Characterizing Fatty Liver in vivo in Rabbits, Using Quantitative Ultrasound

Nguyen, Trong; Podkowa, Anthony; Tam, Alex Y.; Arnold, Eben C.; Miller, Rita J.; Park, Trevor H; Minh, N.; Oelze, Michael L.

doi:10.1016/j.ultrasmedbio.2019.03.021

Cited by 29 publications

(13 citation statements)

References 73 publications

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“…On the other hand, this is the first work to report the correlation between ESD of human liver and MRI-PDFF. Nevertheless, previous studies on rabbit fatty liver found negative correlation between ESD and fat content of the liver [33]. Using different combination of these three QUS parameters estimated using the proposed method with a simple QDA classifier, we were able to discriminate between the steatosis and the non-steatosis class with an accuracy up to 100% and area under the receiver operating characteristic (AUROC) of 1.…”

Section: Discussionmentioning

confidence: 80%

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A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization

Deeba,

Schneider,

Mohammed

et al. 2021

Preprint

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Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) imaging system, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/ √ N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

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Section: Discussionmentioning

confidence: 80%

“…For ESD computation, a Gaussian form factor has been used as it can successfully model the scattering properties of many soft tissues, including liver [20,33]. However, a spherical shell form factor is more appropriate for spherical glass beads, as were used in the phantoms in our experiments [19,20].…”

Section: Discussionmentioning

confidence: 99%

A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization

Deeba,

Schneider,

Mohammed

et al. 2021

Preprint

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“…Progressive accumulation of fatty deposits in the liver altered the spectral content, leading to a significant blue shift in the H-scan US image intensity in the MCD diet animals. With increasing liver fat content, it has been shown that the effective scatterer diameter and density tends to decrease 49 . Lastly, CEUS imaging is sensitive to fatty liver disease progression and can accurately detect vascular features of NASH 20 .…”

Section: Discussionmentioning

confidence: 99%

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

Basavarajappa

Baek

Reddy

et al. 2021

Sci Rep

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Liver disease is increasing in prevalence across the globe. We present here a multiparametric ultrasound (mpUS) imaging approach for assessing nonalcoholic fatty liver disease (NALFD). This study was performed using rats (N = 21) that were fed either a control or methionine and choline deficient (MCD) diet. A mpUS imaging approach that includes H-scan ultrasound (US), shear wave elastography, and contrast-enhanced US measurements were then performed at 0 (baseline), 2, and 6 weeks. Thereafter, animals were euthanized and livers excised for histological processing. A support vector machine (SVM) was used to find a decision plane that classifies normal and fatty liver conditions. In vivo mpUS results from control and MCD diet fed animals reveal that all mpUS measures were different at week 6 (P < 0.05). Principal component analysis (PCA) showed that the H-scan US data contributed the highest percentage to the classification among the mpUS measurements. The SVM resulted in 100% accuracy for classification of normal and high fat livers and 92% accuracy for classification of normal, low fat, and high fat livers. Histology findings found considerable steatosis in the MCD diet fed animals. This study suggests that mpUS examinations have the potential to provide a comprehensive estimation of the main components of early stage NAFLD.

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“…Classifying tissues has recently evolved from model-based approaches such as quantitative ultrasound (QUS) techniques to model-free, DL-based techniques. Nguyen et al [29] demonstrated that QUS techniques are able to detect the presence of steatosis in a rabbit model of fatty liver with a classification accuracy of 84.11%. In a later study, Nguyen et al [5] compared a DL-based classifier to a QUS-based classifier for the problem of fatty liver classifier and found that the DL-based classifier outperformed the QUSbased approach with the accuracy of 74% versus 59%.…”

Section: Introductionmentioning

confidence: 99%

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training

Soylu

Oelze

2023

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

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Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquisition of large and diverse data-sets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy for classifying tissues based on the ultrasonic backscattered RF data, i.e., quantitative ultrasound (QUS), which we named Zone Training. In Zone Training, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of Zone Training is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that Zone Training can require a factor of 2-3 less training data in low data regime to achieve similar classification accuracies compared to a conventional training strategy.

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Characterizing Fatty Liver in vivo in Rabbits, Using Quantitative Ultrasound

Cited by 29 publications

References 73 publications

A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization

A Multiparametric Volumetric Quantitative Ultrasound Imaging Technique for Soft Tissue Characterization

Multiparametric ultrasound imaging for the assessment of normal versus steatotic livers

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training

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