Baris Kanber

Shear Wave Elastography May Be Superior to Greyscale Median for the Identification of Carotid Plaque Vulnerability: A Comparison with Histology

PURPOSE There is a need to develop methods that reliably quantify characteristics associated with vulnerable carotid plaque. Greyscale median (GSM) and shear wave elastography (SWE) are two techniques that may improve individual plaque risk stratification. SWE, which quantifies Youngs Modulus (YM) to estimate tissue stiffness, has been researched in the liver, breast, thyroid and prostate, but its use in carotid plaques is novel. MATERIALS AND METHODS The aim of this study was to quantify YM and GSM of plaques and compare to histology. 25 patients (64% male) with a mean age of 76 underwent both clinical and SWE imaging. The mean GSM was quantified over a cardiac cycle. The mean YM was quantified in multiple regions within the plaque over 5 frames. Histological features were assessed following carotid endarterectomy. RESULTS The mean YM of unstable plaques was significantly lower than that of stable plaques (50.0 kPa vs. 79.1 kPa; p = 0.027). The presence of intra-plaque hemorrhage, thrombus and increasing numbers of foam cells was also associated with a significantly lower YM. Plaque YM did not correlate well with plaque GSM (r =  .12). The mean plaque GSM was the same in both unstable and stable plaques. Fibrous plaques had a significantly higher GSM (p = 0.036). CONCLUSION In conclusion, SWE provides additional information on plaque stiffness which may be of clinical benefit to help identify vulnerable plaque, and warrants further study.

Quantitative assessment of carotid plaque surface irregularities and correlation to cerebrovascular symptoms

BackgroundThe purpose of this study was to determine whether surface irregularities measured from ultrasound images of carotid artery plaques and quantified using a novel method, correlate with the presence of ipsilateral hemispheric cerebrovascular symptoms.MethodsA plaque surface irregularity index (SII) was measured in 47 carotid artery plaques (32 subjects, stenosis range 10% -95%, 49% symptomatic) using ultrasound image sequences spanning several cardiac cycles. The differences in the distribution of SII in plaques with ipsilateral hemispheric symptoms versus those without symptoms and the correlation between the SII of plaques and the degrees of stenosis of the corresponding arteries were assessed. Diagnostic performance of plaque SII was evaluated on its own and in combination with the degree of stenosis.ResultsThe mean SII was significantly greater for plaques with ipsilateral hemispheric symptoms (1.89 radians/mm) than for asymptomatic plaques (1.67 radians/mm, p = 0.03). There was no statistically significant association between the SII and the degree of stenosis (p = 0.30). SII predicted the presence of cerebrovascular symptoms with an accuracy of 66% (sensitivity 65%, specificity 67%) on its own and with an accuracy of 83% (sensitivity 96%, specificity 71%) in combination with the degree of stenosis.ConclusionsQuantitative assessment of carotid plaque surface irregularities using a novel SII parameter correlates with the presence ipsilateral hemispheric cerebrovascular symptoms and may increase diagnostic performance beyond that provided by the degree of stenosis.

A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis.

Multiple sclerosis lesions influence the process of image analysis, leading to tissue segmentation problems and biased morphometric estimates. Existing techniques try to reduce this bias by filling all lesions as normal-appearing white matter on T1-weighted images, considering each time-point separately. However, due to lesion segmentation errors and the presence of structures adjacent to the lesions, such as the ventricles and deep grey matter nuclei, filling all lesions with white matter-like intensities introduces errors and artefacts. In this paper, we present a novel lesion filling strategy inspired by in-painting techniques used in computer graphics applications for image completion. The proposed technique uses a five-dimensional (5D), patch-based (multi-modality and multi-time-point), Non-Local Means algorithm that fills lesions with the most plausible texture. We demonstrate that this strategy introduces less bias, fewer artefacts and spurious edges than the current, publicly available techniques. The proposed method is modality-agnostic and can be applied to multiple time-points simultaneously. In addition, it preserves anatomical structures and signal-to-noise characteristics even when the lesions are neighbouring grey matter or cerebrospinal fluid, and avoids excess of blurring or rasterisation due to the choice of the segmentation plane, shape of the lesions, and their size and/or location.

A Novel Ultrasound-Based Carotid Plaque Risk Index Associated with the Presence of Cerebrovascular Symptoms.

PURPOSE The purpose of this study was to determine the efficacy of a novel ultrasound-based carotid plaque risk index (CPRI) in predicting the presence of cerebrovascular symptoms in patients with carotid artery stenosis. MATERIALS AND METHODS This was a cross-sectional, observational study involving 56 patients (mean age 76.6 years, 62.5 % male). Plaque grayscale median (GSM) and surface irregularity indices (SII) were measured in 82 stenosed carotid arteries (range 10 - 95 %) and combined with the degree of stenosis (DOS) in the form of (DOS*SII)/(1 + GSM). A reduced index DOS/(1 + GSM) not incorporating plaque surface irregularities was also investigated. Receiver operating characteristic curves (ROC) were used to study the diagnostic efficacy of CPRI, comparing against DOS and an equivalent risk index constructed using a conventional logistic regression based method with model parameters optimized to the dataset (CPRIlogistic). RESULTS There were 42 stenosed carotid arteries with cerebrovascular symptoms, and 40 without symptoms. The presence of symptoms significantly correlated with DOS, GSM and SII (p < 0.01). The median CPRI of the symptomatic (asymptomatic) groups were 23.2 (9.2) compared with 0.71 (0.30) for CPRIlogistic (p < 0.01). The diagnostic performance of CPRI exceeded that of CPRIlogistic and DOS, and demonstrated a better separation of the symptomatic and asymptomatic groups. CONCLUSION Our novel risk index combines quantitative measures of carotid plaque echogenicity and surface irregularities with the degree of stenosis. It is a better predictor of cerebrovascular symptoms than the degree of stenosis and could be valuable in studies and clinical trials aimed at identifying vulnerable carotid artery stenoses.

A Probabilistic Approach to Computerized Tracking of Arterial Walls in Ultrasound Image Sequences

Tracking of arterial walls in ultrasound image sequences is useful for studying the dynamics of arteries. Manual delineation is prohibitively labour intensive and existing methods of computerized segmentation are limited in terms of applicability and availability. This paper presents a probabilistic approach to the computerized tracking of arterial walls that is effective and easy to implement. In the probabilistic approach, given a point B with a probability of being in an arterial lumen of interest, the probability that a neighbouring point A is also a part of the same lumen is proportional to with a Gaussian fall in probability with increasing grayscale contrast between the two points. Efficacy of the probabilistic algorithm was evaluated by testing it on ultrasound images and image sequences of the carotid arteries and the abdominal aorta and various laboratory, ultrasound test objects. The results showed that the probabilistic algorithm produced robust and effective lumen segmentation in the majority of cases encountered. Comparison with a conventional region growing technique based on intensity thresholding with a running, regional intensity average identified the main benefits of the probabilistic approach as increased immunity to speckle noise within the arterial lumen and a reduced susceptibility to region overflowing at boundary imperfections.

Physiological motion of the carotid atherosclerotic plaque quantified using ultrasound B-Mode image analysis

Background Physical motion throughout the cardiac cycle may contribute to the rupture of the atherosclerotic carotid plaque, resulting in ischaemic stroke. The purpose of this study was to quantify the physiological motion of the atherosclerotic carotid plaque and to investigate any relationship between the quantified motion parameters and the degree of stenosis, greyscale plaque characteristics, and the presence of cerebrovascular symptoms. Methods Displacement, velocity and acceleration of 81 plaques (51% symptomatic, stenosis range 10%-95%) from 51 patients were measured using an automated system employing a block matching algorithm relative to the ultrasound probe and relative to the periadventitial tissues, over a mean duration of 5 cardiac cycles. Results Averaged across all plaques, the displacement amplitude was 1.2 mm relative to the probe, and 0.35 mm relative to the periadventitial tissues. Maximum and mean plaque velocities were 4.7 and 1.3 mm/s relative to the ultrasound probe, and 2.4 and 0.70 mm/s relative to the periadventitial tissues. The corresponding acceleration magnitudes were 69 and 22 mm/s2 relative to the probe, and 57 and 18 mm/s2 relative to the periadventitial tissues. There were no significant differences in any of the motion parameters, with respect to the presence of cerebrovascular symptoms, and none of the parameters showed a statistically significant relationship to the degree of stenosis, and the greyscale plaque characteristics (p≤0.05). The technique used was able to detect plaque motion amplitudes above 50μm. Conclusions This study provides quantitative data on the physiological motion of the atherosclerotic carotid plaque in-vivo. No significant relationship was found between the measured motion parameters and the presence of cerebrovascular symptoms, the degree of stenosis, and the greyscale plaque characteristics.

A preclinical ultrasound method for the assessment of vascular disease progression in murine models

Introduction The efficacy of preclinical ultrasound at providing a quantitative assessment of mouse models of vascular disease is relatively unknown. In this study, preclinical ultrasound was used in combination with a semi-automatic image processing method to track arterial distension alterations in mouse models of abdominal aortic aneurysm and atherosclerosis. Methods Longitudinal B-mode ultrasound images of the abdominal aorta were acquired using a preclinical ultrasound scanner. Arterial distension was assessed using a semi-automatic image processing algorithm to track vessel wall motion over the cardiac cycle. A standard, manual analysis method was applied for comparison. Results Mean arterial distension was significantly lower in abdominal aortic aneurysm mice between day 0 and day 7 post-onset of disease (p < 0.01) and between day 0 and day 14 (p < 0.001), while no difference was observed in sham control mice. Manual analysis detected a significant decrease (p < 0.05) between day 0 and day 14 only. Atherosclerotic mice showed alterations in arterial distension relating to genetic modification and diet. Arterial distension was significantly lower (p < 0.05) in Ldlr−/− (++/−−) mice fed high-fat western diet when compared with both wild type (++/++) mice and Ldlr−/− (++/−−) mice fed chow diet. The manual method did not detect a significant difference between these groups. Conclusions Arterial distension can be used as an early marker for the detection of arterial disease in murine models. The semi-automatic analysis method provided increased sensitivity to differences between experimental groups when compared to the manual analysis method.

Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting

Pathology can have an important impact on MRI analysis. Specifically, white matter hyper-intensities, tumours, infarcts, etc., can influence the results of various image analysis techniques such as segmentation and registration. Several algorithms have been proposed for image inpainting and restoration, mainly in the context of Multiple Sclerosis lesions. These techniques commonly rely on a set of manually segmented pathological regions for inpainting. Rather than relying on prior segmentations for image restoration, we present a combined segmentation and inpainting algorithm for multimodal images. The proposed method is based on an iterative collaboration between two patch-based techniques, PatchMatch and Non-Local Means, where the former is used to estimate the most probable location of the pathological outliers and the latter to gradually fill the segmented areas with the most plausible multimodal texture. We demonstrate that the proposed method is able to automatically restore multimodal intensities in pathological regions within the context of Multiple Sclerosis.