arXiv subjects

Shiyu Ma

Publications and source records attributed to Shiyu Ma.

4 recordsLinked to original sources

arXiv2025

Physics-informed Neural Networks Enable High Fidelity Shear Wave Viscoelastography across Multiple organs

Tissue viscoelasticity has been recognized as a crucial biomechanical indicator for disease diagnosis and therapeutic monitoring. Conventional shear wave elastography techniques depend on dispersion analysis and face fundamental limitations in clinical scenarios. Particularly, limited wave propagation data with low signal-to-noise ratios, along with challenges in discriminating between dual dispersion sources stemming from viscoelasticity and finite tissue dimensions, pose great difficulties for extracting dispersion relation. In this study, we introduce SWVE-Net, a framework for shear wave viscoelasticity imaging based on a physics-informed neural network (PINN). SWVE-Net circumvents dispersion analysis by directly incorporating the viscoelasticity wave motion equation into the loss functions of the PINN. Finite element simulations reveal that SWVE-Net quantifies viscosity parameters within a wide range (0.15-1.5 Pa*s), even for samples just a few millimeters in size, where substantial wave reflections and dispersion occur. Ex vivo experiments demonstrate its applicability across various organs, including brain, liver, kidney, and spleen, each with distinct viscoelasticity. In in vivo human trials on breast and skeletal muscle tissues, SWVE-Net reliably assesses viscoelastic properties with standard deviation-to-mean ratios below 15%, highlighting robustness under real-world constraints. SWVE-Net overcomes the core limitations of conventional elastography and enables reliable viscoelastic characterization where traditional methods fall short. It holds promise for applications such as grading hepatic lipid accumulation, detecting myocardial infarction boundaries, and distinguishing malignant from benign tumors.

physics.med-ph↗

arXiv2024

Simultaneous imaging of bidirectional guided waves enables synchronous probing of mechanical anisotropy, local blood pressure, and stress in arteries

Arterial biomechanical indicators have long been recognized as fundamental contributors to the physiology and pathology of cardiovascular systems. Probing the multiple biomechanical parameters of arteries simultaneously at different time points within one cardiac cycle is of great importance but remains challenging. Here we report an ultrasound elastography method to quantify arterial anisotropic stiffness, mechanical stresses in arterial wall, and local blood pressure in a single measurement. With programmed acoustic radiation force, arterial axial and circumferential guided elastic waves were induced simultaneously and recorded at multiple time points within one cardiac cycle. Then a mechanical model incorporating acoustoelasticity and viscoelasticity of arteries was proposed to quantitatively predict the correlation of arterial guided elastic waves with arterial biomechanical parameters. Our experimental design and biomechanical model lead to an elastography method to interrogate the variation of blood pressure, arterial bidirectional stiffnesses and mechanical stresses in arterial walls with time. In vivo experiments were performed on healthy young, normotensive older and hypertensive older volunteers. The results demonstrate that the reported method can find applications in understanding aging of cardiovascular system and diagnosis of cardiovascular diseases.

physics.med-ph↗

arXiv2024

LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis

Log analysis is crucial for ensuring the orderly and stable operation of information systems, particularly in the field of Artificial Intelligence for IT Operations (AIOps). Large Language Models (LLMs) have demonstrated significant potential in natural language processing tasks. In the AIOps domain, they excel in tasks such as anomaly detection, root cause analysis of faults, operations and maintenance script generation, and alert information summarization. However, the performance of current LLMs in log analysis tasks remains inadequately validated. To address this gap, we introduce LogEval, a comprehensive benchmark suite designed to evaluate the capabilities of LLMs in various log analysis tasks for the first time. This benchmark covers tasks such as log parsing, log anomaly detection, log fault diagnosis, and log summarization. LogEval evaluates each task using 4,000 publicly available log data entries and employs 15 different prompts for each task to ensure a thorough and fair assessment. By rigorously evaluating leading LLMs, we demonstrate the impact of various LLM technologies on log analysis performance, focusing on aspects such as self-consistency and few-shot contextual learning. We also discuss findings related to model quantification, Chinese-English question-answering evaluation, and prompt engineering. These findings provide insights into the strengths and weaknesses of LLMs in multilingual environments and the effectiveness of different prompt strategies. Various evaluation methods are employed for different tasks to accurately measure the performance of LLMs in log analysis, ensuring a comprehensive assessment. The insights gained from LogEvals evaluation reveal the strengths and limitations of LLMs in log analysis tasks, providing valuable guidance for researchers and practitioners.

cs.CL↗

arXiv2023

Incremental dynamics of prestressed viscoelastic solids and its applications in shear wave elastography

Shear wave elastography (SWE) is a promising imaging modality for mechanical characterization of tissues, offering biomarkers with potential for early and precise diagnosis. While various methods have been developed to extract mechanical parameters from shear wave characteristics, their relationships in viscoelastic materials under prestress remain poorly understood. Here, we present a generalized incremental dynamics theory for finite-strain viscoelastic solids. The theory derives small-amplitude viscoelastic wave motions in a material under static pre-stress. The formalism is compatible with a range of existing constitutive models, including both hyperelasticity and viscoelasticity--such as the combination of Gasser-Ogden-Holzapfel (GOH) and Kelvin-Voigt fractional derivative (KVFD) models used in this study. We validate the theory through experiments and numerical simulations on prestressed soft materials and biological tissues, using both optical coherence elastography and ultrasound elastography. The theoretical predictions closely match experimental dispersion curves over a broad frequency range and accurately capture the effect of prestress. Furthermore, the framework reveals the relationships among shear wave phase velocity, attenuation, and principal stresses, enabling prestress quantification in viscoelastic solids without prior knowledge of constitutive parameters. This generalized acousto-viscoelastic formalism is particularly well-suited for high-frequency, high-resolution SWE in tissues under prestress.

cond-mat.soft↗