Regularized phantom-free construction of local attenuation coefficient slope maps for quantitative ultrasound imaging

Rafati, Iman; Destrempes, François; Cloutier, Guy

doi:10.1109/ius46767.2020.9251808

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…Thus, a method is needed to overcome these limitations to provide parametric maps for cancer diagnosis purposes. In our preliminary study [43], reconstructions of attenuation images using a system independent method showed promising results close to the ground truth (through-transmission substitution method) [36], in the case of homogeneous and side-by-side phantoms. The current study provides additional validations with top-tobottom phantoms, and is also focusing on differentiating lesions with different attenuation and geometrical properties than surrounding tissues.…”

Section: Introductionmentioning

confidence: 54%

“…The ACS can be evaluated using a methodology that involves radiofrequency (RF) data acquisition without the need of acquiring RF signals from a reference phantom. The method is based on spectra normalization at different frequencies [43]. Specific contributions are: 1) a linear interpolation of the power spectrum in log-scale; 2) the relaxation of the underlying hypothesis on the diffraction factor within an ROI; and 3) a generalization to nonhomogeneous local ACS.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…The first modification to [43] was to consider a Gaussian fit in log-scale to power spectra for improving robustness of ACS maps. Next, according to [42], the power ratio ( , ) ik RS f z (no unit) between adjacent frequency components ( , ) ik S f z and 1 ( , )…”

Section: A Power Spectrum Modelingmentioning

confidence: 99%

“…To favor a greater number of fused CWs within local attenuation maps, we adopted in this work a "strong BIC" criterion [31,43], which is defined by selecting the largest value of  for which the condition…”

Section: Model's Selectionmentioning

confidence: 99%

“…Recent studies proposed system independent methods as an alternative for estimating local attenuation without the need of a reference phantom [42,43]. This strategy cancels system dependent effects using spectral normalization in adjacent frequency components and is known as the reference frequency method (RFM).…”

Section: Introductionmentioning

confidence: 99%

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Regularized Ultrasound Phantom-Free Local Attenuation Coefficient Slope (ACS) Imaging in Homogeneous and Heterogeneous Tissues

Rafati

Destrempes

Yazdani

et al. 2022

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

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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.

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Section: Introductionmentioning

confidence: 54%

Section: Theoretical Frameworkmentioning

confidence: 99%