Quantitative segmentation of principal carotid atherosclerotic lesion components by feature space analysis based on multicontrast MRI at 1.5 T

Imaging Approaches to the Diagnosis of Vascular Diseases

PURPOSE OF REVIEW

Vascular imaging is a complex field including numerous modalities and imaging markers. This review is focused on important and recent findings in atherosclerotic carotid artery plaque imaging with an emphasis on developments in magnetic resonance imaging (MRI) and computed tomography (CT).

RECENT FINDINGS

Recent evidence shows that carotid plaque characteristics and not only established measures of carotid plaque burden and stenosis are associated independently with cardiovascular outcomes. On carotid MRI, the presence of a lipid-rich necrotic core (LRNC) has been associated with incident cardiovascular disease (CVD) events independent of wall thickness, a traditional measure of plaque burden. On carotid MRI, intraplaque hemorrhage (IPH) presence has been identified as an independent predictor of stroke. The presence of a fissured carotid fibrous cap has been associated with contrast enhancement on CT angiography imaging. Carotid artery plaque characteristics have been associated with incident CVD events, and advanced plaque imaging techniques may gain additional prominence in the clinical treatment decision process.

Techniques to derive geometries for image-based Eulerian computations

PURPOSE

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

DESIGN/METHODOLOGY/APPROACH

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

FINDINGS

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

ORIGINALITY/VALUE

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

The Effect of Lipid Modification on Peripheral Artery Disease after Endovascular Intervention Trial (ELIMIT)

METHODS

A total of 102 patients were randomized to either mono-therapy with simvastatin (40 mg daily) or triple-therapy with simvastatin (40 mg daily), extended-release niacin (1500 mg daily), and ezetimibe (10 mg daily). MRI was performed at baseline and 6, 12, and 24 months. SFA wall, lumen, and total vessel volumes were quantified. MRI-derived SFA parameters and lipids were analyzed with multilevel models and nonparametric tests, respectively.

RESULTS

Baseline characteristics did not differ between mono and triple-therapy groups, except for ethnicity (p = 0.02). SFA wall, lumen, and total vessel volumes increased non-significantly for both groups between baseline and 24-months. Non-high-density lipoprotein cholesterol was significantly reduced at 12 months with triple-therapy compared with mono-therapy (p = 0.01).

CONCLUSION

No significant differences were observed between mono-therapy using simvastatin and triple-therapy with simvastatin, extended-release niacin, and ezetimibe for 24-month changes in SFA wall, lumen, and total vessel volumes.

CLINICAL TRIAL REGISTRATION INFORMATION

NCT00687076; Link: http://clinicaltrials.gov/ct2/show/NCT00687076.

An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation

A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.

Bayes clustering and structural support vector machines for segmentation of carotid artery plaques in multicontrast MRI

Accurate segmentation of carotid artery plaque in MR images is not only a key part but also an essential step for in vivo plaque analysis. Due to the indistinct MR images, it is very difficult to implement the automatic segmentation. Two kinds of classification models, that is, Bayes clustering and SSVM, are introduced in this paper to segment the internal lumen wall of carotid artery. The comparative experimental results show the segmentation performance of SSVM is better than Bayes.

Carotid artery disease and stroke: assessing risk with vessel wall MRI

Although MRI is widely used to diagnose stenotic carotid arteries, it also detects characteristics of the atherosclerotic plaque itself, including its size, composition, and activity. These features are emerging as additional risk factors for stroke that can be feasibly acquired clinically. This paper summarizes the state of evidence for a clinical role for MRI of carotid atherosclerosis.

Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology

We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and μCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and μCT images to MRI allowed for 3D rotations and in-plane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3%) and was significantly better than when only original intensities and distance features were used (Friedman, p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant.

In vivo evaluation of quantitative MR angiography in a canine carotid artery stenosis model

BACKGROUND AND PURPOSE

Large-vessel cerebral blood flow quantification has emerged as a potential predictor of stroke risk. QMRA uses phase-contrast techniques to noninvasively measure vessel flows. To evaluate the in vivo accuracy of QMRA for measuring the effects of progressive arterial stenosis, we compared this technique with invasive flow measurements from a sonographic transit-time flow probe in a canine model.

MATERIALS AND METHODS

A sonographic flow probe was implanted around the CCA of hound dogs (n = 4) under general anesthesia. Pulsatile blood flow and arterial pressure were continuously recorded during CCA flow measurements with QMRA. A vascular tourniquet was applied around the CCA to produce progressive stenosis and varying flow rates. Statistical comparisons were made by using the Pearson product moment correlation coefficient.

RESULTS

A total of 60 paired CCA flow measurements were compared. Mean blood flows ranged between 21 and 691 mL/min during QMRA acquisition as measured by the flow probe. The correlation coefficients between flow probe and QMRA measurements for mean, maximum, and minimum volume flow rates were 0.99 (P < .0001), 0.98 (P < .0001), and 0.96 (P < .0001), respectively. The overall proportional difference between the 2 techniques was 7.8 ± 1%. Measurements at higher flow rates and in the absence of arterial stenosis had the lowest PD.

CONCLUSIONS

Noninvasive CCA flow measurements by using QMRA are accurate compared with invasive flow-probe measurements in a canine arterial flow model with stenosis and may be useful for the evaluation of the hemodynamic effects of stenosis caused by cerebrovascular atherosclerosis.