GPU Accelerated Multilevel Lagrangian Carotid Strain Imaging

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.

In Vivo Longitudinal Monitoring of Cardiac Remodeling in Murine Ischemia Models With Adaptive Bayesian Regularized Cardiac Strain Imaging: Validation Against Histology

Adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) uses raw radiofrequency signals to estimate myocardial wall contractility as a surrogate measure of relative tissue elasticity incorporating regularization in the Bayesian sense. We determined the feasibility of using ABR-CSI -derived strain for in vivo longitudinal monitoring of cardiac remodeling in a murine ischemic injury model (myocardial infarction [MI] and ischemia-reperfusion [IR]) and validated the findings against ground truth histology. We randomly stratified 30 BALB/CJ mice (17 females, 13 males, median age = 10 wk) into three surgical groups (MI = 10, IR = 12, sham = 8) and imaged pre-surgery (baseline) and 1, 2, 7 and 14 d post-surgery using a pre-clinical high-frequency ultrasound system (VisualSonics Vevo 2100). We then used ABR-CSI to estimate end-systolic and peak radial (er) and longitudinal (el) strain estimates. ABR-CSI was found to have the ability to serially monitor non-uniform cardiac remodeling associated with murine MI and IR non-invasively through temporal variation of strain estimates post-surgery. Furthermore, radial end-systole (ES) strain images and segmental strain curves exhibited improved discrimination among infarct, border and remote regions around the myocardium compared with longitudinal strain results. For example, the MI group had significantly lower (Friedman's with Bonferroni-Dunn test, p = 0.002) ES er values in the anterior middle (infarcted) region at day 14 (n = 9, 9.23 ± 7.39%) compared with the BL group (n = 9, 44.32 ± 5.49). In contrast, anterior basal (remote region) mean ES er values did not differ significantly (non-significant Friedman's test, χ2 = 8.93, p = 0.06) at day 14 (n = 6, 33.05 ± 6.99%) compared with baseline (n = 6, 34.02 ± 6.75%). Histology slides stained with Masson's trichrome (MT) together with a machine learning model (random forest classifier) were used to derive the ground truth cardiac fibrosis parameter termed histology percentage of myocardial fibrosis (PMF). Both radial and longitudinal strain were found to have strong statistically significant correlations with the PMF parameter. However, radial strain had a higher Spearman's correlation value (εresρ = -0.67, n = 172, p < 0.001) compared with longitudinal strain (εlesρ = -0.60, n = 172, p < 0.001). Overall, the results of this study indicate that ABR-CSI can reliably perform non-invasive detection of infarcted and remote myocardium in small animal studies.

Time-Resolved Wall Shear Rate Mapping Using High-Frame-Rate Ultrasound Imaging

In atherosclerosis, low wall shear stress (WSS) is known to favor plaque development, while high WSS increases plaque rupture risk. To improve plaque diagnostics, WSS monitoring is crucial. Here, we propose wall shear imaging (WASHI), a noninvasive contrast-free framework that leverages high-frame-rate ultrasound (HiFRUS) to map the wall shear rate (WSR) that relates to WSS by the blood viscosity coefficient. Our method measures WSR as the tangential flow velocity gradient along the arterial wall from the flow vector field derived using a multi-angle vector Doppler technique. To improve the WSR estimation performance, WASHI semiautomatically tracks the wall position throughout the cardiac cycle. WASHI was first evaluated with an in vitro linear WSR gradient model; the estimated WSR was consistent with theoretical values (an average error of 4.6% ± 12.4 %). The framework was then tested on healthy and diseased carotid bifurcation models. In both scenarios, key spatiotemporal dynamics of WSR were noted: 1) oscillating shear patterns were present in the carotid bulb and downstream to the internal carotid artery (ICA) where retrograde flow occurs; and 2) high WSR was observed particularly in the diseased model where the measured WSR peaked at 810 [Formula: see text] due to flow jetting. We also showed that WASHI could consistently track arterial wall motion to map its WSR. Overall, WASHI enables high temporal resolution mapping of WSR that could facilitate investigations on causal effects between WSS and atherosclerosis.

Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo

An adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) algorithm for in vivo murine myocardial function assessment is presented. We report on 31 BALB/CJ mice (n = 17 females, n = 14 males), randomly stratified into three surgical groups: myocardial infarction (MI, n = 10), ischemia–reperfusion (IR, n = 13) and control (sham, n = 8) imaged pre-surgery (baseline- BL), and 1, 2, 7 and 14 days post-surgery using a high frequency ultrasound imaging system (Vevo 2100). End-systole (ES) radial and longitudinal strain images were used to generate cardiac fibrosis maps using binary thresholding. Percentage fibrotic myocardium (PFM) computed from regional fibrosis maps demonstrated statistically significant differences post-surgery in scar regions. For example, the MI group had significantly higher PFM_Radial (%) values in the anterior mid region (p = 0.006) at Day 14 (n = 8, 42.30 ± 14.57) compared to BL (n = 12, 1.32 ± 0.85). A random forest classifier automatically detected fibrotic regions from ground truth Masson’s trichrome stained histopathology whole slide images. Both PFM_Radial (r = 0.70) and PFM_Longitudinal (r = 0.60) results demonstrated strong, positive correlation with PFM_{Histopathology} (p < 0.001).

Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results

Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (p < 0.001). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (SNR_e) calculated using stochastic analysis. STBR-2 had the highest expected SNR_e both for radial and longitudinal strain. The mean expected SNR_e values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo.

Bayesian Regularized Strain Imaging for Assessment of Murine Cardiac Function In vivo

A cardiac strain imaging framework with adaptive Bayesian regularization (ABR) is proposed for in vivo assessment of murine cardiac function. The framework uses ultrasound (US) radio-frequency data collected with a high frequency (f_c = 30MHz) imaging system and a multi-level block matching algorithm with ABR to derive inter-frame cardiac displacements. Lagrangian cardiac strain (radial, e_r and longitudinal, e_l) tensors were derived by segmenting the myocardial wall starting at the ECG R-wave and accumulating interframe deformations over a cardiac cycle. In vivo feasibility was investigated through a longitudinal study with two mice (one ischemia-perfusion (IR) injury and one sham) imaged at five sessions (pre-surgery (BL) and 1,2,7 and 14 days post-surgery). End-systole (ES) strain images and segmental strain curves were derived for quantitative evaluation. Both mice showed periodic variation of e_r and e_l strain at BL with segmental synchroneity. Infarcted regions of IR mouse at Day 14 were associated with reduced or sign reversed ES e_r and e_l values while the sham mouse had similar or higher strain than at BL. Infarcted regions identified in vivo were associated with increased collagen content confirmed with Masson's Trichrome stained ex vivo heart sections.Clinical Relevance-Higher quality cardiac strain images derived with RF data and Bayesian regularization can potentially improve the sensitivity and accuracy of non-invasive assessment of cardiovascular disease models.

Differential Imaging of Liver Tumors before and after Microwave Ablation with Electrode Displacement Elastography

Liver cancer is a leading cause of cancer-related deaths; however, primary treatment options such as surgical resection and liver transplant may not be viable for many patients. Minimally invasive image-guided microwave ablation (MWA) provides a locally effective treatment option for these patients with an impact comparable to that of surgery for both cancer-specific and overall survival. MWA efficacy is correlated with accurate image guidance; however, conventional modalities such as B-mode ultrasound and computed tomography have limitations. Alternatively, ultrasound elastography has been used to demarcate post-ablation zones, yet has limitations for pre-ablation visualization because of variability in strain contrast between cancer types. This study attempted to characterize both pre-ablation tumors and post-ablation zones using electrode displacement elastography (EDE) for 13 patients with hepatocellular carcinoma or liver metastasis. Typically, MWA ablation margins of 0.5-1.0 cm are desired, which are strongly correlated with treatment efficacy. Our results revealed an average estimated ablation margin inner quartile range of 0.54-1.21 cm with a median value of 0.84 cm. These treatment margins lie within or above the targeted ablative margin, indicating the potential to use EDE for differentiating index tumors and ablated zones during clinical ablations. We also obtained a high correlation between corresponding segmented cross-sectional areas from contrast-enhanced computed tomography, the current clinical gold standard, when compared with EDE strain images, with r2 values of 0.97 and 0.98 for pre- and post-ablation regions.

Study of the Relationship Between Ultrasound Strain Indices and Cognitive Decline for Vulnerable Carotid Plaque

A relationship between ultrasound strain indices in carotid plaque to cognitive domains of executive and language function are studied in 42 symptomatic and 34 asymptomatic patients. The mean and standard deviation of the percentage stenosis were 72.10 ± 15.19 and 77.41 ± 11.20 for symptomatic and asymptomatic patients respectively. Pearson's correlation between axial, lateral and shear strain indices versus executive and language composite scores was performed.. A significant inverse correlation for both executive and language function for symptomatic patients to strain indices was found. On the other hand, for asymptomatic patients only executive function was inversely correlated with the corresponding strain indices. Our hypothesis that microemboli from vulnerable plaque and possible 'silent strokes' may be responsible for decline in executive function for both symptomatic and asymptomatic patients'. Strokes and transient ischemic attacks may be responsible for further cognitive decline in language function for symptomatic patients.

Carotid Plaque Strain Indices Were Correlated With Cognitive Performance in a Cohort With Advanced Atherosclerosis, and Traditional Doppler Measures Showed no Association

OBJECTIVES

Traditional Doppler measures have been used to predict cognitive performance in patients with carotid atherosclerosis. Novel measures, such as carotid plaque strain indices (CPSIs), have shown associations with cognitive performance. We hypothesized that lower mean middle cerebral artery (MCA) velocities, higher bulb-internal carotid artery (ICA) velocities, the MCA pulsatility index (PI), and CPSIs would be associated with poorer cognitive performance in individuals with advanced atherosclerosis.

METHODS

Neurocognitive testing, carotid ultrasound imaging, transcranial Doppler imaging, and carotid strain imaging were performed on 40 patients scheduled for carotid endarterectomy. Kendall tau correlations were used to examine relationships between cognitive tests and the surgical-side maximum peak systolic velocity (PSV; from the bulb, proximal, mid, or distal ICA), mean MCA velocity and PI, and maximum CPSIs (axial, lateral, and shear strain indices used to characterize plaque deformations with arterial pulsation). Cognitive measures included age-adjusted indices of verbal fluency, verbal and visual learning/memory, psychomotor speed, auditory attention/working memory, visuospatial construction, and mental flexibility.

RESULTS

Participants had a median age of 71.0 (interquartile range, 9.75) years; 26 were male (65%), and 14 were female (35%). Traditional Doppler parameters, PSV, mean MCA velocity, and MCA PI did not predict cognitive performance (all P > .05). Maximum CPSIs were significantly associated with cognitive performance (P < .05).

CONCLUSIONS

Traditional velocity measurements of the maximum bulb-ICA PSV, mean MCA velocity, and PI were not associated with cognitive performance in patients with advanced atherosclerotic disease; however, maximum CPSIs were associated with cognitive performance. These findings suggest that cognition may be associated with unstable plaque rather than blood flow.

Adaptation of Dictionary Learning for Electrode Displacement Elastography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:2023-2026. [PMID: 33018401 PMCID: PMC7538652 DOI: 10.1109/embc44109.2020.9175319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Microwave ablation has become a common treatment method for liver cancers. Unfortunately, microwave ablation success is correlated with clinician's ability for proper electrode placement and assess ablative margins, requiring accurate imaging of liver tumors and ablated zones. Conventionally, ultrasound and computed tomography are utilized for this purpose, yet both have their respective drawbacks. As an alternate approach, electrode displacement elastography offers promise but is still plagued by decorrelation artifacts reducing lesion depiction and visualization. A recent filtering method, namely dictionary representation, has improved contrast-to-noise ratios without reducing delineation contrast. As a supplement to this recent work, this paper evaluates adaptations on this initial dictionary-learning algorithm and applies them to an EDE phantom and 15 in-vivo patient datasets. Two new adaptations of dictionary representations were evaluated, namely a combined dictionary and magnitude-based dictionary representation. When comparing numerical results, the combined dictionary representation algorithm outperforms the previous developed dictionary representation in signal-to-noise (1.54 dB) and contrast-to-noise (0.67 dB) ratios, while a magnitude dictionary representation produces higher noise levels, but improves visualized strain tensor resolution.

Improving Ultrasound Lateral Strain Estimation Accuracy using Log Compression of Regularized Correlation Function

Normalized cross-correlation (NCC) function used in ultrasound strain imaging can get corrupted due to signal decorrelation inducing large displacement errors. Bayesian regularization has been applied in an iterative manner to regularize the NCC function and to reduce estimation variance and peak-hopping errors. However, incorrect choice of the number of iterations can lead to over-regularization errors. In this paper, we propose the use of log compression of regularized NCC function to improve subsample estimation. Performance of parabolic interpolation before and after log compression of the regularized NCC function were compared in numerical simulations of uniform and inclusion phantoms. Significant improvement was achieved with the proposed scheme for lateral estimation results. For example, lateral signal-to-noise ratio (SNR) was 10 dB higher after log compression at 3% strain in a uniform phantom. Lateral contrast-to-noise ratio (CNR) was 1.81 dB higher with proposed method at 3% strain in inclusion phantom. No significant difference was observed in axial estimation due to presence of phase information and high sampling frequency. Our results suggest that this simple approach makes Bayesian regularization robust to over-regularization artifacts.

Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging

Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p   <  0.001). Contrast-to-noise (CNR _e ) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNR _e of  -0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p   <  0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

Physiological Motion Reduction Using Lagrangian Tracking for Electrode Displacement Elastography

Minimally invasive treatments such as microwave ablation (MWA) have been growing in popularity for extending liver cancer survival rates in patients, when surgery is not an option. As a non-ionizing, real-time alternative to contrast-enhanced computed tomography, electrode displacement elastography (EDE) has shown promise as an imaging modality for MWA. Despite imaging efficacy, motion artifacts caused by physiological motion result in unintended speckle pattern variance, thereby inhibiting consistent and accurate ablated region visualization. To combat these unavoidable motion artifacts, a Lagrangian deformation tracking (LDT) approach based on freehand EDE was developed to track tissue movement and better define tissue properties. For validating LDT efficacy, a spherical inclusion phantom as well as seven in vivo data sets were processed, and strain tensor images were compared with identical time sampled images estimated using a traditional Eulerian approach. In vivo results revealed greater consistency among visualized LDT strain tensor images, with segmented ablated regions exhibiting standard deviation reductions of up to 98% when compared with Eulerian strain tensor images. Additionally, Lagrangian strain tensor images provided Dice coefficient improvements up to 25%, and success rates improved from approximately 50% to nearly 100% for ablated region visualization.

Hierarchical Motion Estimation With Bayesian Regularization in Cardiac Elastography: Simulation and In Vivo Validation

Cardiac elastography (CE) is an ultrasound-based technique utilizing radio-frequency (RF) signals for assessing global and regional myocardial function. In this work, a complete strain estimation pipeline for incorporating a Bayesian regularization-based hierarchical block-matching algorithm, with Lagrangian motion description and myocardial polar strain estimation is presented. The proposed regularization approach is validated using finite-element analysis (FEA) simulations of a canine cardiac deformation model that is incorporated into an ultrasound simulation program. Interframe displacements are initially estimated using a hierarchical motion estimation framework. Incremental displacements are then accumulated under a Lagrangian description of cardiac motion from end-diastole (ED) to end-systole (ES). In-plane Lagrangian finite strain tensors are then derived from the accumulated displacements. Cartesian to cardiac coordinate transformation is utilized to calculate radial and longitudinal strains for ease of interpretation. Benefits of regularization are demonstrated by comparing the same hierarchical block-matching algorithm with and without regularization. Application of Bayesian regularization in the canine FEA model provided improved ES radial and longitudinal strain estimation with statistically significant ( ) error reduction of 48.88% and 50.16%, respectively. Bayesian regularization also improved the quality of temporal radial and longitudinal strain curves with error reductions of 78.38% and 86.67% ( ), respectively. Qualitative and quantitative improvements were also visualized for in vivo results on a healthy murine model after Bayesian regularization. Radial strain elastographic signal-to-noise ratio (SNR_e) increased from 3.83 to 4.76 dB, while longitudinal strain SNR_e increased from 2.29 to 4.58 dB with regularization.

Lagrangian carotid strain imaging indices normalized to blood pressure for vulnerable plaque

OBJECTIVE

Ultrasound Lagrangian carotid strain imaging (LCSI) utilizes physiological deformation caused by arterial pressure variations to generate strain tensor maps of the vessel walls and plaques. LCSI has been criticized for the lack of normalization of magnitude-based strain indices to physiological stimuli, namely blood pressure. We evaluated the impact of normalization of magnitude-based strain indices to blood pressure measured immediately after the acquisition of radiofrequency (RF) data loops for LCSI.

MATERIALS AND METHODS

A complete clinical ultrasound examination along with RF data loops for LCSI was performed on 50 patients (30 males and 20 females) who presented with >60% carotid stenosis and were scheduled for carotid endarterectomy. Cognition was assessed using the 60-minute neuropsychological test protocol.

RESULTS

For axial strains correlation of maximum accumulated strain indices (MASI), cognition scores were -0.46 for non-normalized and -0.45, -0.49, -0.37, and -0.48 for systolic, diastolic, pulse pressure, and mean arterial pressure normalized data, respectively. The corresponding area under the curve (AUC) values for classifiers designed using maximum likelihood estimation of a binormal distribution with a median-split of the executive function cognition scores were 0.73, 0.70, 0.71, 0.70, and 0.71, respectively.

CONCLUSIONS

No significant differences in the AUC estimates were obtained between normalized and non-normalized magnitude-based strain indices.

In vivo carotid strain imaging using principal strains in longitudinal view

Carotid plaque rupture can result in stroke or transient ischemic attack that can be devastating for patients. Ultrasound strain imaging provides a noninvasive method to identify unstable plaque likely to rupture. Axial, lateral and shear strains in carotid plaque have been shown to be linked to carotid plaque instability. Recently, there has been interest in using principal strains, which do not depend on angle of insonification of the carotid artery for quantifying instability in plaque along the longitudinal view. In this work relationships between angle dependent axial, lateral and shear strain along with axis independent principal strains are compared. Three strain indices were defined, 1) Average Mean Strain (AMS), 2) Maximum Mean Strain (MMS) and 3) Mean Standard Deviation (MSD) to identify relationships between these five strain image types in a group of 76 in vivo patients. The maximum principal strain demonstrated the highest strain values when compared to axial strain for all patients with a linear regression slope of 1.6 and a y intercept of 2.4 percent strain for AMS. The maximum shear strain when compared to shear strain had a slope of 1.15 and a y intercept of 0.21 percent for AMS. Next, the effect of insonification angle, which is the angle subtended by the artery at the location of plaque was studied. Patients were divided into three sub groups, i.e. less than 5 degrees (n = 31), between 5 and 10 degrees (n = 24) and above 10 degrees (n = 21). The angle of insonification did not make a significant difference between the three angle groups when comparing the relationship between the angle dependent and independent strain values.

Dictionary Representations for Electrode Displacement Elastography

Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to monitor ablated regions in human patients after minimally invasive microwave ablation procedures. Displacement estimation for EDE is commonly plagued by decorrelation noise artifacts degrading displacement estimates. In this paper, we propose a global dictionary learning approach applied to denoising displacement estimates with an adaptively learned dictionary from EDE phantom displacement maps. The resulting algorithm is one that represents displacement patches sparsely if they contain low noise and averages remaining patches thereby denoising displacement maps while retaining important edge information. The results of dictionary-represented displacements presented with a higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) with improved contrast, as well as improved phantom inclusion delineation when compared to initial displacements, median-filtered displacements, and spline smoothened displacements, respectively. In addition to visualized noise reduction, dictionary-represented displacements presented with the highest SNR, CNR, and improved contrast with values of 1.77, 4.56, and 4.35 dB, respectively, when compared to axial strain tensor images estimated using the initial displacements. Following EDE phantom imaging, we utilized dictionary representations from in vivo patient data, further validating efficacy. Denoising displacement estimates are a newer application for dictionary learning producing strong ablated region delineation with little degradation from denoising.