Aktharuzzaman, Md

Accurate assessment of arterial wall mechanics is essential for detecting vascular pathologies. The technique described herein incorporates a commercially available ultrasound system and applanation tonometry to noninvasively evaluate the local mechanical properties of the common carotid artery (CCA). Pressure waveforms were acquired using a tonometer combined with ultrasound-based diameter of the CCA. The signals were temporally aligned, converted to physical units using brachial systolic and diastolic pressure, and interpolated over uniform strain increments. Hoop stress was estimated using thick-wall assumptions, and hoop strain was derived from mid-wall circumferential stretch. Incremental elastic modulus was computed from local linear fits to the stress–strain data, and the area of the resulting hysteresis loop was used as a surrogate for viscoelastic energy loss, quantified by the energy dissipation ratio (EDR). The method was applied to healthy subjects (HS) and those with cardiovascular risk or confirmed atherosclerosis. In HS, pressure–strain elastic modulus (Ep) ranged from 50–55 kPa and EDR from 2%–6%, while non-healthy subjects showed higher EDR values (10%–20%), reflecting increased arterial viscoelasticity. Results revealed nonlinear pressure–diameter relationships with clear hysteresis, reflecting viscoelastic effects. The technique offers potential as a diagnostic tool for evaluating early, localized vascular alterations.

The color Doppler twinkling artifact has been attributed to existing microbubbles or cavitation has been reported for objects with rough surfaces like kidney stones, sandpaper, and polymethyl methacrylate (PMMA) bone cement. Passive cavitation detection (PCD) is a method for determining the presence of bubble oscillation from subharmonic, ultraharmonic, and inharmonic components in the Fourier domain. We evaluated multiple PMMA samples with varying twinkling amounts as well as a non-twinkling metal rod. Ultrasound scans were conducted using a Verasonics L11-4v probe on a V1 system from 3–9 MHz Doppler transmit frequency. A Sonic Concepts Y-107 PCD probe connected to a JSR DPR300 pulser/receiver collected data during Doppler ultrasound transmission. The Fourier spectra showed increased magnitude trends in the midband frequency range between the fundamental, f, and second harmonic, 2f, on the twinkling samples compared to non-twinkling metal rod. The midband increase was highest at frequencies that showed the most twinkling. This work indicates that the presence of color Doppler twinkling signal on PMMA gives rise to additional peaks in the Fourier domain, particularly in the midband range. Additionally, as there were no distinct subharmonic peaks, the increased midband magnitudes could be due to cavitation on the rough surfaces of the PMMA.

Increased arterial stiffness is an early indicator of various cardiovascular diseases (CVD). Arterial Dispersion Ultrasound Vibrometry (ADUV) employs acoustic radiation force to induce arterial wall motion, enabling estimation of arterial wave velocity (i.e., phase velocity). This study investigates the correlation between ADUV-derived wave velocity and traditional arterial stiffness metrics, including distensibility, compliance, and stiffness index in the right and left common carotid arteries (CCAs). In a cohort of 59 subjects, categorized as (a) confirmed CVD (n = 27), (b) individuals with cardiovascular risk factors (n = 20), and (c) healthy controls (n = 12), we assessed arterial wave velocity across the cardiac cycle, split into 10 stages, to capture variations in mechanical properties. Univariate analysis demonstrated a statistically significant inverse linear relationship between ADUV-derivedwave velocity and distensibility for the right CCA during the systolic phase (cardiac stages 3–5) and diastolic phase (cardiac stages 7–9) (−0.35 < ρ < −0.50, p < 0.02). The wave velocity showed associations with compliance in some of these phases. No significant correlations were observed between wave velocity and the stiffness index. Our findings provide insights into the clinical relevance of ADUV-derived wave velocity as a biomarker for arterial stiffness and its potential to complement or enhance traditional stiffness assessments.

Abstract Full waveform inversion (FWI) is one of the advanced inversion-based ultrasound computed tomography (USCT) techniques that can identify the material properties and produce high-resolution inverted images of the specimen, making it promising for applications in nondestructive evaluation (NDE) and structural health monitoring (SHM). However, this process is computationally expensive and time-consuming due to its ill-posed nature and inherent nonlinearity. To mitigate these challenges and enhance the efficiency of FWI, this study explores an alternative approach using artificial intelligence (AI) as a data-driven regularization. However, one of the major challenges in such a data-driven approach is the inadequate amount of data to train the neural network. To address this bottleneck, 2D numerically generated samples are used to train the neural network in this study. In addition, two different source time functions (STF) were used for FWI simulations to understand the effect of using different waveforms on the deep-learning (DL) based solution. The first STF was numerically generated and the second was acquired by inverting experimental data using a multi-channel data acquisition system from the Verasonics. For evaluation, the structural similarity index measure (SSIM) and peak-signal noise ratio (PSNR) metrics are used to compare the predicted images with the ground truth images. The results demonstrate the efficacy of the DL-based approach, with improvements of 22.5% and 78% in SSIM and 47% and 73% in PSNR over the classical FWI simulations for the first and second STFs, respectively. The DL-based adjoint tomography architecture for USCT presented in this work has a strong potential for material characterization and detecting internal defects and variations.