Evaluation of fatty proportion in fatty liver using least squares method with constraints

Li, Xingsong; Deng, Yinhui; Yu, Jinhua; Wang, Yuanyuan; Shamdasani, Vijay

doi:10.3233/bme-141099

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…4. For echo attenuation to discriminate fatty liver disease, the optimal cut-off value is 0.69 dB/MHz/cm in our study and it is consistent with the previously reported work [18]. Furthermore, Table 5 clearly manifests the significant improvement from the model by combining all the parameters.…”

Section: Resultssupporting

confidence: 90%

“…The accuracy as 76% for echo attenuation increased to 90% as for the established model. Since echo attenuation is comparable with the B-mode assessment by experienced doctors [18], the proposed learning-based model can help significantly increase the accuracy of the diagnosis of fatty liver disease for clinical practice. For the outputs of the established model, the optimal cut-off value is 6.6%.…”

Section: Resultsmentioning

confidence: 99%

“…Conventional echo attenuation is directly estimated from the tracking-echo RF signals. A least-squares method is applied with the learning-based optimization for its parameters [18]. The power spectrum of the backscattered echo signals can be described aswhere is the power spectrum of the backscattered echo signal with as frequency and as the depth.…”

Section: Methodsmentioning

confidence: 99%

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Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence

Shi

Liu

et al. 2019

BioMed Eng OnLine

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BackgroundAn efficient and accurate approach to quantify the steatosis extent of liver is important for clinical practice. For the purpose, we propose a specific designed ultrasound shear wave sequence to estimate ultrasonic and shear wave physical parameters. The utilization of the estimated quantitative parameters is then studied.ResultsShear wave attenuation, shear wave absorption, elasticity, dispersion slope and echo attenuation were simultaneously estimated and quantified from the proposed novel shear wave sequence. Then, a regression tree model was utilized to learn the connection between the space represented by all the physical parameters and the liver fat proportion. MR mDIXON quantification was used as the ground truth for liver fat quantification. Our study included a total of 60 patients. Correlation coefficient (CC) with the ground truth were applied to mainly evaluate different methods for which the corresponding values were − 0.25, − 0.26, 0.028, 0.045, 0.46 and 0.83 for shear wave attenuation, shear wave absorption, elasticity, dispersion slope, echo attenuation and the learning-based model, respectively. The original parameters were extremely outperformed by the learning-based model for which the root mean square error for liver steatosis quantification is only 4.5% that is also state-of-the-art for ultrasound application in the related field.ConclusionsAlthough individual ultrasonic and shear wave parameters were not perfectly adequate for liver steatosis quantification, a promising result can be achieved by the proposed learning-based acoustic model based on them.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence

Shi

Liu

et al. 2019

BioMed Eng OnLine

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“…These methods assume total attenuation as a function of attenuation coefficient in an exponential form. Then an echo signal from a well-characterized reference target is utilized and the attenuation coefficients can be estimated for the processed target [8]. However, parenchyma heterogeneity may negatively impact these methods.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic fatty liver imaging

Deng

Jago

Gong

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Fatty liver disease is a prevalent condition which may result in serious liver complications and is currently lack of an effective and efficient approach for its quantification. In the paper, we propose to directly image the fat content distribution in liver based on ultrasound echo radio-frequency signals. In the proposed method, spectral difference is utilized to represent the small pieces of liver tissues. Then the connection between the data representation and liver tissues is directly established by an elaborately designed learning process in the high-dimensional feature space, which includes comprehensive hyperparameter learning and model learning. Experimental results demonstrate the effectiveness of the proposed method which is able to visualize the fat distribution and has a 0.93 correlation coefficient with the fat-percentage quantification results of doctor's pathological analysis.

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Evaluation of fatty proportion in fatty liver using least squares method with constraints

Cited by 2 publications

References 13 publications

Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence

Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence

Ultrasonic fatty liver imaging

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