In vivo carotid strain imaging using principal strains in longitudinal view

Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque

In patients at high risk for ischemic stroke, clinical carotid ultrasound is often used to grade stenosis, determine plaque burden and assess stroke risk. Analysis currently requires a trained sonographer to manually identify vessel and plaque regions, which is time and labor intensive. We present a method for automatically determining bounding boxes and lumen segmentation using a Mask R-CNN network trained on sonographer assisted ground-truth carotid lumen segmentations. Automatic lumen segmentation also lays the groundwork for developing methods for accurate plaque segmentation, and wall thickness measurements in cases with no plaque. Different training schemes are used to identify the Mask R-CNN model with the highest accuracy. Utilizing a single-channel B-mode training input, our model produces a mean bounding box intersection over union (IoU) of 0.81 and a mean lumen segmentation IoU of 0.75. However, we encountered errors in prediction when the jugular vein is the most prominently visualized vessel in the B-mode image. This was due to the fact that our dataset has limited instances of B-mode images with both the jugular vein and carotid artery where the vein is dominantly visualized. Additional training datasets are anticipated to mitigate this issue.

In Vivo Adaptive Bayesian Regularized Lagrangian Carotid Strain Imaging for Murine Carotid Arteries and Its Associations With Histological Findings

OBJECTIVES

Non-invasive methods for monitoring arterial health and identifying early injury to optimize treatment for patients are desirable. The objective of this study was to demonstrate the use of an adaptive Bayesian regularized Lagrangian carotid strain imaging (ABR-LCSI) algorithm for monitoring of atherogenesis in a murine model and examine associations between the ultrasound strain measures and histology.

METHODS

Ultrasound radiofrequency (RF) data were acquired from both the right and left common carotid artery (CCA) of 10 (5 male and 5 female) ApoE tm1Unc/J mice at 6, 16 and 24 wk. Lagrangian accumulated axial, lateral and shear strain images and three strain indices-maximum accumulated strain index (MASI), peak mean strain of full region of interest (ROI) index (PMSRI) and strain at peak axial displacement index (SPADI)-were estimated using the ABR-LCSI algorithm. Mice were euthanized (n = 2 at 6 and 16 wk, n = 6 at 24 wk) for histology examination.

RESULTS

Sex-specific differences in strain indices of mice at 6, 16 and 24 wk were observed. For male mice, axial PMSRI and SPADI changed significantly from 6 to 24 wk (mean axial PMSRI at 6 wk = 14.10 ± 5.33% and that at 24 wk = -3.03 ± 5.61%, p < 0.001). For female mice, lateral MASI increased significantly from 6 to 24 wk (mean lateral MASI at 6 wk = 10.26 ± 3.13% and that at 24 wk = 16.42 ± 7.15%, p = 0.048). Both cohorts exhibited strong associations with ex vivo histological findings (male mice: correlation between number of elastin fibers and axial PMSRI: rs = 0.83, p = 0.01; female mice: correlation between shear MASI and plaque score: rs = 0.77, p = 0.009).

CONCLUSION

The results indicate that ABR-LCSI can be used to measure arterial wall strain in a murine model and that changes in strain are associated with changes in arterial wall structure and plaque formation.

Strain estimation in aortic roots from 4D echocardiographic images using medial modeling and deformable registration

Even though the central role of mechanics in the cardiovascular system is widely recognized, estimating mechanical deformation and strains in-vivo remains an ongoing practical challenge. Herein, we present a semi-automated framework to estimate strains from four-dimensional (4D) echocardiographic images and apply it to the aortic roots of patients with normal trileaflet aortic valves (TAV) and congenital bicuspid aortic valves (BAV). The method is based on fully nonlinear shell-based kinematics, which divides the strains into in-plane (shear and dilatational) and out-of-plane components. The results indicate that, even for size-matched non-aneurysmal aortic roots, BAV patients experience larger regional shear strains in their aortic roots. This elevated strains might be a contributing factor to the higher risk of aneurysm development in BAV patients. The proposed framework is openly available and applicable to any tubular structures.

Deformability of ascending thoracic aorta aneurysms assessed using ultrafast ultrasound and a principal strain estimator: In vitro evaluation and in vivo feasibility

BACKGROUND

Noninvasive vascular strain imaging under conventional line-by-line scanning has a low frame rate and lateral resolution, and depends on the coordinate system. It is thus affected by high deformations due to image decorrelation between frames.

PURPOSE

To develop an ultrafast time-ensemble regularized tissue-Doppler optical-flow principal strain estimator for aorta deformability assessment in a long-axis view.

METHODS

This approach alleviated the impact of lateral resolution using image compounding and that of the coordinate system dependency using principal strain. Accuracy and feasibility were evaluated in two aorta-mimicking phantoms first, and then in four age-matched individuals with either a normal aorta or a pathological ascending thoracic aorta aneurysm (TAA).

RESULTS

Instantaneous aortic maximum and minimum principal strain maps and regional accumulated strains during each cardiac cycle were estimated at systolic and diastolic phases to characterize the normal aorta and TAA. In vitro, principal strain results matched sonomicrometry measurements. In vivo, a significant decrease in maximum and minimum principal strains was observed in TAA cases, whose range was respectively 7.9 ± 6.4% and 8.2 ± 2.6% smaller than in normal aortas.

CONCLUSIONS

The proposed principal strain estimator showed an ability to potentially assess TAA deformability, which may provide an individualized and reliable evaluation method for TAA rupture risk assessment. This article is protected by copyright. All rights reserved.

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture

Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which the dilated convolution layers were used in the bottleneck. Both a fully automatic and a semi-automatic approach with a bounding box was implemented. The performance degradation in plaque segmentation due to errors in the bounding box is quantified. We found that the bounding box significantly improved the performance of the networks with U-Net Dice coefficients of 0.48 for automatic and 0.83 for semi-automatic segmentation of plaque. Similar results were also obtained for the dilated U-Net with Dice coefficients of 0.55 for automatic and 0.84 for semi-automatic when compared to manual segmentations of the same plaque by an experienced sonographer. A 5% error in the bounding box in both dimensions reduced the Dice coefficient to 0.79 and 0.80 for U-Net and dilated U-Net respectively.

Attenuation Coefficient Parameter Computations for Tissue Composition Assessment of Carotid Atherosclerotic Plaque in Vivo

Quantitative ultrasound has been used to assess carotid plaque tissue composition. Here, we compute the attenuation coefficient (AC) in vivo with the optimum power spectral shift estimator (OPSSE) and reference phantom method (RPM), extract AC parameters and form parametric maps. Differences between OPSSE and RPM AC parameters are computed. Relationships between AC parameters, surgical scores and histopathology assessments are examined. Kendall's τ correlations between OPSSE AC and surgical scores are significant, including those between cholesterol and Standard Deviation (adjusted p = 0.038); thrombus and Minimum (adjusted p = 0.002), Maximum (adjusted p = 0.021) and Standard Deviation (adjusted p = 0.001); ulceration and Average (adjusted p = 0.033), Median (unadjusted p = 0.013), Maximum (unadjusted p = 0.039) and Mode (adjusted p = 0.009). The strongest correlations with histopathology are percentage cholesterol and Median OPSSE (unadjusted p = 0.007); percentage hemorrhage and Minimum OPSSE (adjusted p < 0.001); hemosiderin score and Median OPSSE (adjusted p = 0.010); and percentage calcium and Percentage Non-physical RPM Pixels (unadjusted p = 0.014). Kruskal-Wallis H and Dunn's post hoc tests have the ability to distinguish between groups (p < 0.05). Results suggest AC parameters may assist in vivo evaluation of carotid plaque vulnerability.