Investigating the Association of Carotid Atherosclerotic Plaque MRI Features and Silent Stroke After Carotid Endarterectomy.

BACKGROUND: The predictive value of carotid plaque characteristics for silent stroke (SS) after carotid endarterectomy (CEA) is unclear. OBJECTIVE: To investigate the associations between carotid plaque characteristics and postoperative SS in patients undergoing CEA. STUDY TYPE: Prospective. POPULATION: One hundred fifty-three patients (mean age: 65.4 ± 7.9 years; 126 males) with unilateral moderate-to-severe carotid stenosis (evaluated by CT angiography) referred for CEA. FIELD STRENGTH/SEQUENCE: 3 T, brain-MRI:T2-PROPELLER, T1-/T2-FLAIR, diffusion weighted imaging (DWI) and T2*, carotid-MRI:black-blood T1-/T2W, 3D TOF, Simultaneous Non-contrast Angiography intraplaque hemorrhage. ASSESSMENT: Patients underwent carotid-MRI within 1-week before CEA, and brain-MRI within 48-hours pre-/post-CEA. The presence and size (volume, maximum-area-percentage) of carotid lipid-rich necrotic core (LRNC), intraplaque hemorrhage (Type-I/Type-II IPH) and calcification were evaluated on carotid-MR images. Postoperative SS was assessed from pre-/post-CEA brain DWI. Patients were divided into moderate-carotid-stenosis (50%-69%) and severe-carotid-stenosis (70%-99%) groups and the associations between carotid plaque characteristics and SS were analyzed. STATISTICAL TESTS: Independent t test, Mann-Whitney U-test, chi-square test and logistic regressions (OR: odds ratio, CI: confidence interval). P value <0.05 was considered statistically significant. RESULTS: SS was found in 8 (16.3%) of the 49 patients with moderate-carotid-stenosis and 21 (20.2%) of the 104 patients with severe-carotid-stenosis. In patients with severe-carotid-stenosis, those with SS had significantly higher IPH (66.7% vs. 39.8%) and Type-I IPH (66.7% vs. 38.6%) than those without. The presence of IPH (OR 3.030, 95% CI 1.106-8.305) and Type-I IPH (OR 3.187, 95% CI 1.162-8.745) was significantly associated with SS. After adjustment, the associations of SS with presence of IPH (OR 3.294, 95% CI 1.122-9.669) and Type-I IPH (OR 3.633, 95% CI 1.216-10.859) remained significant. Moreover, the volume of Type-II IPH (OR 1.014, 95% CI 1.001-1.028), and maximum-area-percentage of Type-II IPH (OR 1.070, 95% CI 1.002-1.142) and LRNC (OR 1.030, 95% CI 1.000-1.061) were significantly associated with SS after adjustment. No significant (P range: 0.203-0.980) associations were found between carotid plaque characteristics and SS in patients with moderate-carotid-stenosis. DATA CONCLUSIONS: In patients with unilateral severe-carotid-stenosis, carotid vulnerable plaque MR features, particularly presence and size of IPH, might be effective predictors for SS after CEA. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid vessel wall volume measurements on three-dimensional ultrasonography.

PURPOSE: Carotid vessel wall volume (VWV) measurement on three-dimensional ultrasonography (3DUS) outperforms conventional two-dimensional ultrasonography for carotid atherosclerosis evaluation. Although time-saving semi-automated algorithms have been introduced, their clinical availability remains limited due to a lack of validation, particularly an extensive reliability analysis. This study compared inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid VWV measurements on 3DUS. METHODS: Thirty-one 3DUS volume datasets were prospectively acquired from 20 healthy subjects, aged >18 years, without previous stroke, transient ischemic attack, or cardiovascular disease. Five observers segmented all volume datasets both manually and semi-automatically. The process was repeated five times. Reliability was expressed by the intraclass correlation coefficient, supplemented by the coefficient of variation. RESULTS: Carotid VWV measurements using the common carotid artery (CCA) were more reliable than those using the internal carotid artery (ICA) or external carotid artery (ECA) for both manual and semiautomated segmentation (manual segmentation, CCA: inter-observer, 0.935; intra-observer, 0.934 to 0.966; ICA: inter-observer, 0.784; intra-observer, 0.756 to 0.878; ECA: inter-observer, 0.732; intraobserver, 0.919 to 0.962; semi-automated segmentation, CCA: inter-observer, 0.986; intra-observer, 0.954 to 0.993; ICA: inter-observer, 0.977; intra-observer, 0.958 to 0.978; ECA: inter-observer, 0.966; intra-observer, 0.884 to 0.937). Total carotid VWV measurements by manual (inter-observer, 0.922; intra-observer, 0.927 to 0.961) and semi-automated segmentation (inter-observer, 0.987; intra-observer, 0.968 to 0.989) were highly reliable. Semi-automated segmentation showed higher reliability than manual segmentation for both individual and total carotid VWV measurements. CONCLUSION: 3DUS carotid VWV measurements of the CCA are more reliable than measurements of the ICA and ECA. Total carotid VWV measurements are highly reliable. Semi-automated segmentation has higher reliability than manual segmentation.

Domain adaptive and fully automated carotid artery atherosclerotic lesion detection using an artificial intelligence approach (LATTE) on 3D MRI.

PURPOSE: To develop and evaluate a domain adaptive and fully automated review workflow (lesion assessment through tracklet evaluation, LATTE) for assessment of atherosclerotic disease in 3D carotid MR vessel wall imaging (MR VWI). METHODS: VWI of 279 subjects with carotid atherosclerosis were used to develop LATTE, mainly convolutional neural network (CNN)-based domain adaptive lesion classification after image quality assessment and artery of interest localization. Heterogeneity in test sets from various sites usually causes inferior CNN performance. With our novel unsupervised domain adaptation (DA), LATTE was designed to accurately classify arteries into normal arteries and early and advanced lesions without additional annotations on new datasets. VWI of 271 subjects from four datasets (eight sites) with slightly different imaging parameters/signal patterns were collected to assess the effectiveness of DA of LATTE using the area under the receiver operating characteristic curve (AUC) on all lesions and advanced lesions before and after DA. RESULTS: LATTE had good performance with advanced/all lesion classification, with the AUC of >0.88/0.83, significant improvements from >0.82/0.80 if without DA. CONCLUSIONS: LATTE can locate target arteries and distinguish carotid atherosclerotic lesions with consistently improved performance with DA on new datasets. It may be useful for carotid atherosclerosis detection and assessment on various clinical sites.

[Carotid artery wall imaging using 7.0T magnetic resonance imaging with threedimensional DANTE-prepared FLASH in ApoE-/- mice].

OBJECTIVE: To explore the feasibility of three-dimensional (3D) vessel wall imaging of carotid atherosclerotic plaques in ApoE-/- mice using 7.0T magnetic resonance imaging (MRI) with delays alternating with nutations for tailored excitation (DANTE)-prepared fast low-angle shot (DANTE-FLASH) technique. OBJECTIVE: Numerical simulations were performed for optimizing imaging parameters to maximize the wall-lumen contrast. Six ApoE-/- and three wild-type mice were scanned using a 7.0T MRI scanner with DANTE-FLASH and multi-slice 2D RARE coupled with inflow outflow saturation bands (2D-IOSBRARE). The wall signal-to-noise ratio (SNRwall), lumen SNR (SNRlumen), wall-lumen contrast-to-noise ratio (CNR), lumen area (LA), and wall area (WA) were compared between DANTE- FLASH and 2D-IOSB-RARE sequences. Linear regression analysis was performed to assess the correlation between the MRI measurements and histopathological measurements of LA and WA. OBJECTIVE: Based on the simulation results, a flip angle of 15° and a train length of 150 were implemented in the live imaging study. Compared with 2D-IOSB-RARE, DANTE-FLASH provided a slightly reduced CNR (P < 0.001) but much improved slice resolution. The LA and WA measurements from the DANTE-FLASH and 2D-IOSB- RARE showed excellent agreement based on ICC analysis (LA: ICC=0.94, P < 0.001; WA: ICC=0.93, P < 0.001) and Bland-Altman plots. Strong correlations were observed between the MRI and histopathological measurements for both LA (P < 0.0001) and WA (P < 0.0001). OBJECTIVE: As a 3D black-blood MR sequence, DANTE-FLASH provides isotropic high spatial resolution to allow reliable visualization and quantitative evaluation of the arteriosclerotic lesions within the carotid artery of ApoE-/- mice using a 7.0T MRI scanner.

A Comparison of Black-blood T2 Mapping Sequences for Carotid Vessel Wall Imaging at 3T: An Assessment of Accuracy and Repeatability.

PURPOSE: This study is to compare the accuracy of four different black-blood T2 mapping sequences in carotid vessel wall. METHODS: Four different black-blood T2 mapping sequences were developed and tested through phantom experiments and 17 healthy volunteers. The four sequences were: 1) double inversion-recovery (DIR) prepared 2D multi-echo spin-echo (MESE); 2) DIR-prepared 2D multi-echo fast spin-echo (MEFSE); 3) improved motion-sensitized driven-equilibrium (iMSDE) prepared 3D FSE and 4) iMSDE prepared 3D fast spoiled gradient echo (FSPGR). The concordance correlation coefficient and Bland-Altman statistics were used to compare the sequences with a gold-standard 2D MESE, without blood suppression in phantom studies. The volunteers were scanned twice to test the repeatability. Mean and standard deviation of vessel wall T2, signal-to-noise (SNR), the coefficient of variance and interclass coefficient (ICC) of the two scans were compared. RESULTS: The phantom study demonstrated that T2 measurements had high concordance with respect to the gold-standard (all r values >0.9). In the volunteer study, the DIR 2D MEFSE had significantly higher T2 values than the other three sequences (P < 0.01). There was no difference in T2 measurements obtained using the other three sequences (P > 0.05). iMSDE 3D FSE had the highest SNR (P < 0.05) compared with the other three sequences. The 2D DIR MESE has the highest repeatability (ICC: 0.96, [95% CI: 0.88-0.99]). CONCLUSION: Although accurate T2 measurements can be achieved in phantom by the four sequences, in vivo vessel wall T2 quantification shows significant differences. The in vivo images can be influenced by multiple factors including black-blood preparation and acquisition method. Therefore, a careful choice of acquisition methods and analysis of the confounding factors are required for accurate in vivo carotid vessel wall T2 measurements. From the settings in this study, the iMSDE prepared 3D FSE is preferred for the future volunteer/patient scans.

The study on the segmentation of carotid vessel wall in multicontrast MR images based on U?Net neural network / 中华放射学杂志

Objective To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U?Net neural network. Methods Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state?of?the?art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine?tuned U?Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U?Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland?Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U?Net segmentation and the manual segmentation. Results The sensitivity, specificity and Dice coefficient of the fine?tuned U?Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland?Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm2. The substantial agreement was observed between U?Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine?tuned U?Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

The study on the segmentation of carotid vessel wall in multicontrast MR images based on U-Net neural network / 中华放射学杂志

Objective@#To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U-Net neural network.@*Methods@#Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state-of-the-art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine-tuned U-Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U-Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland-Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U-Net segmentation and the manual segmentation.@*Results@#The sensitivity, specificity and Dice coefficient of the fine-tuned U-Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland-Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm2. The substantial agreement was observed between U-Net segmentation method and manual segmentation method.@*Conclusion@#Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine-tuned U-Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

Fast Von Mises strain imaging on ultrasound carotid vessel wall by flow driven diffusion method.

The elasticity of the vessel wall is important for the clinical identification of rupture-risks. The Von Mises strain can be a potential index for the indication of carotid vessel pathologies. In this paper, a fast clinically applicable real-time algorithm from time-sequence of B-mode carotid images is developed. Due to the compression induced by the normal cardiac pulsation, tissue motion occurs radially and non-rigidly. To obtain an accurate motion field, we developed a variational functional integrating the optical flow equation and an anisotropic regularizer, and designed a diffusion tensor to encourage coherence diffusion. The motion field is smoothed along the desired motion flow orientation. A fast, additive operator splitting scheme, which is ten times faster than the conventional discrete method, is used for the numerical implementation. To demonstrate the efficiency of the proposed approach, finite element models are set up for normal and pathological carotid vessel walls. The results indicate that the proposed diffusion approach obtains an accurate smooth and continuous motion field and greatly improves the follow up strain estimation using a fast differential strain filter. Furthermore, our approach using the Von Mises strain imaging on clinical ultrasound images of the carotid artery is validated. Participants above 65-years in age suffering from different stages of atherosclerosis in their carotid artery are selected. The results are evaluated by an experienced physician. The evaluation results demonstrate that the Von Mises strain has a good correspondence to the presence of certain suspicious areas in the B-mode images. The proposed method is therefore clinically applicable for the real-time Von Mises strain imaging of carotid vessel walls, and can be of great value as a complementary method to B-mode image for the clinical identification of the risk of plaque vulnerability.

Low-Grade Carotid Stenosis: Implications of MR Imaging.

Although treatment guidelines are well established for symptomatic patients with greater than 69% carotid stenosis on catheter angiography, optimal management of lower degrees of stenosis remain unclear. Vessel wall MR imaging of the carotid arteries has proved helpful in the evaluation of plaque burden and vulnerable plaque characteristics, and in stratifying risk in low-grade carotid stenosis. This article discusses the pathophysiology and imaging of atherosclerotic plaques resulting in low-grade carotid stenosis, and the corresponding stroke risk and association with plaque elsewhere in the cardiovascular system.