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Li S, Tsui PH, Wu W, Wu S, Zhou Z. Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications. Ultrasonics 2024; 138:107256. [PMID: 38325231 DOI: 10.1016/j.ultras.2024.107256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.
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Affiliation(s)
- Sinan Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Weiwei Wu
- College of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China.
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Fang J, Shen YC, Ting YN, Fang HY, Chen YW. Quantitative assessment of pneumothorax by using Shannon entropy of lung ultrasound M-mode image and diaphragmatic excursion based on automated measurement. Quant Imaging Med Surg 2024; 14:123-135. [PMID: 38223084 PMCID: PMC10784102 DOI: 10.21037/qims-23-636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/12/2023] [Indexed: 01/16/2024]
Abstract
Background Lung ultrasound (LUS) and diaphragm ultrasound (DUS) are the appropriate modalities for conservative observation to those patients who are with stable pneumothorax, as well as for the timely detection of life-threatening pneumothorax at any location, due to they are portable, real-time, relatively cost effective, and most important, without radiation exposure. The absence of lung sliding on LUS M-mode images and the abnormality of diaphragmatic excursion (DE) on DUS M-mode images are the most common and novel diagnostic criteria for pneumothorax, respectively. However, visual inspection of M-mode images remains subjective and quantitative analysis of LUS and DUS M-mode images are required. Methods Shannon entropy of LUS M-mode image (ShanEnLM) and DE based on the automated measurement (DEAM) are adapted to the objective pneumothorax diagnoses and the severity quantifications in this study. Mild, moderate, and severe pneumothoraces were induced in 24 male New Zealand rabbits through insufflation of room air (5, 10 and 15, and 25 and 40 mL/kg, respectively) into their pleural cavities. In vivo intercostal LUS and subcostal DUS M-mode images were acquired using a point-of-care system for estimating ShanEnLM and DEAM. Results ShanEnLM and DEAM as functions of air insufflation volumes exhibited U-shaped curves and were exponentially decreasing, respectively. Either ShanEnLM or DEAM had areas under the receiver operating characteristic curves [95% confidence interval (CI)] of 1.0000 (95% CI: 1.0000-1.0000), 0.9833 (95% CI: 0.9214-1.0000), and 0.9407 (95% CI: 0.8511-1.0000) for differentiating between normal and mild pneumothorax, mild and moderate pneumothoraces, and moderate and severe pneumothoraces, respectively. Conclusions Our findings imply that the combination of ShanEnLM and DEAM give the promising potential for pneumothorax quantitative diagnosis.
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Affiliation(s)
- Jui Fang
- Research & Development Center for x-Dimensional Extracellular Vesicles, China Medical University Hospital, Taichung City
| | - Yu-Cheng Shen
- Division of Thoracic Surgery, Department of Surgery, China Medical University Hospital, Taichung City
| | - Yen-Nien Ting
- Research & Development Center for x-Dimensional Extracellular Vesicles, China Medical University Hospital, Taichung City
| | - Hsin-Yuan Fang
- Division of Thoracic Surgery, Department of Surgery, China Medical University Hospital, Taichung City
- School of Medicine, College of Medicine, China Medical University, Taichung City
| | - Yi-Wen Chen
- Research & Development Center for x-Dimensional Extracellular Vesicles, China Medical University Hospital, Taichung City
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung City
- High Performance Materials Institute for xD Printing, Asia University, Taichung City
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Tsui PH. Information Entropy and Its Applications. Adv Exp Med Biol 2023; 1403:153-167. [PMID: 37495918 DOI: 10.1007/978-3-031-21987-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Ultrasound is a first-line diagnostic tool for imaging many disease states. A number of statistical distributions have been proposed to describe ultrasound backscattering measured from tissues having different disease states. As an example, in this chapter we use nonalcoholic fatty liver disease (NAFLD), which is a critical health issue on a global scale, to demonstrate the capabilities of ultrasound to diagnose disease. Ultrasound interaction with the liver is typically characterized by scattering, which is quantified for the purpose of determining the degree of liver steatosis and fibrosis. Information entropy provides an insight into signal uncertainty. This concept allows for the analysis of backscattered statistics without considering the distribution of data or the statistical properties of ultrasound signals. In this chapter, we examined the background of NAFLD and the sources of scattering in the liver. The fundamentals of information entropy and an algorithmic scheme for ultrasound entropy imaging are then presented. Lastly, some examples of using ultrasound entropy imaging to grade hepatic steatosis and evaluate the risk of liver fibrosis in patients with significant hepatic steatosis are presented to illustrate future opportunities for clinical use.
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Affiliation(s)
- Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan City, Taiwan.
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Behnia A, Behnam H, Shaswary E, Tavakkoli J. Thermometry using entropy imaging of ultrasound radio frequency signal time series. Proc Inst Mech Eng H 2022; 236:1502-1512. [DOI: 10.1177/09544119221122645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Low intensity focused ultrasound (LIFU) is a novel approach that could activate drug release and considerably improve the delivery of anticancer drug. LIFU treatment has some features like is able to penetrate deep into the tissue and being non-invasive, as a consequence LIFU displays great capability for controlling the drug release and improving the chemotherapy treatment efficiency. The goal of this study is to research the feasibility of the entropy parameter of RF time series of ultrasound backscattered signals for measuring the changes in temperature induced by a LIFU device. Entropy Imaging is a technique for reconstructing ultrasound images based on the average uncertainty of time-series in a signal. Furthermore, the Shannon Entropy can quantify the uncertainty of a random process and is usually used as a measure for the information content of probability distributions. In this study, we use the Entropy Imaging method for measuring the LIFU-induced temperature changes in the deep region of ex vivo porcine tissue samples. The results obtained show that the changes of entropy parameter of RF time series signal are proportional to temperature changes recorded by a calibrated thermocouple in the temperature range of 37–47°C. In conclusion, in this study we show that Shannon entropy of RF time series signal possesses promising features like succinctly capturing the available information in a system by considering the uncertainty in a given data that can be used, as a new method, to measure temperature changes non-invasively and quantitatively in the deep region of tissue.
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Affiliation(s)
- Ashkan Behnia
- School of Electrical Engineering, Department of Biomedical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hamid Behnam
- School of Electrical Engineering, Department of Biomedical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Elyas Shaswary
- Department of Physics, Ryerson University, Toronto, ON, Canada
| | - Jahan Tavakkoli
- Department of Physics, Ryerson University, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, Institute for Biomedical Engineering, Science and Technology (iBEST), St. Michael’s Hospital, Toronto, ON, Canada
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Fang J, Ting YN, Chen YW. Quantitative Assessment of Lung Ultrasound Grayscale Images Based on Shannon Entropy for the Detection of Pulmonary Aeration: An Animal Study. J Ultrasound Med 2022; 41:1699-1711. [PMID: 34698398 DOI: 10.1002/jum.15851] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/23/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration. METHODS Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry. RESULTS The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR. CONCLUSION The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.
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Affiliation(s)
- Jui Fang
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung City, Taiwan
| | - Yen-Nien Ting
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung City, Taiwan
| | - Yi-Wen Chen
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, Taiwan
- High Performance Materials Institute for xD Printing, Asia University, Taichung City, Taiwan
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Li X, Jia X, Shen T, Wang M, Yang G, Wang H, Sun Q, Wan M, Zhang S. Ultrasound Entropy Imaging for Detection and Monitoring of Thermal Lesion During Microwave Ablation of Liver. IEEE J Biomed Health Inform 2022; 26:4056-4066. [PMID: 35417359 DOI: 10.1109/jbhi.2022.3167252] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0.895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0.90 against ground truth, AUROC of 0.868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5.86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.
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Wang CY, Chu SY, Lin YC, Tsai YW, Tai CL, Yang KC, Tsui PH. Quantitative imaging of ultrasound backscattered signals with information entropy for bone microstructure characterization. Sci Rep 2022; 12:414. [PMID: 35013540 PMCID: PMC8748747 DOI: 10.1038/s41598-021-04425-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/08/2021] [Indexed: 12/19/2022] Open
Abstract
Osteoporosis is a critical problem during aging. Ultrasound signals backscattered from bone contain information associated with microstructures. This study proposed using entropy imaging to collect the information in bone microstructures as a possible solution for ultrasound bone tissue characterization. Bone phantoms with different pounds per cubic foot (PCF) were used for ultrasound scanning by using single-element transducers of 1 (nonfocused) and 3.5 MHz (nonfocused and focused). Clinical measurements were also performed on lumbar vertebrae (L3 spinal segment) in participants with different ages (n = 34) and postmenopausal women with low or moderate-to-high risk of osteoporosis (n = 50; identified using the Osteoporosis Self-Assessment Tool for Taiwan). The signals backscattered from the bone phantoms and subjects were acquired for ultrasound entropy imaging by using sliding window processing. The independent t-test, one-way analysis of variance, Spearman correlation coefficient rs, and the receiver operating characteristic (ROC) curve were used for statistical analysis. The results indicated that ultrasound entropy imaging revealed changes in bone microstructures. Using the 3.5-MHz focused ultrasound, small-window entropy imaging (side length: one pulse length of the transducer) was found to have high performance and sensitivity in detecting variation among the PCFs (rs = − 0.83; p < 0.05). Small-window entropy imaging also performed well in discriminating young and old participants (p < 0.05) and postmenopausal women with low versus moderate-to-high osteoporosis risk (the area under the ROC curve = 0.80; cut-off value = 2.65; accuracy = 86.00%; sensitivity = 71.43%; specificity = 88.37%). Ultrasound small-window entropy imaging has great potential in bone tissue characterization and osteoporosis assessment.
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Affiliation(s)
- Chiao-Yin Wang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyüan, Taiwan
| | - Sung-Yu Chu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyüan, Taiwan
| | - Yu-Ching Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Keelung and Chang Gung University, Taoyüan, Taiwan
| | - Yu-Wei Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyüan, Taiwan
| | - Ching-Lung Tai
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyüan, Taiwan
| | - Kuen-Cheh Yang
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyüan, Taiwan. .,Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyüan, Taiwan. .,Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyüan, Taiwan.
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Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2976-2980. [PMID: 34891870 DOI: 10.1109/embc46164.2021.9630198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Osteoarthritis is a common disease that implies joint degeneration and that strongly affects the quality of life. Conventional radiography remains currently the most used diagnostic method, even if it allows only an indirect assessment of the articular cartilage and employ the use of ionizing radiations. A non-invasive, continuous and reliable diagnosis is crucial to detect impairments and to improve the treatment outcomes.Quantitative ultrasound techniques have proved to be very useful in providing an objective diagnosis of several soft tissues. In this study, we propose quantitative ultrasound parameters, based on the analysis of radiofrequency data derived from both healthy and osteoarthritis-mimicking (through chemical degradation) ex-vivo cartilage samples. Using a transmission frequency typically employed in the clinical practice (7.5-15 MHz) with an external ultrasound probe, we found results in terms of reflection at the cartilage surface and sample thickness comparable to those reported in the literature by exploiting arthroscopic transducers at high frequency (from 20 to 55 MHz). Moreover, for the first time, we introduce an objective metric based on the phase entropy calculation, able to discriminate the healthy cartilage from the degenerated one.Clinical Relevance- This preliminary study proposes a novel and quantitative method to discriminate healthy from degenerated cartilage. The obtained results pave the way to the use of quantitative ultrasound in the diagnosis and monitoring of knee osteoarthritis.
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Sorriento A, Poliziani A, Cafarelli A, Valenza G, Ricotti L. A novel quantitative and reference-free ultrasound analysis to discriminate different concentrations of bone mineral content. Sci Rep 2021; 11:301. [PMID: 33432022 PMCID: PMC7801603 DOI: 10.1038/s41598-020-79365-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/07/2020] [Indexed: 12/19/2022] Open
Abstract
Bone fracture is a continuous process, during which bone mineral matrix evolves leading to an increase in hydroxyapatite and calcium carbonate content. Currently, no gold standard methods are available for a quantitative assessment of bone fracture healing. Moreover, the available tools do not provide information on bone composition. Whereby, there is a need for objective and non-invasive methods to monitor the evolution of bone mineral content. In general, ultrasound can guarantee a quantitative characterization of tissues. However, previous studies required measurements on reference samples. In this paper we propose a novel and reference-free parameter, based on the entropy of the phase signal calculated from the backscattered data in combination with amplitude information, to also consider absorption and scattering phenomena. The proposed metric was effective in discriminating different hydroxyapatite (from 10 to 50% w/v) and calcium carbonate (from 2 to 6% w/v) concentrations in bone-mimicking phantoms without the need for reference measurements, paving the way to their translational use for the diagnosis of tissue healing. To the best of our knowledge this is the first time that the phase entropy of the backscattered ultrasound signals is exploited for monitoring changes in the mineral content of bone-like materials.
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Affiliation(s)
- A Sorriento
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy.
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy.
| | - A Poliziani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
| | - A Cafarelli
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
| | - G Valenza
- Bioengineerring and Robotics Research Centre E Piaggio, University of Pisa, 56122, Pisa, Italy
- Department of Information Engineering, University of Pisa, 56123, Pisa, Italy
| | - L Ricotti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
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Chen JR, Chao YP, Tsai YW, Chan HJ, Wan YL, Tai DI, Tsui PH. Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis. Entropy (Basel) 2020; 22:E1006. [PMID: 33286775 PMCID: PMC7597079 DOI: 10.3390/e22091006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/31/2020] [Accepted: 09/07/2020] [Indexed: 02/07/2023]
Abstract
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.
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Affiliation(s)
- Jheng-Ru Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333323, Taiwan;
- Graduate Institute of Biomedical Engineering, Chang Gung University, College of Engineering, Taoyuan 333323, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Yu-Wei Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Hsien-Jung Chan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Yung-Liang Wan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333423, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
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Lin YH, Wan YL, Tai DI, Tseng JH, Wang CY, Tsai YW, Lin YR, Chang TY, Tsui PH. Considerations of Ultrasound Scanning Approaches in Non-alcoholic Fatty Liver Disease Assessment through Acoustic Structure Quantification. Ultrasound Med Biol 2019; 45:1955-1969. [PMID: 31130411 DOI: 10.1016/j.ultrasmedbio.2019.04.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 04/10/2019] [Accepted: 04/12/2019] [Indexed: 02/07/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a risk factor for hepatic fibrosis and cirrhosis. Acoustic structure quantification (ASQ), based on statistical analysis of ultrasound echoes, is an emerging technique for hepatic steatosis diagnosis. A standardized measurement protocol for ASQ analysis was suggested previously; however, an optimal ultrasound scanning approach has not been concluded thus far. In this study, the suitability of scanning approaches for the ASQ-based evaluation of hepatic steatosis was investigated. Hepatic fat fractions (HFFs; liver segments VIII, III and VI) of 70 living liver donors were assessed with magnetic resonance spectroscopy. A clinical ultrasound machine equipped with a 3-MHz convex transducer was used to scan each participant using the intercostal, epigastric and subcostal planes to acquire raw data for estimating two ASQ parameters (Cm2 and focal disturbance [FD] ratio) of segments VIII, III and VI, respectively. The parameters were plotted as functions of the HFF for calculating the values of the correlation coefficient (r) and probability value (p). The diagnostic performance of the parameters in discriminating between the normal and steatotic (≥5 and ≥10%) groups was also compared using receiver operating characteristic (ROC) curves. The Cm2 and FD ratio values measured using the epigastric and subcostal planes did not correlate with the severity of hepatic steatosis. However, intercostal imaging exhibited a higher correlation between the ASQ parameters and HFF (r = -0.64, p < 0.001). The diagnostic performance of Cm2 and FD ratio in detecting hepatic steatosis using intercostal imaging was also satisfactory (areas under ROC curves >0.8). Intercostal imaging is an appropriate scanning approach for ASQ analysis of the liver.
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Affiliation(s)
- Ying-Hsiu Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Jeng-Hwei Tseng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Chiao-Yin Wang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yu-Wei Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ru Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Tu-Yung Chang
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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Lin YH, Liao YY, Yeh CK, Yang KC, Tsui PH. Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome. Entropy (Basel) 2018; 20:E893. [PMID: 33266617 DOI: 10.3390/e20120893] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/18/2018] [Accepted: 11/20/2018] [Indexed: 02/06/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the leading cause of advanced liver diseases. Fat accumulation in the liver changes the hepatic microstructure and the corresponding statistics of ultrasound backscattered signals. Acoustic structure quantification (ASQ) is a typical model-based method for analyzing backscattered statistics. Shannon entropy, initially proposed in information theory, has been demonstrated as a more flexible solution for imaging and describing backscattered statistics without considering data distribution. NAFLD is a hepatic manifestation of metabolic syndrome (MetS). Therefore, we investigated the association between ultrasound entropy imaging of NAFLD and MetS for comparison with that obtained from ASQ. A total of 394 participants were recruited to undergo physical examinations and blood tests to diagnose MetS. Then, abdominal ultrasound screening of the liver was performed to calculate the ultrasonographic fatty liver indicator (US-FLI) as a measure of NAFLD severity. The ASQ analysis and ultrasound entropy parametric imaging were further constructed using the raw image data to calculate the focal disturbance (FD) ratio and entropy value, respectively. Tertiles were used to split the data of the FD ratio and entropy into three groups for statistical analysis. The correlation coefficient r, probability value p, and odds ratio (OR) were calculated. With an increase in the US-FLI, the entropy value increased (r = 0.713; p < 0.0001) and the FD ratio decreased (r = –0.630; p < 0.0001). In addition, the entropy value and FD ratio correlated with metabolic indices (p < 0.0001). After adjustment for confounding factors, entropy imaging (OR = 7.91, 95% confidence interval (CI): 0.96–65.18 for the second tertile; OR = 20.47, 95% CI: 2.48–168.67 for the third tertile; p = 0.0021) still provided a more significant link to the risk of MetS than did the FD ratio obtained from ASQ (OR = 0.55, 95% CI: 0.27–1.14 for the second tertile; OR = 0.42, 95% CI: 0.15–1.17 for the third tertile; p = 0.13). Thus, ultrasound entropy imaging can provide information on hepatic steatosis. In particular, ultrasound entropy imaging can describe the risk of MetS for individuals with NAFLD and is superior to the conventional ASQ technique.
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