Regularized ultrasound phantom-free local Attenuation Coefficient Slope (ACS) imaging in homogeneous and heterogeneous tissues
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16 octobre 2023
- handle: 10670/1.01pjlq
- doi: 10.1109/TUFFC.2022.3218920
Mots-clés
Compression wave attenuation imaging Local attenuation coefficient slope Quantitative ultrasound Regularization System independent tissue characterization Ultrasound attenuation coefficient estimationCiter ce document
Iman Rafati et al., « Regularized ultrasound phantom-free local Attenuation Coefficient Slope (ACS) imaging in homogeneous and heterogeneous tissues », Papyrus : le dépôt institutionnel de l'Université de Montréal, ID : 10.1109/TUFFC.2022.3218920
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Résumé
Attenuation map or measurements based on local attenuation coefficient slope (ACS) in quantitative ultrasound (QUS) has shown potential for diagnosis of liver steatosis. In liver cancers, tissue abnormalities and tumors detected using ACS are also of interest to provide new image contrast to clinicians. Current phantom-based approaches have the limitation of assuming comparable speed of sound between the reference phantom and insonified tissues. Moreover, these methods present the inconvenience for operators to acquire data on phantoms as well as on patients. The main goal was to alleviate these drawbacks by proposing a methodology for constructing phantom-free regularized (PF-R) local ACS maps and investigate the performance in both homogeneous and heterogeneous media. The proposed method was tested on two tissue mimicking media with different ACS constructed as homogeneous phantoms, side-byside and top-to-bottom phantoms, and inclusion phantoms with different attenuations. Moreover, an in-vivo proof-of-concept was performed on healthy, steatotic and cancerous human liver datasets. Modifications brought to previous works include: a) a linear interpolation of the power spectrum in log-scale; b) the relaxation of the underlying hypothesis on the diffraction factor; c) a generalization to nonhomogeneous local ACS; and d) an adaptive restriction of frequencies to a more reliable range than the usable frequency range. Regularization was formulated as a generalized LASSO, and a variant of the Bayesian Information Criterion (BIC) was applied to estimate the Lagrangian multiplier on the LASSO constraint. In addition, we evaluated the proposed algorithm when applying median filtering before and after regularization. Tests conducted showed that the PF-R yielded robust results in all tested conditions, suggesting potential for additional validation as a diagnosis method.