Mechanical Anisotropy Assessment in Kidney Cortex Using ARFI Peak Displacement: Preclinical Validation and Pilot &lt;italic&gt;In Vivo&lt;/italic&gt; Clinical Results in Kidney Allografts

Hossain, Murad; Detwiler, Randal K.; Chang, Emily; Caughey, Melissa C.; Fisher, Melrose; Nichols, Timothy C.; Merricks, Elizabeth P.; Raymer, Robin A.; Whitford, Margaret H.; Bellinger, Dwight A.; Wimsey, Lauren E.; Gallippi, Caterina M.

doi:10.1109/tuffc.2018.2865203

Cited by 33 publications

(5 citation statements)

References 38 publications

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“…Variations of this quantity as a function of the probe orientation were seen to correlate with anisotropy in shear moduli [26]. This approach showed promising results, for example, for monitoring the status of renal transplant in humans [27].…”

Section: Studies Addressing This Topic Have Only Focused On In Vitromentioning

confidence: 89%

Toward speed-of-sound anisotropy quantification in muscle with pulse-echo ultrasound

Martiartu¹,

Simutė²,

Jaeger³

et al. 2022

Preprint

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<p>The velocity of ultrasound longitudinal waves (speed of sound) is emerging as a valuable biomarker for a wide range of diseases, including musculoskeletal disorders. Muscles are fiber-rich tissues that exhibit anisotropic behavior, meaning that velocities vary with the wave-propagation direction. Therefore, quantifying anisotropy is essential to improve velocity estimates while providing a new metric related to muscle composition and architecture. For the first time, this work presents a method to estimate speed-of-sound anisotropy in transversely isotropic tissues using pulse-echo ultrasound. We assume elliptical anisotropy and consider an experimental setup with a flat reflector parallel to the linear probe, with the muscle in between. This setup allows us to measure first-arrival reflection traveltimes using multistatic operation. Unknown muscle parameters are the orientation angle of the anisotropy symmetry axis and the velocities along and across this axis. We derive analytical expressions for the nonlinear relationship between traveltimes and anisotropy parameters, including reflector inclinations. These equations are exact for homogeneous media and are useful to estimate the effective average anisotropy in muscles. To analyze the structure of this forward problem, we formulate the inversion statistically using the Bayesian framework. We demonstrate that anisotropy parameters can be uniquely constrained by combining traveltimes from different reflector inclinations. Numerical results from wide-ranging acquisition and anisotropy properties show that uncertainties in velocity estimates are substantially lower than expected velocity differences in the muscle. Thus, our approach could provide meaningful muscle anisotropy estimates in future clinical applications.</p>

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Section: Studies Addressing This Topic Have Only Focused On In Vitromentioning

confidence: 89%

Toward speed-of-sound anisotropy quantification in muscle with pulse-echo ultrasound

Martiartu¹,

Simutė²,

Jaeger³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Variations of this quantity as a function of the probe orientation was seen to correlate with anisotropy in shear moduli [26]. This approach showed promising results, for example, for monitoring the status of renal transplant in humans [27].…”

Section: Introductionmentioning

confidence: 88%

Toward Speed-of-Sound Anisotropy Quantification in Muscle With Pulse-Echo Ultrasound

Martiartu¹,

Simutė²,

Jaeger

et al. 2022

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The velocity of ultrasound longitudinal waves (speed of sound) is emerging as a valuable biomarker for a wide range of diseases, including musculoskeletal disorders. Muscles are fiber-rich tissues that exhibit anisotropic behavior, meaning that velocities vary with the wave-propagation direction. Therefore, quantifying anisotropy is essential to improve velocity estimates while providing a new metric related to muscle composition and architecture. For the first time, this work presents a method to estimate speed-of-sound anisotropy in transversely isotropic tissues using pulse-echo ultrasound. We assume elliptical anisotropy and consider an experimental setup with a flat reflector parallel to the linear probe, with the muscle in between. This setup allows us to measure first-arrival reflection traveltimes using multistatic operation. Unknown muscle parameters are the orientation angle of the anisotropy symmetry axis and the velocities along and across this axis. We derive analytical expressions for the nonlinear relationship between traveltimes and anisotropy parameters, including reflector inclinations. These equations are exact for homogeneous media and are useful to estimate the effective average anisotropy in muscles. To analyze the structure of this forward problem, we formulate the inversion statistically using the Bayesian framework. We demonstrate that anisotropy parameters can be uniquely constrained by combining traveltimes from different reflector inclinations. Numerical results from wide-ranging acquisition and anisotropy properties show that uncertainties in velocity estimates are substantially lower than expected velocity differences in the muscle. Thus, our approach could provide meaningful muscle anisotropy estimates in future clinical applications.

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“…A quick intelligent clinical decisions are framed to help medical practitioners to find out the disease in primitive stages and thereby makes the treatment cheaper. This machine learning study is used in this model to detect lung cancer and its stage detection which is propounded by S Mukherjee, S Bohra in 2020 [8].…”

Section: Litreture Reviewmentioning

confidence: 99%

Machine Learning Methodology for Prediction of Chronic Kidney Disease

2023

IJFMR

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Chronic Kidney Disease (CKD) is a major health problem affecting millions of people worldwide. Early and accurate diagnosis of CKD is essential for successful management and treatment of the disease. In this paper, we propose a machine learning-based approach for diagnosing CKD using different classification algorithms. Our approach utilizes a combination of demographic data, medical history, and laboratory test results to predict CKD. We tested our approach using several machine learning algorithms, including decision trees, random forests, and support vector machines (SVM), and compared our results with traditional diagnostic methods. Our results show that SVM achieved the highest accuracy in diagnosing CKD, followed by decision trees and random forests. Our approach outperformed traditional diagnostic methods in terms of accuracy and reliability, demonstrating the potential of machine learning in improving CKD diagnosis. Our approach can be used to develop a computer-aided diagnosis system to assist clinicians in the early and accurate diagnosis of CKD, leading to better patient outcomes.

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

Cited by 33 publications

References 38 publications

Toward speed-of-sound anisotropy quantification in muscle with pulse-echo ultrasound

Toward speed-of-sound anisotropy quantification in muscle with pulse-echo ultrasound

Toward Speed-of-Sound Anisotropy Quantification in Muscle With Pulse-Echo Ultrasound

Machine Learning Methodology for Prediction of Chronic Kidney Disease

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