Automated Evaluation of Vascular MR Image data
Dutch Technology Foundation (STW LGT.6454)
Patrick de Koning, MSc
Ronald van 't Klooster, MSc
Magnetic resonance imaging (MRI) is playing an increasingly important clinical role as a non-invasive imaging modality for the evaluation of the vascular system. Magnetic resonance angiography (MRA) provides three-dimensional (3D) images of the vascular lumen, allowing detection of stenoses based on 3D image information. MRI vessel wall imaging (MR-VWI), on the other hand, provides information about the vessel morphology, and also information about the tissue components within the vessel wall based on high-resolution 2D cross-sectional images. While MRA is mainly of value for the detection of vascular stenoses or aneurisms, the strength of MR-VWI is in the early detection of the formation of plaque in the arterial wall and the identification of the type of plaque. Various studies have demonstrated the potential value of MR-VWI for the identification of 'vulnerable' plaque as opposed to stable plaque. Vulnerable plaques are rupture-prone plaques, which are mostly associated with mild to moderate luminal narrowings (20-50% luminal reduction) and exhibit well-defined histological characteristics; these are also often referred to as "thin-cap fibroatheromas". In addition to the presence of a thin fibrous cap, plaques are also thought to be vulnerable when they contain an elevated number of macrophages, and a large lipid/necrotic core. Vulnerable plaque(s) can be identified prospectively by MRI, followed by a proper therapeutic intervention; this would result in the prevention of the serious complications of plaque rupture.
As described above, the introduction of these new non-invasive MR imaging techniques will provides important clinical benefits. However, visual analysis of all the acquired image data is difficult due to the large number of images acquired in a typical MR examination. A typical MRA data set consists of between 500 and 1500 images and a typical VW dataset contains several tens of images. Accurate quantification of vascular stenoses from MRA data alone is hampered by the relatively low image resolution. The current state-of-the-art for the analysis of MRA data is based on visual assessment of Maximum Intensity Projections (MIP renderings) from multiple viewing angles, which result in loss of 3D information and loss in signal-to-noise ratio (SNR). Visual image analysis is also subject to inter- and intra-observer variabilities. Also VWI-MR images are currently mainly analyzed visually, or quantitatively using observer-dependent manual contour tracing tools. When multiple scans are acquired, such as combinations of MRA and VWI, currently no versatile software algorithms are available to link the image data from the MRA scan with the VWI scan.
In the proposed project the research is focused at developing novel knowledge-guided automated image segmentation methods for quantitative analysis of vascular MR image data by integrating information from both the MRA and MR-VWI techniques. The combination of MRA and MR-VWI is potentially a very powerful combination providing a detailed description of vascular pathologies in a non-invasive manner.
The MRA image data is used to obtain a 3D segmentation of the vascular lumen. After image registration, the segmented MRA lumen is used as initialization for the lumen and outer wall contour detection in the 2D VWI slices. Automated registration of the various VWI sequences will be performed such that for each position in the vessel wall the intensity values of the obtained VWI sequences can be derived. For each pixel in the vessel wall various features will be computed derived form the image data. Using pattern recognition techniques, each pixel will be classified as either being part of a certain plaque component, or as normal vessel wall.
Figure 1 Image processing pipeline
Figure 2 Vessel pathline automatically detected in the MRA volume
Figure 3. Automated segmentation of the lumen boundaries from the MRA volume
Figure 4 Segmentation steps for the lumen and outer contours in the T1-weighted images
Figure 5 Screen shot of the VesselMASS software package showing a manually plaque classification result for the common and internal carotid artery of a patient. The upper left panel shows the 6 available sequences for the selected slice level: 1) Time-of-flight, 2) MPRAGE, 3) Contrast-enhanced MRA, 4) T2-weighted, 5) T1-weighted, 6) T1-weighted post-contrast. The bottom panel shows the T1-weighted images of all acquired 18 slices in the current study.
Red=Lumen contour, Green=Outer contour, Yellow=Lipid tissue, Blue=hemorrhagic tissue.
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Rob J. van der Geest, PhD
Division of Image Processing
Department of Radiology, 1-C2S
Leiden University Medical Center
P.O. Box 9600
2300 RC Leiden
Tel. +31 (0)71 526 2138
Fax. +31 (0)71 526 6801