Relationship between Ultrasound Backscattered Statistics and the Concentration of Fatty Droplets in Livers: An Animal Study

Ho, Ming‐Chih; Lee, Yu-Hsin; Jeng, Yung‐Ming; Chen, Chiung-Nien; Chang, King‐Jen; Tsui, Po-Hsiang

doi:10.1371/journal.pone.0063543

Cited by 32 publications

(23 citation statements)

References 26 publications

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“…SL = 3 was used because the side length of the window corresponding to three times the pulse length of the transducer enables a stable estimation of the Nakagami parameter13152529. The Nakagami parameters and the entropy values of benign and malignant tumors were shown in the box plots (expressed by the median and interquartile range, IQR) for comparisons.…”

Section: Methodsmentioning

confidence: 99%

“…Researchers reported potential applications of ultrasound Nakagami parametric imaging in visualizing backscattered statistics for quantifying the properties of tissues, such as in breast tumor classification1314, liver fibrosis detection in rats1516, radiotherapy evaluation17, cataract detection18, skin characterization19, vascular flow analysis20, thermal ablation monitoring21, and characterizing the structural anisotropy in the myocardium22. Various research groups demonstrated that the Nakagami image visualizes scatterer arrangements and concentrations and complements the conventional B-scan for tissue characterization.…”

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confidence: 99%

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Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Tsui

Chen

Kuo

et al. 2017

Sci Rep

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Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Tsui

Chen

Kuo

et al. 2017

Sci Rep

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“…The second method uses statistical distributions to model the echo amplitude distribution for a more accurate statistical analysis. This concept has been realized using statistical models, such as K [26] and Nakagami distributions [27,28], indicating that the echo amplitude distribution varies from pre-Rayleigh to Rayleigh with the formation of fatty liver.…”

Section: Introductionmentioning

confidence: 99%

“…From this point of view, non-model-based parameters that can be calculated using any type of data irrespective of the data distribution are suggested as alternatives for characterizing tissues. Among non-model-based statistical parameters, entropy has been shown to correlate with the parameters of the statistical models and the envelope statistics [33], which is a useful clue for characterizing fatty liver [27,28]. Recall that Shannon established information theory and defined entropy as a measure of information uncertainty [34].…”

Section: Introductionmentioning

confidence: 99%

Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals

Tsui

Wan

2016

Entropy

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This study explored the effects of fatty infiltration on the signal uncertainty of ultrasound backscattered echoes from the liver. Standard ultrasound examinations were performed on 107 volunteers. For each participant, raw ultrasound image data of the right lobe of liver were acquired using a clinical scanner equipped with a 3.5-MHz convex transducer. An algorithmic scheme was proposed for ultrasound B-mode and entropy imaging. Fatty liver stage was evaluated using a sonographic scoring system. Entropy values constructed using the ultrasound radiofrequency (RF) and uncompressed envelope signals (denoted by H R and H E , respectively) as a function of fatty liver stage were analyzed using the Pearson correlation coefficient. Data were expressed as the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis with 95% confidence intervals (CIs) was performed to obtain the area under the ROC curve (AUC). The brightness of the entropy image typically increased as the fatty stage varied from mild to severe. The median value of H R monotonically increased from 4.69 (IQR: 4.60-4.79) to 4.90 (IQR: 4.87-4.92) as the severity of fatty liver increased (r = 0.63, p < 0.0001). Concurrently, the median value of H E increased from 4.80 (IQR: 4.69-4.89) to 5.05 (IQR: 5.02-5.07) (r = 0.69, p < 0.0001). In particular, the AUCs obtained using H E (95% CI) were 0. 93 (0.87-0.99), 0.88 (0.82-0.94), and 0.76 (0.65-0.87) for fatty stages ≥mild, ≥moderate, and ≥severe, respectively. The sensitivity, specificity, and accuracy were 93.33%, 83.11%, and 86.00%, respectively (≥mild). Fatty infiltration increases the uncertainty of backscattered signals from livers. Ultrasound entropy imaging has potential for the routine examination of fatty liver disease.

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“…Furthermore, as the Nakagami parameter varies from 0 to 1, the echo amplitude distribution changes from a pre-Rayleigh to a Rayleigh distribution; a Nakagami parameter of >1 represents that the echo amplitude distribution conforms to a post-Rayleigh distribution22. Recently, the Nakagami parameter has been widely used in soft-tissue characterization of, for example, cataracts26, blood27, liver2829, breast30, and thrombus31. The Nakagami parameter is also used in modeling the echo amplitude distribution of bony tissues3233.…”

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confidence: 99%

Ultrasound characterization of the mastoid for detecting middle ear effusion: A preliminary clinical validation

Chen

Fang

Wan

et al. 2016

Sci Rep

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Ultrasound detection of middle ear effusion (MEE) is an emerging technique in otolaryngology. This study proposed using ultrasound characterization of the mastoid to noninvasively measure MEE-induced mastoid effusion (ME) as a new strategy for determining the presence of MEE. In total, 53 patients were enrolled (Group I: normal, n = 20; Group II: proven MEE through both otoscopy and tympanometry, n = 15; Group III: patients with MEE having effusions observed during grommet surgery, n = 18). A 2.25-MHz delay-line transducer was used to measure backscattered signals from the mastoid. The Nakagami parameter was estimated using the acquired signals to model the echo amplitude distribution for quantifying changes in the acoustic structures of mastoid air cells. The median Nakagami parameter and interquartile range were 0.35 (0.34–0.37) for Group I, 0.39 (0.37–0.41) for Group II, and 0.43 (0.39–0.51) for Group III. The echo amplitude distribution observed for patients with MEE was closer to Rayleigh distribution than that without MEE. Receiver operating characteristic (ROC) curve analysis further revealed that the area under the ROC was 0.88, sensitivity was 72.73%, specificity was 95%, and accuracy was 81.13%. The proposed method has considerable potential for noninvasive and comfortable evaluation of MEE.

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Relationship between Ultrasound Backscattered Statistics and the Concentration of Fatty Droplets in Livers: An Animal Study

Cited by 32 publications

References 26 publications

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals

Ultrasound characterization of the mastoid for detecting middle ear effusion: A preliminary clinical validation

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