Citations

Key Points Robotic ultrasound performed favorably compared with magnetic resonance imaging for evaluating total kidney volume. Collagen evaluation by two novel methods of picrosirius red imaging were more informative than the standard method by brightfield imaging. Findings can improve research by increasing speed and access to total kidney volume determination and sensitivity of collagen assessment. Background 3D imaging and histology are critical tools for assessing polycystic kidney disease (PKD) in patients and animal models. Magnetic resonance (MR) imaging provides micron resolution but is time consuming and expensive, and access to equipment and expertise is limited. Robotic ultrasound (US) imaging has lower spatial resolution but is faster, more cost-effective, and accessible. Similarly, picrosirius red (PSR) staining and brightfield microscopy are commonly used to assess fibrosis; however, alternative methods have been shown in non-kidney tissues to provide greater sensitivity and more detailed structural characterization. Methods In this study, we evaluated the utility of robotic US and alternative methods of quantifying PSR staining for PKD research. We compared longitudinal total kidney volume measurements using US and MR imaging. We additionally compared PSR imaging and quantification using standard brightfield microscopy with that by circularly polarized light with hue analysis and fluorescence imaging analyzed using curvelet transform fiber extraction software for automatic detection of individual collagen fibers. Results Increased total kidney volume was detected by US in Pkd1 RC/RC versus wild-type (WT) at time points spanning from early to established disease. US interobserver variability was greater but allowed scanning in 2–5 minutes/mouse, whereas MR imaging required 20–30 minutes/mouse. While no change in fibrotic index was detected in this cohort of relatively mild disease using brightfield microscopy, polarized light showed fibers skewed thinner in Pkd1 RC/RC versus WT. Fluorescence imaging showed a higher density of collagen fibers in Pkd1 RC/RC versus WT, and fibers were thinner and curvier with no change in length. In addition, fiber density was higher in both glomeruli and tubules in Pkd1 RC/RC , and glomeruli had a higher fiber density than tubules in Pkd1 RC/RC and trended higher in WT. Conclusions These studies show robotic US is a rigorous imaging tool for preclinical PKD research. In addition, they demonstrate the increased sensitivity of polarized and fluorescence analysis of PSR-stained collagen.

Ultrasound shear wave elastography (SWE) is a useful technique for non-invasive tissue assessment. For effective quantitative analysis the employment of physical models is necessary for rheological parameter estimation, such as the Kelvin-Voigt (KV) model. From SWE data, the frequency response can be calculated, and the shear wave velocity dispersion can be fit, yielding the shear elasticity and shear viscosity, μ1 and μ2, respectively. In this study, a parametric evaluation is performed using staggered-grid finite-difference simulations of materials with μ1 = 1–25 kPa (increment: 1 kPa) and μ2 = 0–10 Pa s (increment: 0.5 Pa s), with an acoustic radiation force push of f-number equal to 2. The novel Stockwell transform combined with a slant frequency-wavenumber analysis (GST-SFK) was compared with the two-dimensional Fourier transform for dispersion curve estimation over a variety of frequency ranges. Regardless of dispersion curve estimation technique, the accuracy of estimating μ1 is confounded for μ2 values above 5 Pa s. It was found that KV fitting benefits from wider frequency ranges (up to 1kHz), with lower μ1 and μ2 estimation errors. The GST-SFK was the best dispersion curve calculation technique, yet the KV fitting process was found to be unreliable for parameter estimation in highly viscous media.

End-stage kidney disease is the result of progressive declines in function and frequently requires a transplant. Maintaining kidney transplant health with regularly scheduled function tests or biopsies is necessary to adjust treatments. Ultrasound shear wave elastography (SWE) could provide an inexpensive, noninvasive modality to monitor allograft health. In this project, we built models of the kidney cortex from segmented kidney transplant histology images. We assigned viscoelastic properties, based on a Kelvin-Voigt model, to the major constituents, glomeruli, interstitia, tubules, and fluid and simulated the wave propagation through tissue with no disease, inflammation, interstitial fibrosis, and tubular atrophy. The resulting in silico wave motion in the time- and frequency-domains was characterized to compare the in silico and in vivo mechanical properties. We also attempted modeling the effects of perfusion by modifying the constituents’ viscoelastic properties. We found the most differentiation for shear elasticity, shear viscosity, and phase velocity dispersion in the inflammation patients. When accounting for perfusion we observed increases in shear wave group velocity, shear viscosity, and dispersion, bringing the results from in silico models in closer agreement with in vivo results. These results are encouraging and demonstrate first steps of simulating the shear wave propagation based on histological models.

Renal transplant is a long-term therapy for patients in end-stage renal disease. Renal transplant health is monitored using laboratory tests and renal biopsy. However, renal biopsy is invasive and has issues with sampling errors. Ultrasound-based shear wave elastography (SWE) is being evaluated in renal transplants to identify patients with early stages of interstitial fibrosis and inflammation. We are exploring the use of new image reconstruction approaches such as local phase velocity imaging (LPVI) to evaluate viscoelasticity in a cohort of 23 renal transplants. Additionally, from point SWE measurements we have compiled results from 97 patients and have used various machine learning approaches to differentiate patients without disease such as interstitial fibrosis, inflammation, or interstitial fibrosis with tubular atrophy (IFTA). The LPVI method provides two-dimensional maps of phase velocity and images of viscoelastic parameters after fitting to a rheological model. The machine learning approaches combine multiple elastographic parameters to provide differentiation with high sensitivity, specificity, and accuracy (∼ 85%–90%). These approaches could have substantial benefit for patients for identifying early disease in renal transplants that my precede changes in laboratory test results.

Abstract Diastolic dysfunction causes close to half of congestive heart failures and is associated with increased stiffness in left-ventricular myocardium. A clinical tool capable of measuring viscoelasticity of the myocardium could be beneficial in clinical settings. We used Lamb wave Dispersion Ultrasound Vibrometry (LDUV) for assessing the feasibility of making in vivo non-invasive measurements of myocardial elasticity and viscosity in pigs. In vivo open-chest measurements of myocardial elasticity and viscosity obtained using a Fourier space based analysis of Lamb wave dispersion are reported. The approach was used to perform ECG-gated transthoracic in vivo measurements of group velocity, elasticity and viscosity throughout a single heart cycle. Group velocity, elasticity and viscosity in the frequency range 50–500 Hz increased from diastole to systole, consistent with contraction and relaxation of the myocardium. Systolic group velocity, elasticity and viscosity were 5.0 m s −1 , 19.1 kPa, 6.8 Pa · s, respectively. In diastole, the measured group velocity, elasticity and viscosity were 1.5 m s −1 , 5.1 kPa and 3.2 Pa · s, respectively.