Improving the quality of deep-tissue elastography imaging: A Kelvin–Voigt model-based and multi-point denoising approach
DOI:
https://doi.org/10.54939/1859-1043.j.mst.110.2026.34-44Keywords:
AHI algorithm; LMS filter; Elastography image; Median filter; Multi-point excitation.Abstract
Tissue stiffness has been recognized as an important indicator of pathological conditions. Ultrasound elastography, which relies on the mechanical stiffness properties of tissue, has proven effective in diagnosing various Malignant conditions like breast cancer, thyroid disorders, prostate abnormalities. With advantages including speed, low cost, non-invasiveness, and reliability, elastography has gained significant attention and is now widely applied in clinical diagnostics. However, this technique faces challenges in deep tissue regions, where increased noise and shear wave attenuation lead to degraded image quality and reduced diagnostic accuracy. To address these limitations, this study proposes an integrated approach combining LMS filtering, median filtering, and enhanced multi-source excitation to improve the signal-to-noise ratio (SNR) and enhance image quality in deep tissues. The effectiveness of the proposed method is evaluated using RMSE and Q-index metrics, demonstrating significant noise reduction and improved diagnostic reliability.
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