Research
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Figure 1. Yim et al, Proceeding of the ISMRM 2004 |
Vascular Imaging
Powerful new methods for the detection and evaluation of vascular disease are becoming widely available including contrast-enhanced magnetic resonance angiography and computed tomographic angiography. However, images of the vasculature produced by these modalities are complex and can be challenging to interpret. Research in our department is being conducted to improve interpretation of vascular imaging. A particular emphasis of our research is on the development of novel computational algorithms for the analysis of vascular imaging. This research is summarized below.
Characterization of the carotid artery from magnetic resonance angiography
Typically, the severity of atherosclerotic disease is described by the degree of stenosis, or narrowing of the artery. In clinical practice, assessment of the degree of stenosis is more qualitative than quantitative in nature as it is based on the radiologists impression. We are thus developing novel algorithms to produce quantitative measurement of the degree of stenosis. Recent work in this area focused on the use of the Isosurface Deformable Model for segmentation of the carotid artery from magnetic resonance angiography (figure 1).
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Figure 2. Yim et al, Plaque Characterization Using Multi-modality Imaging: Pixels to Molecular, Suri, Yuan, Wilson, and Laxminaryan eds. |
The risk of stroke from carotid artery disease is determined in part, by the degree of stenosis of the carotid artery, but other aspects of the carotid artery are likely to contribute to the risk of stroke. Evidence is emerging, in particular, that hemodynamic conditions within the carotid artery may play a role in the destabilization of the atherosclerotic plaque of the carotid artery. However, hemodynamic conditions and their relationship to instability of carotid artery plaque are not well understood. Research in our department is being directed at modeling of hemodynamic conditions in the carotid artery including wall shear stress patterns using computational fluid dynamics (figure 2).
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Figure 3. Robinson et al, Proceedings of SPIE 2006, Volume 6143. |
Alternatively, there is a great diversity of shapes of atherosclerotic carotid arteries. Studies in digital subtraction angiography of the carotid artery have shown that irregularity of the carotid artery shape is predictive of stroke. Conceivably, this finding should also be obtained from magnetic resonance angiography. We have developed computational methods that allow for reproducible characterization of the irregularity of carotid artery shape (figure 3).
Surface reconstruction from orthogonal contours in abdominal aortic aneurysms
Abdominal aortic aneurysms can be challenging to visualize and to characterize in a quantitative manner. Research is underway to improve visualization and analysis of AAA from computed tomographic angiography. A novel method has been developed that has potential application to the interpretation of AAA in which a surface is reconstructed from orthogonal contours (figure 4).
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Figure 4. Zhang et al Proceedings of SPIE 2006, Volume 6141. |
Subtraction computed tomographic angiography
Computed tomographic angiography is a promising technique for the evaluation of atherosclerotic disease of the lower extremities. However, the use of CTA in evaluation of atherosclerotic disease of the lower extremities has been relatively modest. The primary shortcoming of CTA in this vascular territory has been the difficulty of interpretation of CTA in the presence of a calcified arterial wall. When arterial wall is calcified, the lumenal narrowing can be obscured, in which case the radiologist must review each cross-sectional image to appreciate the degree of lumenal narrowing. Even with careful review of the cross-sectional images, the presence of calcification commonly causes an over-estimation of the degree of stenosis. We have developed the method of subtraction computed tomographic angiography that allows for the unobstructed visualization of calcified arteries. The algorithm is based on precise registration, or alignment, of pre- and post-contrast computed tomographic angiography followed by subtraction (figure 5).
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