Performance of acoustic radiation force impulse ultrasound imaging for carotid plaque characterization with histologic validation

Multi-spectral photoacoustic imaging combined with acoustic radiation force impulse imaging for applications in tissue engineering

Tissue engineering is a dynamic field focusing on the creation of advanced scaffolds for tissue and organ regeneration. These scaffolds are customized to their specific applications and are often designed to be complex, large structures to mimic tissues and organs. This study addresses the critical challenge of effectively characterizing these thick, optically opaque scaffolds that traditional imaging methods fail to fully image due to their optical limitations. We introduce a novel multi-modal imaging approach combining ultrasound, photoacoustic, and acoustic radiation force impulse imaging. This combination leverages its acoustic-based detection to overcome the limitations posed by optical imaging techniques. Ultrasound imaging is employed to monitor the scaffold structure, photoacoustic imaging is employed to monitor cell proliferation, and acoustic radiation force impulse imaging is employed to evaluate the homogeneity of scaffold stiffness. We applied this integrated imaging system to analyze melanoma cell growth within silk fibroin protein scaffolds with varying pore sizes and therefore stiffness over different cell incubation periods. Among various materials, silk fibroin was chosen for its unique combination of features including biocompatibility, tunable mechanical properties, and structural porosity which supports extensive cell proliferation. The results provide a detailed mesoscale view of the scaffolds' internal structure, including cell penetration depth and biomechanical properties. Our findings demonstrate that the developed multimodal imaging technique offers comprehensive insights into the physical and biological dynamics of tissue-engineered scaffolds. As the field of tissue engineering continues to advance, the importance of non-ionizing and non-invasive imaging systems becomes increasingly evident, and by facilitating a deeper understanding and better characterization of scaffold architectures, such imaging systems are pivotal in driving the success of future tissue-engineering solutions.

Novel ultrasound techniques in the identification of vulnerable plaques-an updated review of the literature

Atherosclerosis is an inflammatory disease partly mediated by lipoproteins. The rupture of vulnerable atherosclerotic plaques and thrombosis are major contributors to the development of acute cardiovascular events. Despite various advances in the treatment of atherosclerosis, there has been no satisfaction in the prevention and assessment of atherosclerotic vascular disease. The identification and classification of vulnerable plaques at an early stage as well as research of new treatments remain a challenge and the ultimate goal in the management of atherosclerosis and cardiovascular disease. The specific morphological features of vulnerable plaques, including intraplaque hemorrhage, large lipid necrotic cores, thin fibrous caps, inflammation, and neovascularisation, make it possible to identify and characterize plaques with a variety of invasive and non-invasive imaging techniques. Notably, the development of novel ultrasound techniques has introduced the traditional assessment of plaque echogenicity and luminal stenosis to a deeper assessment of plaque composition and the molecular field. This review will discuss the advantages and limitations of five currently available ultrasound imaging modalities for assessing plaque vulnerability, based on the biological characteristics of the vulnerable plaque, and their value in terms of clinical diagnosis, prognosis, and treatment efficacy assessment.

Spectral analysis of ultrasound backscatter for non-invasive measurement of plaque composition

Carotid atherosclerotic plaque composition may be an important indication of patient risk for future cerebrovascular events. Ultrasound spectral analysis has the potential to provide a robust measure of plaque composition in vivo if the backscatter transfer function can be sufficiently isolated from the effects of attenuation from overlying tissue, receive and transmit transfer functions from the ultrasound system and transducer, and diffraction. This study examines the usefulness of the nonlinearly generated second harmonic portion of the backscatter signal and the effects of a variety of attenuation compensation techniques for noninvasively characterizing human carotid plaque using spectral analysis and machine learning. Post-beamformed ultrasound backscatter radiofrequency (RF) data were acquired from 6 normal subjects and 119 carotid endarterectomy patients prior to surgery. Plaque obtained following surgery was histologically processed, and regions of interest (ROI) corresponding to homogenous tissue types (fibrous/fibro-lipidic, hemorrhagic and/or necrotic core and calcified) were selected from RF data. Both the harmonic and fundamental power spectra for each ROI was obtained and normalized by data from a uniform phantom (0.5 dB/cm-MHz slope of attenuation). Additional attenuation compensation approaches were compared to simply using the reference phantom: (1) optimum power spectral shift estimation, (2) one-step adventitial, or (3) two-step adventitial. Spectral parameters extracted from both the fundamental and harmonic estimates of the backscatter transfer function of 363 ROI's from 152 plaque specimens were used to train and test random forest and support vector machine classification models. The best results came from using spectral parameters derived from both the fundamental and second harmonic bands with a predictive accuracy of 65-68%, kappa statistic of 0.49-0.54, and accuracies of 84% for fibrous, 68-74% for hemorrhagic and/or necrotic core, and 78-81% for calcified ROI's. The result indicated that the nonlinearly generated second harmonic portion of backscatter is useful for carotid plaque tissue characterization and that a reference phantom approach with a 0.5 dB/cm-MHz slope of attenuation works as well as more complicated approaches.

Imaging of Single Transducer-Harmonic Motion Imaging-Derived Displacements at Several Oscillation Frequencies Simultaneously

Mapping of mechanical properties, dependent on the frequency of motion, is relevant in diagnosis, monitoring treatment response, or intra-operative surgical resection planning. While shear wave speeds at different frequencies have been described elsewhere, the effect of frequency on the "on-axis" acoustic radiation force (ARF)-induced displacement has not been previously investigated. Instead of generating single transducer-harmonic motion imaging (ST-HMI)-derived peak-to-peak displacement (P2PD) image at a particular frequency, a novel multi-frequency excitation pulse is proposed to generate P2PD images at 100-1000 Hz simultaneously. The performance of the proposed excitation pulse is compared with the ARFI by imaging 16 different inclusions (Young's moduli of 6, 9, 36, 70 kPa and diameters of 1.6, 2.5, 6.5, and 10.4 mm) embedded in an 18 kPa background. Depending on inclusion size and stiffness, the maximum CNR and contrast were achieved at different frequencies and were always higher than ARFI. The frequency, at which maximum CNR and contrast were achieved, increased with stiffness for fixed inclusion's size and decreased with size for fixed stiffness. In vivo feasibility is tested by imaging a 4T1 breast cancer mouse tumor on Day 6, 12, and 19 post-injection of tumor cells. Similar to phantoms, the CNR of ST-HMI images was higher than ARFI and increased with frequency for the tumor on Day 6. Besides, P2PD at 100-1000 Hz indicated that the tumor became stiffer with respect to the neighboring non-cancerous tissue over time. These results indicate the importance of using a multi-frequency excitation pulse to simultaneously generate displacement at multiple frequencies to better delineate inclusions or tumors.

Advances in Noninvasive Carotid Wall Imaging with Ultrasound: A Narrative Review

Carotid atherosclerosis is a major cause for stroke, with significant associated disease burden morbidity and mortality in Western societies. Diagnosis, grading and follow-up of carotid atherosclerotic disease relies on imaging, specifically ultrasound (US) as the initial modality of choice. Traditionally, the degree of carotid lumen stenosis was considered the sole risk factor to predict brain ischemia. However, modern research has shown that a variety of other imaging biomarkers, such as plaque echogenicity, surface morphology, intraplaque neovascularization and vasa vasorum contribute to the risk for rupture of carotid atheromas with subsequent cerebrovascular events. Furthermore, the majority of embolic strokes of undetermined origin are probably arteriogenic and are associated with nonstenosing atheromas. Therefore, a state-of-the-art US scan of the carotid arteries should take advantage of recent technical developments and should provide detailed information about potential thrombogenic (/) and emboligenic arterial wall features. This manuscript reviews recent advances in ultrasonographic assessment of vulnerable carotid atherosclerotic plaques and highlights the fields of future development in multiparametric arterial wall imaging, in an attempt to convey the most important take-home messages for clinicians performing carotid ultrasound.

Safety of arterial shear wave elastography-ex-vivoassessment of induced strain and strain rates

Shear wave elastography (SWE) is a promising technique for characterizing carotid plaques and assessing local arterial stiffness. The mechanical stress to which the tissue is subjected during SWE using acoustic radiation force (ARF), leading to strain at a certain strain rate, is still relatively unknown. Because SWE is increasingly used for arterial applications where the mechanical stress could potentially lead to significant consequences, it is important to understand the risks of SWE- induced strain and strain rate. The aim of this study was to investigate the safety of SWE in terms of induced arterial strain and strain rateex-vivoand in a human carotid arteryin-vivo. SWE was performed on six porcine aortae as a model of the human carotid artery using different combinations of ARF push parameters (push voltage: 60/90 V, aperture width: f/1.0/1.5, push length: 100/150/200 μs) and distance to push position. The largest induced strain and strain rate were 1.46 % and 54 s^-1(90 V, f/1.0, 200 μs), respectively. Moreover, the SWE-induced strains and strain rates increased with increasing push voltage, aperture, push length, and decreasing distance between the region of interest and the push. In the human carotid artery, the SWE-induced maximum strain was 0.06 % and the maximum strain rate was 1.58 s^-1, compared with the maximum absolute strain and strain rate of 12.61 % and 5.12 s^-1, respectively, induced by blood pressure variations in the cardiac cycle. Our results indicate thatex-vivoarterial SWE does not expose the artery to higher strain rate than normal blood pressure variations, and to strain one order of magnitude higher than normal blood pressure variations, at the push settings and distances from the region of interest used in this study.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Feasibility of Harmonic Motion Imaging Using a Single Transducer: In Vivo Imaging of Breast Cancer in a Mouse Model and Human Subjects

Harmonic motion imaging (HMI) interrogates the mechanical properties of tissues by simultaneously generating and tracking harmonic oscillation using focused ultrasound and imaging transducers, respectively. Instead of using two transducers, the objective of this work is to develop a single transducer HMI (ST-HMI) to both generate and track harmonic motion at "on-axis" to the force for facilitating data acquisition. In ST-HMI, the amplitude-modulated force was generated by modulating excitation pulse duration and tracking of motion was performed by transmitting tracking pulses interleaved between excitation pulses. The feasibility of ST-HMI was performed by imaging two elastic phantoms with three inclusions (N = 6) and comparing it with acoustic radiation force impulse (ARFI) imaging, in vivo longitudinal monitoring of 4T1, orthotropic breast cancer mice (N = 4), and patients (N = 3) with breast masses in vivo. Six inclusions with Young's moduli of 8, 10, 15, 20, 40, and 60 kPa were embedded in a 5 kPa background. The ST-HMI-derived peak-to-peak displacement (P2PD) successfully detected all inclusions with [Formula: see text] of the linear regression between the P2PD ratio of background to inclusion versus Young's moduli ratio of inclusion to background. The contrasts of 10 and 15 kPa inclusions were higher in ST-HMI than ARFI-derived images. In the mouse study, the median P2PD ratio of tumor to non-cancerous tissues was 3.0, 5.1, 6.1, and 7.7 at 1, 2, 3, and 4 weeks post-injection of the tumor cells, respectively. In the clinical study, ST-HMI detected breast masses including fibroadenoma, pseudo angiomatous stromal hyperplasia, and invasive ductal carcinoma with a P2PD ratio of 1.37, 1.61, and 1.78, respectively. These results indicate that ST-HMI can assess the mechanical properties of tissues via generation and tracking of harmonic motion "on-axis" to the ARF. This study is the first step towards translating ST-HMI in clinics.

Electronic Point Spread Function Rotation Using a Three-Row Transducer for ARFI-Based Elastic Anisotropy Assessment: In Silico and Experimental Demonstration

Degree of anisotropy (DoA) of mechanical properties has been assessed as the ratio of acoustic radiation force impulse (ARFI)-induced peak displacements (PDs) achieved using spatially asymmetric point spread functions (PSFs) that are rotated 90° to each other. Such PSF rotation has been achieved by manually rotating a linear array transducer, but manual rotation is cumbersome and prone to misalignment errors and higher variability in measurements. The purpose of this work is to evaluate the feasibility of electronic PSF rotation using a three-row transducer, which will reduce variability in DoA assessment. A Siemens 9L4, with 3×192 elements, was simulated in Field II to generate spatially asymmetric ARFI PSFs that were electronically rotated 63° from each other. Then, using the finite element method (FEM), PD due to the ARFI excitation PSFs in 42 elastic, incompressible, transversely isotropic (TI) materials with shear moduli ratios of 1.0-6.0 were modeled. Finally, the ratio of PDs achieved using the two rotated PSFs was evaluated to assess elastic DoA. DoA increased with increasing shear moduli ratios and distinguished materials with 17% or greater difference in shear moduli ratios (Wilcoxon, ). Experimentally, the ratio of PDs achieved using ARFI PSF rotated 63° from each other distinguished the biceps femoris muscle from two pigs, which had median shear moduli ratios of 4.25 and 3.15 as assessed by shear wave elasticity imaging (SWEI). These results suggest that ARFI-based DoA assessment can be achieved without manual transducer rotation using a three-row transducer capable of electronically rotating PSFs by 63°. It is expected that electronic PSF rotation will facilitate data acquisitions and improve the reproducibility of elastic anisotropy assessments.

Carotid Plaque Fibrous Cap Thickness Measurement by ARFI Variance of Acceleration: In Vivo Human Results

This study evaluates the performance of an acoustic radiation force impulse (ARFI)-based outcome parameter, the decadic logarithm of the variance of acceleration, or log(VoA), for measuring carotid fibrous cap thickness. Carotid plaque fibrous cap thickness measurement by log(VoA) was compared to that by ARFI peak displacement (PD) in patients undergoing clinically indicated carotid endarterectomy using a spatially-matched histological validation standard. Fibrous caps in parametric log(VoA) and PD images were automatically segmented using a custom clustering algorithm, and a pathologist with expertise in atherosclerosis hand-delineated fibrous caps in histology. Over 10 fibrous caps, log(VoA)-derived thickness was more strongly correlated to histological thickness than PD-derived thickness, with Pearson correlation values of 0.98 for log(VoA) compared to 0.89 for PD. The log(VoA)-derived cap thickness also had better agreement with histology-measured thickness, as assessed by the concordance correlation coefficient (0.95 versus 0.62), and, by Bland-Altman analysis, was more consistent than PD-derived fibrous cap thickness. These results suggest that ARFI log(VoA) enables improved discrimination of fibrous cap thickness relative to ARFI PD and further contributes to the growing body of evidence demonstrating ARFI's overall relevance to delineating the structure and composition of carotid atherosclerotic plaque for stroke risk prediction.

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.

Assessment of Carotid Artery Plaque Components With Machine Learning Classification Using Homodyned-K Parametric Maps and Elastograms

Quantitative ultrasound (QUS) imaging methods, including elastography, echogenicity analysis, and speckle statistical modeling, are available from a single ultrasound (US) radio-frequency data acquisition. Since these US imaging methods provide complementary quantitative tissue information, characterization of carotid artery plaques may gain from their combination. Sixty-six patients with symptomatic ( n = 26 ) and asymptomatic ( n = 40 ) carotid atherosclerotic plaques were included in the study. Of these, 31 underwent magnetic resonance imaging (MRI) to characterize plaque vulnerability and quantify plaque components. US radio-frequency data sequence acquisitions were performed on all patients and were used to compute noninvasive vascular US elastography and other QUS features. Additional QUS features were computed from three types of images: homodyned-K (HK) parametric maps, Nakagami parametric maps, and log-compressed B-mode images. The following six classification tasks were performed: detection of 1) a small area of lipid; 2) a large area of lipid; 3) a large area of calcification; 4) the presence of a ruptured fibrous cap; 5) differentiation of MRI-based classification of nonvulnerable carotid plaques from neovascularized or vulnerable ones; and 6) confirmation of symptomatic versus asymptomatic patients. Feature selection was first applied to reduce the number of QUS parameters to a maximum of three per classification task. A random forest machine learning algorithm was then used to perform classifications. Areas under receiver-operating curves (AUCs) were computed with a bootstrap method. For all tasks, statistically significant higher AUCs were achieved with features based on elastography, HK parametric maps, and B-mode gray levels, when compared to elastography alone or other QUS alone ( ). For detection of a large area of lipid, the combination yielding the highest AUC (0.90, 95% CI 0.80-0.92, ) was based on elastography, HK, and B-mode gray-level features. To detect a large area of calcification, the highest AUC (0.95, 95% CI 0.94-0.96, ) was based on HK and B-mode gray level features. For other tasks, AUCs varied between 0.79 and 0.97. None of the best combinations contained Nakagami features. This study shows the added value of combining different features computed from a single US acquisition with machine learning to characterize carotid artery plaques.

Delineation of Human Carotid Plaque Features In Vivo by Exploiting Displacement Variance

While in vivo acoustic radiation force impulse (ARFI)-induced peak displacement (PD) has been demonstrated to have high sensitivity and specificity for differentiating soft from stiff plaque components in patients with carotid plaque, the parameter exhibits poorer performance for distinguishing between plaque features with similar stiffness. To improve discrimination of carotid plaque features relative to PD, we hypothesize that signal correlation and signal-to-noise ratio (SNR) can be combined, outright or via displacement variance. Plaque feature detection by displacement variance, evaluated as the decadic logarithm of the variance of acceleration and termed "log(VoA)," was compared to that achieved by exploiting SNR, cross correlation coefficient, and ARFI-induced PD outcome metrics. Parametric images were rendered for 25 patients undergoing carotid endarterectomy, with spatially matched histology confirming plaque composition and structure. On average, across all plaques, log(VoA) was the only outcome metric with values that statistically differed between regions of lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Further, log(VoA) achieved the highest contrast-to-noise ratio (CNR) for discriminating between LRNC and IPH, COL and CAL, and grouped soft (LRNC and IPH) and stiff (COL and CAL) plaque components. More specifically, relative to the previously demonstrated ARFI PD parameter, log(VoA) achieved 73% higher CNR between LRNC and IPH and 59% higher CNR between COL and CAL. These results suggest that log(VoA) enhances the differentiation of LRNC, IPH, COL, and CAL in human carotid plaques, in vivo, which is clinically relevant to improving stroke risk prediction and medical management.

ARFI variance of acceleration (VoA) for noninvasive characterization of human carotid plaques in vivo

Rather than degree of stenosis, assessing plaque structure and composition is relevant to discerning risk for plaque rupture with downstream ischemic event. The structure and composition of carotid plaque has been assessed noninvasively using Acoustic Radiation Force Impulse (ARFI) ultrasound imaging. In particular, ARFI-derived peak displacement (PD) estimations have been demonstrated for discriminating soft (lipid rich necrotic core (LRNC) or intraplaque hemorrhage (IPH)) from stiff (collagen (COL) or calcium (CAL)) plaque features; however, PD did not differentiate LRNC from IPH or COL from CAL. The purpose of this study is to evaluate a new ARFI-based measurement, the variance of acceleration (VoA), for differentiating among soft and stiff plaque components. Both PD and VoA results were obtained in vivo for a human carotid plaque acquired in a previous study and matched to a histological standard analyzed by a pathologist. With VoA, plaque feature contrast was increased by an average of 60% in comparison to PD.