Mechanical Anisotropy Assessment in Kidney Cortex Using ARFI Peak Displacement: Preclinical Validation and Pilot In Vivo Clinical Results in Kidney Allografts

Shear Wave Elastography for Assessment of Biopsy-Proven Renal Fibrosis: A Systematic Review and Meta-analysis

The purpose of this meta-analysis was to evaluate the diagnostic performance of shear wave elastography (SWE) for the staging of renal fibrosis in patients with chronic kidney disease (CKD). Classification of CKD into mild, moderate and severe fibrosis was based on renal biopsy pathology (glomerulosclerosis, tubulointerstitial injury and vascular sclerosis). The Cochrane Library, Medline, PubMed, Web of Science, EMBASE and CNKI databases were searched from January 1, 2009, to April 20, 2022. Pooled sensitivity, specificity, diagnostic odds ratio and area under the receiver operating characteristic curve (AUROC) were calculated using random effects models. A total of 1394 patients from 14 studies were included in the final analysis. For mild, moderate and severe renal fibrosis, SWE had a sensitivity of 0.79 (95% confidence interval [CI]: 0.67-0.88), 0.73 (95% CI: 0.65-0.80) and 0.87 (95% CI: 0.71-0.95); a specificity of 0.82 (95% CI: 0.75-0.87), 72% (95% CI: 0.67-0.77) and 0.83 (95% CI: 0.80-0.86); an AUROC of 0.87 (95% CI: 0.84-0.90), 0.78 (95% CI: 0.75-0.82) and 0.86 (95% CI: 0.82-0.88); and a diagnostic odds ratio of 17 (95% CI: 7-43), 7 (95% CI: 4-12) and 34 (95% CI: 13-88), respectively. Meta-regressions revealed that the publication date, system used and number of valid measurements of SWE were the main causes of heterogeneity. SWE is a good technique for diagnosing mild and severe renal fibrosis, as well as a fair technique for diagnosing moderate fibrosis.

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.

Quantitative Estimation of Mechanical Anisotropy Using Acoustic Radiation Force (ARF)-Induced Peak Displacements (PD): In Silico and Experimental Demonstration

Elastic degree of anisotropy (DoA) is a diagnostically relevant biomarker in muscle, kidney, breast, and other organs. Previously, elastic DoA was qualitatively assessed as the ratio of peak displacements (PD) achieved with the long-axis of a spatially asymmetric Acoustic Radiation Force Impulse (ARFI) excitation point spread function (PSF) aligned along versus across the axis of symmetry (AoS) in transversely isotropic materials. However, to better enable longitudinal and cross-sectional analyses, a quantitative measure of elastic DoA is desirable. In this study, qualitative ARFI PD ratios are converted to quantitative DoA, measured as the ratio of longitudinal over transverse shear elastic moduli, using a model empirically derived from Field II and finite element method (FEM) simulations. In silico, the median absolute percent error (MAPE) in ARFI-derived shear moduli ratio (SMR) was 1.75%, and predicted SMRs were robust to variations in transverse shear modulus, Young's moduli ratio, speed of sound, attenuation, density, and ARFI excitation PSF dimension. Further, ARFI-derived SMRs distinguished two materials when the true SMRs of the compared materials differed by as little as 10%. Experimentally, ARFI-derived SMRs linearly correlated with the corresponding ratios measured by Shear Wave Elasticity Imaging (SWEI) in excised pig skeletal muscle ( [Formula: see text], MAPE = 13%) and in pig kidney, in vivo ( [Formula: see text], MAPE = 5.3%). These results demonstrate the feasibility of using the ARFI PD to quantify elastic DoA in biological tissues.

Kidney Ultrasound for the Nephrologist: A Review

Ultrasound imaging is a key investigatory step in the evaluation of chronic kidney disease and kidney transplantation. It uses nonionizing radiation, is noninvasive, and generates real-time images, making it the ideal initial radiographic test for patients with abnormal kidney function. Ultrasound enables the assessment of both structural (form and size) and functional (perfusion and patency) aspects of kidneys, both of which are especially important as the disease progresses. Ultrasound and its derivatives have been studied for their diagnostic and prognostic significance in chronic kidney disease and kidney transplantation. Ultrasound is rapidly growing more widely accessible and is now available even in handheld formats that allow for bedside ultrasound examinations. Given the trend toward ubiquity, the current use of kidney ultrasound demands a full understanding of its breadth as it and its variants become available. We described the current applications and future directions of ultrasound imaging and its variants in the context of chronic kidney disease and transplantation in this review.

Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease

Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits.

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.

A 1.5-D Array for Acoustic Radiation Force (ARF)-Induced Peak Displacement-Based Tissue Anisotropy Assessment With a Row-Column Excitation Method

Many biological tissues, including muscle or kidney, are mechanically anisotropic, and the degree of anisotropy (DoA) in mechanical properties is diagnostically relevant. DoA can be assessed either using the ratio of shear wave velocities (SWVs) or acoustic radio forced impulse (ARFI)-induced peak displacements (PD) measured longitudinal over transverse orientations. Whether using SWV or PD as a basis, DoA expressed as the ratio of values requires 90° transducer rotation when a linear array is employed. This large rotation angle is prone to misalignment errors. One solution is the use of a fully sampled matrix array for electronic rotation of point spread function (PSF). However, the challenges of matrix array are its high fabrication cost and complicated fabrication procedures. The cheaper and simpler alternative of matrix array is the use of a row-column array. A 3×64 elements 1.5-D array with a row-column excitation mode is proposed to assess DoA in mechanical properties using the PD ratio. Different numbers of elements in elevational and lateral directions were selected to have orthogonal ARFI excitation beams without rotating the transducer. A custom-designed flex circuit was used to fabricate the array with a simpler electrode connection than a fully sampled matrix array. The performance of the array was evaluated in Field II simulation and experiment. The output pressure was 0.57-MPa output under a 40- [Formula: see text] excitation with a -6-dB point spread dimension of 14×4 mm2 in orthogonal directions. The PD was measured to be [Formula: see text] in an isotropic elastic phantom with Young's modulus of 5.4 kPa. These results suggest that the array is capable of assessing DoA using PD ratio without physical rotation of the transducer. The array has the potential to reduce the misalignment errors for DoA assessment.

Hydrophone Spatial Averaging Correction for Acoustic Exposure Measurements From Arrays-Part I: Theory and Impact on Diagnostic Safety Indexes

This article reports underestimation of mechanical index (MI) and nonscanned thermal index for bone near focus (TIB) due to hydrophone spatial averaging effects that occur during acoustic output measurements for clinical linear and phased arrays. TIB is the appropriate version of thermal index (TI) for fetal imaging after ten weeks from the last menstrual period according to the American Institute of Ultrasound in Medicine (AIUM). Spatial averaging is particularly troublesome for highly focused beams and nonlinear, nonscanned modes such as acoustic radiation force impulse (ARFI) and pulsed Doppler. MI and variants of TI (e.g., TIB), which are displayed in real-time during imaging, are often not corrected for hydrophone spatial averaging because a standardized method for doing so does not exist for linear and phased arrays. A novel analytic inverse-filter method to correct for spatial averaging for pressure waves from linear and phased arrays is derived in this article (Part I) and experimentally validated in a companion article (Part II). A simulation was developed to estimate potential spatial-averaging errors for typical clinical ultrasound imaging systems based on the theoretical inverse filter and specifications for 124 scanner/transducer combinations from the U.S. Food and Drug Administration (FDA) 510(k) database from 2015 to 2019. Specifications included center frequency, aperture size, acoustic output parameters, hydrophone geometrical sensitive element diameter, etc. Correction for hydrophone spatial averaging using the inverse filter suggests that maximally achievable values for MI, TIB, thermal dose ( t ₄₃ ), and spatial-peak-temporal-average intensity ( [Formula: see text]) for typical clinical systems are potentially higher than uncorrected values by (means ± standard deviations) 9% ± 4% (ARFI MI), 19% ± 15% (ARFI TIB), 50% ± 41% (ARFI t ₄₃ ), 43% ± 39% (ARFI [Formula: see text]), 7% ± 5% (pulsed Doppler MI), 15% ± 11% (pulsed Doppler TIB), 42% ± 31% (pulsed Doppler t ₄₃ ), and 33% ± 27% (pulsed Doppler [Formula: see text]). These values correspond to frequencies of 3.2 ± 1.3 (ARFI) and 4.1 ± 1.4 MHz (pulsed Doppler), and the model predicts that they would increase with frequency. Inverse filtering for hydrophone spatial averaging significantly improves the accuracy of estimates of MI, TIB, t ₄₃ , and [Formula: see text] for ARFI and pulsed Doppler signals.

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.

Comparison of Supersonic Shear Wave Imaging-Derived Renal Parenchyma Stiffness Between Diabetes Mellitus Patients With and Without Diabetic Kidney Disease

This study aims to evaluate the difference in renal parenchyma stiffness assessed by measuring Young's modulus (YM) using a supersonic shear wave imaging (SSI) technique among healthy patients and patients with type 2 diabetes mellitus (DM) with and without diabetic kidney disease (DKD). We analyzed the correlations of YM with clinical information and conventional ultrasound parameters. All patients (N = 124) were divided into three groups: (i) healthy patients (patients without kidney disease or type 2 DM, N = 31); (ii) patients with type 2 DM without kidney disease (N = 38); and (iii) patients with DKD (N = 55). Conventional and SSI ultrasound examinations were performed in all individuals for both kidneys. Then, we recorded renal length, width, parenchyma thickness, interlobar arterial resistive index (RI) and values of mean, mininum and maximum YM. The mean values of these parameters for the left and right kidneys were calculated for statistical analysis. Statistical significance was considered at p < 0.05. Among all ultrasound parameters, the mean YM demonstrated the largest area under the receiver operating characteristic (ROC) curve (0.860). The areas under the ROC curve (AUCs) for renal length, width, parenchyma thickness, interlobar arterial RI, minimum YM and maximum YM were 0.493, 0.616, 0.507, 0.733, 0.848 and 0.794, respectively. The corresponding cutoff value of mean YM was 31.73 kPa, with a sensitivity of 85.5% and a specificity of 71.0%. The mean YM in patients with type 2 DM without kidney disease (31.44 ± 3.83 kPa) was significantly higher than that in the healthy group (26.45 ± 4.32 kPa) and lower than that in the DKD group (37.60 ± 6.56 kPa). Patients with type 2 DM without kidney disease were considered as stage 0 of DKD. Thus, the mean YM in the control group was significantly lower than that in the stage 0, 2, 3, 4 and 5 subgroups. The mean YM in the stage 0-2 subgroups was lower than that in the stage 5 group, and the mean YM in the stage 0 group was lower than that in the stage 4 group. In the DKD group, the mean YM had a positive correlation with cystine-c (r = 0.634), urea (r = 0.596), creatine (r = 0.690), uric acid (r = 0.263), albumin/creatinine ratio (r = 0.428) and the presence or absence of diabetic retinopathy (r = 0.354). The mean YM also had a negative correlation with the estimated glomerular filtration rate (r = -0.657). SSI is a non-invasive method with which to diagnose DKD and has a performance superior to that of conventional ultrasound. In addition, SSI may provide a secondary index for the staging of DKD and the monitoring of renal damage in type 2 DM patients.