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A review on MR vascular image processing algorithms: acquisition and prefiltering: part I
Jasjit S Suri1, Kecheng Liu, Laura Reden
1Philips Medical Systems, Inc., Cleveland, OH 44143, USA.
Abstract:
Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.
Insights
This review covers magnetic resonance angiography (MRA) physics, data acquisition techniques, and prefiltering methods. It details vessel segmentation algorithms and discusses future challenges in MRA data processing.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Image Processing
Background:
- Vascular segmentation is crucial for medical image analysis.
- Magnetic Resonance Angiography (MRA) is a key technique for visualizing blood vessels.
- Advances in MRA necessitate efficient segmentation and filtering methods.
Purpose of the Study:
- To provide a comprehensive review of MRA physics and data acquisition.
- To discuss prefiltering techniques for MRA datasets.
- To explore vessel segmentation algorithms and their clinical applications.
Main Methods:
- Review of MRA physics, including receive coils and acquisition techniques (TOF, phase-contrast, contrast-enhanced, black-blood, T2-weighted, T2*-weighted).
- Analysis of prefiltering algorithms for removing non-vascular structures.
- Examination of various vessel segmentation algorithms.
Main Results:
- Detailed comparison of different MRA acquisition techniques, highlighting their advantages and disadvantages.
- Explanation of the necessity and methods for prefiltering MRA data.
- Overview of current vessel segmentation approaches.
Conclusions:
- MRA data acquisition and automated filtering present ongoing challenges.
- Future research should focus on improving automated filtering and segmentation accuracy.
- Clinical integration of advanced MRA techniques requires robust image processing pipelines.
