machine learning-based approaches is provided too.
Insights
Arterial spin labeling (ASL) perfusion MRI quantifies cerebral blood flow (CBF). This review covers ASL MRI data acquisition, processing pipelines, and heuristic and machine learning methods for accurate CBF mapping.
Area of Science:
- Radiology and Imaging
- Neuroscience
- Medical Physics
Background:
- Arterial spin labeling (ASL) perfusion MRI is a non-invasive method for cerebral blood flow (CBF) quantification.
- ASL MRI signal is influenced by factors like arterial blood volume, signal decay, physiological fluctuations, and head motion.
- While some factors are managed by ASL MRI sequences, others require robust post-processing strategies.
Purpose of the Study:
- To provide a comprehensive review of ASL MRI data processing.
- To introduce typical methods used in ASL MRI data processing pipelines.
- To briefly discuss both traditional heuristic and recent machine learning-based approaches.
Main Methods:
- Overview of ASL MRI data processing pipeline steps.
- Introduction to traditional heuristic model-based post-processing methods.
- Brief introduction to machine learning-based approaches for ASL MRI data analysis.
Main Results:
- ASL MRI requires sophisticated post-processing to address signal variations and improve CBF quantification.
- A range of heuristic methods have been developed over decades for ASL MRI data analysis.
- Emerging machine learning techniques offer potential advancements in ASL MRI data processing.
Conclusions:
- Effective post-processing is crucial for accurate ASL MRI-based CBF mapping.
- This review provides a foundational understanding of ASL MRI processing, encompassing traditional and novel methods.
- Further research into advanced post-processing techniques, including machine learning, is warranted for optimizing ASL MRI applications.
