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Minami Y, Ogura I. Comparison of single photon emission computed tomography-computed tomography, computed tomography and magnetic resonance imaging of medication-related osteonecrosis of jaw by new calculated parameters. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF... 2024; 68:126-132. [PMID: 36287042 DOI: 10.23736/s1824-4785.22.03483-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND This study aimed to investigate parameters for medication-related osteonecrosis of jaw (MRONJ) patients using the bone SPECT/CT, especially bone mineral-based parameters. METHODS Sixty-three patients with MRONJ (43 osteoporosis and 20 bone metastasises) underwent CT, MRI and SPECT/CT. A commercially available software automatically detected lesion area and calculated the quantitative SPECT/CT parameters as bone mineral-based standardized uptake value (SUV). RESULTS Regarding stage of MRONJ patients, bone mineral based maximum SUV of stage 3 was significantly higher than stage 1, 2 (P=0.018). Regarding duration of medication therapy, bone mineral based maximum SUV 1 year or more was significantly higher than less than 1 year (P=0.019). Regarding present of periosteal bone proliferation on CT, bone mineral based maximum SUV was significantly higher than those of absent (P=0.029). Regarding spread of soft tissue inflammation on MRI, bone mineral based maximum SUV of 2 or more was significantly higher than those of less than 2 spaces (P=0.025). Regarding blood pool phase imaging with SPECT, bone mineral based maximum SUV of intense uptake was significantly higher than those of decrease uptake (P=0.002). CONCLUSIONS SPECT/CT bone mineral-based parameters indicated significant difference in staging, dosing period, periosteal bone proliferation on CT, spread of soft tissue inflammation on MRI, and blood phase imaging with SPECT. Bone SPECT/CT bone mineral-based parameters are helpful for the assessment of MRONJ.
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Affiliation(s)
- Yoshiyuki Minami
- Quantitative Diagnostic Imaging, Field of Oral and Maxillofacial Imaging and Histopathological Diagnostics, Course of Applied Science, The Nippon Dental University Graduate School of Life Dentistry at Niigata, Niigata, Japan -
| | - Ichiro Ogura
- Quantitative Diagnostic Imaging, Field of Oral and Maxillofacial Imaging and Histopathological Diagnostics, Course of Applied Science, The Nippon Dental University Graduate School of Life Dentistry at Niigata, Niigata, Japan
- Department of Oral and Maxillofacial Radiology, The Nippon Dental University School of Life Dentistry at Niigata, Niigata, Japan
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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] [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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Yan D, Li Q, Lin CW, Shieh JY, Weng WC, Tsui PH. Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy. IEEE J Biomed Health Inform 2024; 28:835-845. [PMID: 37930927 DOI: 10.1109/jbhi.2023.3330578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
BACKGROUND Duchenne muscular dystrophy (DMD) is a neuromuscular disorder that affects ambulatory function. Quantitative ultrasound (QUS) imaging, utilizing envelope statistics, has proven effective in diagnosing DMD. Radiomics enables the extraction of detailed features from QUS images. This study further proposes a hybrid QUS radiomics and explores its value in characterizing DMD. METHODS Patients (n = 85) underwent ultrasound examinations of gastrocnemius through Nakagami, homodyned K (HK), and information entropy imaging. The hybrid QUS radiomics extracted, selected, and integrated the retained features derived from each QUS image for classification of ambulatory function using support vector machine. Nested five fold cross-validation of the data was conducted, with the rotational process repeated 50 times. The performance was assessed by averaging the areas under the receiver operating characteristic curve (AUROC). RESULTS Radiomics enhanced the average AUROC of B-scan, Nakagami, HK, and entropy imaging to 0.790, 0.911, 0.869, and 0.890, respectively. By contrast, the hybrid QUS radiomics using HK and entropy images for diagnosing ambulatory function in DMD patients achieved a superior average AUROC of 0.971 (p < 0.001 compared with conventional radiomics analysis). CONCLUSIONS The proposed hybrid QUS radiomics incorporates microstructure-related backscattering information from various envelope statistics models to effectively enhance the performance of DMD assessment.
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Hsieh CS, Lai MW, Chen CC, Chao HC, Wang CY, Wan YL, Zhou Z, Tsui PH. Quantitative ultrasound envelope statistics imaging as a screening approach for pediatric hepatic steatosis and liver fibrosis: using biomarker and transient elastography as reference standards. Heliyon 2023; 9:e22743. [PMID: 38213577 PMCID: PMC10782159 DOI: 10.1016/j.heliyon.2023.e22743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 01/13/2024] Open
Abstract
Quantitative ultrasound (QUS) envelope statistics imaging is an emerging technique for the assessment of hepatic steatosis in adults. Blood tests are currently recommended as the screening tool for pediatric hepatic steatosis, a condition that can lead to liver fibrosis in children. This study examined the utility of QUS envelope statistics imaging in grading biomarker-diagnosed hepatic steatosis and detecting liver fibrosis in a pediatric population. A total of 173 subjects was enrolled (Group A) for QUS envelope statistics imaging using two statistical distributions, Nakagami and homodyned K (HK) models, and information entropy. QUS parameter values were compared with the hepatic steatosis index (HSI) and steatosis grade (G0: HSI <30; G1: 30 ≤ HSI <36; G2: 36 ≤ HSI <41.6; G3: ≥41.6). An additional cohort of 63 subjects (Group B) was recruited to undergo both QUS envelope statistics imaging and liver stiffness measurements (LSM) obtained from the transient elastography (Fibroscan), with a cutoff value set at 5 kPa to indicate liver fibrosis. The diagnostic performances were evaluated using the area under the receiver operating characteristic curve (AUROC). QUS envelope statistics imaging generated the AUROC values for steatosis grading at levels ≥ G1, ≥ G2, and ≥ G3 ranged from 0.94 to 0.97, 0.91 to 0.93, and 0.83 to 0.87, respectively, and produced an AUROC range of between 0.82 and 0.84 for identifying liver fibrosis. QUS envelope statistics imaging integrates the benefits of both biomarkers and elastography, enabling the screening of hepatic steatosis and detection of liver fibrosis in a pediatric population.
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Affiliation(s)
- Chiao-Shan Hsieh
- Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Ming-Wei Lai
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Liver Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Chien-Chang Chen
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsun-Chin Chao
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chiao-Yin Wang
- 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
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Po-Hsiang Tsui
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Liver Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Karbalaeisadegh Y, Yao S, Zhu Y, Grimal Q, Muller M. Ultrasound Characterization of Cortical Bone Using Shannon Entropy. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1824-1829. [PMID: 37244812 DOI: 10.1016/j.ultrasmedbio.2023.04.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Ultrasound backscattered signals encompass information on the microstructure of heterogeneous media such as cortical bone, in which pores act as scatterers and result in the scattering and multiple scattering of ultrasound waves. The objective of this study was to investigate whether Shannon entropy can be exploited to characterize cortical porosity. METHODS In the study described here, to demonstrate proof of concept, Shannon entropy was used as a quantitative ultrasound parameter to experimentally evaluate microstructural changes in samples with controlled scatterer concentrations made of a highly absorbing polydimethylsiloxane matrix (PDMS). Similar assessment was then performed using numerical simulations on cortical bone structures with varying average pore diameter (Ct.Po.Dm.), density (Ct.Po.Dn.) and porosity (Ct.Po.). RESULTS The results suggest that an increase in pore diameter and porosity lead to an increase in entropy, indicating increased levels of randomness in the signals as a result of increased scattering. The entropy-versus-scatterer volume fraction in PDMS samples indicates an initial increasing trend that slows down as the scatterer concentration increases. High levels of attenuation cause the signal amplitudes and corresponding entropy values to decrease drastically. The same trend is observed when porosity of the bone samples is increased above 15%. CONCLUSION Sensitivity of entropy to microstructural changes in highly scattering and absorbing media can potentially be exploited to diagnose and monitor osteoporosis.
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Affiliation(s)
- Yasamin Karbalaeisadegh
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Shanshan Yao
- Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Yong Zhu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
| | - Quentin Grimal
- Laboratory of Biomedical Imaging, Sorbonne University, Paris, France
| | - Marie Muller
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
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Zhang Q, Liu X, Chang J, Lu M, Jing Y, Yang R, Sun W, Deng J, Qi T, Wan M. Ultrasound image segmentation using Gamma combined with Bayesian model for focused-ultrasound-surgery lesion recognition. ULTRASONICS 2023; 134:107103. [PMID: 37437399 DOI: 10.1016/j.ultras.2023.107103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/30/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
This study aims to investigate the feasibility of combined segmentation for the separation of lesions from non-ablated regions, which allows surgeons to easily distinguish, measure, and evaluate the lesion area, thereby improving the quality of high-intensity focused-ultrasound (HIFU) surgery used for the non-invasive tumor treatment. Given that the flexible shape of the Gamma mixture model (GΓMM) fits the complex statistical distribution of samples, a method combining the GΓMM and Bayes framework is constructed for the classification of samples to obtain the segmentation result. An appropriate normalization range and parameters can be used to rapidly obtain a good performance of GΓMM segmentation. The performance values of the proposed method under four metrics (Dice score: 85%, Jaccard coefficient: 75%, recall: 86%, and accuracy: 96%) are better than those of conventional approaches including Otsu and Region growing. Furthermore, the statistical result of sample intensity indicates that the finding of the GΓMM is similar to that obtained by the manual method. These results indicate the stability and reliability of the GΓMM combined with the Bayes framework for the segmentation of HIFU lesions in ultrasound images. The experimental results show the possibility of combining the GΓMM with the Bayes framework to segment lesion areas and evaluate the effect of therapeutic ultrasound.
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Affiliation(s)
- Quan Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Xuan Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Juntao Chang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Mingzhu Lu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China.
| | - Yanshu Jing
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Rongzhen Yang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Weihao Sun
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Jie Deng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Tingting Qi
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
| | - Mingxi Wan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an 710049, China
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Wu J, Liu N, Li X, Fan Q, Li Z, Shang J, Wang F, Chen B, Shen Y, Cao P, Liu Z, Li M, Qian J, Yang J, Sun Q. Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study. BMC Med Imaging 2023; 23:18. [PMID: 36717773 PMCID: PMC9885575 DOI: 10.1186/s12880-023-00975-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 01/24/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs. METHODS A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model's performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists. RESULTS The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists' readings. CONCLUSIONS The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists' workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.
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Affiliation(s)
- Jiangfen Wu
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,grid.43169.390000 0001 0599 1243The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710054 China ,InferVision Institute of Research, Beijing, 100025 China ,grid.11135.370000 0001 2256 9319Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100191 China
| | - Nijun Liu
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,Department of Medical Imaging, No. 215 Hospital of Shaanxi Nuclear Industry, Xianyang, 712000 China
| | - Xianjun Li
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China
| | - Qianrui Fan
- InferVision Institute of Research, Beijing, 100025 China
| | | | - Jin Shang
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China
| | - Fei Wang
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China
| | - Bowei Chen
- grid.412262.10000 0004 1761 5538School of Information Science and Technology, Northwest University, Xi’an, 710127 China
| | - Yuanwang Shen
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,Department of Medical Imaging, No. 215 Hospital of Shaanxi Nuclear Industry, Xianyang, 712000 China
| | - Pan Cao
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,Department of Radiology, Tuberculosis Hospital of Shannxi Province (The Fifth People’s Hospital of Shaanxi Province), Xi’an, 710100 China
| | - Zhe Liu
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China
| | - Miaoling Li
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China
| | - Jiayao Qian
- InferVision Institute of Research, Beijing, 100025 China
| | - Jian Yang
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,grid.43169.390000 0001 0599 1243The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710054 China
| | - Qinli Sun
- grid.452438.c0000 0004 1760 8119Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Yanta West Road No. 277, Xi’an, 710061 China ,grid.43169.390000 0001 0599 1243The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710054 China
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Tsui PH. Information Entropy and Its Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 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] [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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9
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Kaplan E, Chan WY, Dogan S, Barua PD, Bulut HT, Tuncer T, Cizik M, Tan RS, Acharya UR. Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images. Med Eng Phys 2022; 108:103895. [DOI: 10.1016/j.medengphy.2022.103895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 10/14/2022]
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Song H, Kang J, Boctor EM. A novel design framework of synthetic radial aperture focusing for volumetric transrectal ultrasound imaging. JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022; 9:1852-1865. [PMID: 36268473 PMCID: PMC9563629 DOI: 10.1093/jcde/qwac083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 04/19/2022] [Accepted: 05/14/2022] [Indexed: 06/16/2023]
Abstract
In this paper, we present a novel design framework of synthetic radial aperture focusing for three-dimensional (3D) transrectal ultrasound imaging (TRUS-rSAF), in which multiple transmittance/reception events at different scanning angles are synthesized to reconstruct a radial plane in the target volume, securing high spatial resolution and texture uniformity. A theory-based design approach has not been available to push the envelope of the 3D rSAF technique. Herein, a closed-form analytical description of the TRUS-rSAF method is presented for the first time, effectively delineating spatial resolution and grating lobe positions in the radial dimension of a TRUS transducer. We demonstrate a solid optimization workflow based on the theoretical foundation to improve its spatiotemporal resolution, grating lobe artifacts, and signal-to-noise ratio. A specific design criterion was considered to outperform a clinical 3D TRUS imaging as a reference (TRUS-REF), where each radial plane is reconstructed with a single transmittance/reception event using a motorized actuator. The optimized TRUS-rSAF method significantly enhanced spatial resolution up to 50% over the TRUS-REF method while providing clinically effective temporal resolution (2-8 volume/sec) with negligible grating lobe artifacts. The results indicate that the proposed design approach would enable a novel TRUS imaging solution in clinics.
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Affiliation(s)
- Hyunwoo Song
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
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11
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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] [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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12
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Chowdhury A, Razzaque RR, Muhtadi S, Shafiullah A, Ul Islam Abir E, Garra BS, Kaisar Alam S. Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework. ULTRASONICS 2022; 124:106744. [PMID: 35390626 DOI: 10.1016/j.ultras.2022.106744] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 03/22/2022] [Accepted: 03/31/2022] [Indexed: 06/14/2023]
Abstract
In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions and characterization of breast tissue. Through a sliding window technique, we generated seven types of Nakagami images for each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the suitable window size for image generation, we conducted an empirical analysis using 4 windows, which includes 3 column windows of lengths 0.1875 mm, 0.45 mm and 0.75 mm and widths of 0.002 mm, along with the standard square window with sides equal to three times the pulse length of incident ultrasound. From the parametric image sets generated using each window, we extracted a total of 72 features that consisted of morphometric, elemental and hybrid features. To our knowledge no other literature has conducted such a comprehensive analysis of Nakagami parametric images for the classification of breast lesions. Feature selection was performed to find the most useful subset of features from each of the parametric image sets for the classification of breast cancer. Analyzing the classification accuracy and Area under the Receiver Operating Characteristic (ROC) Curve (AUC) of the selected feature subsets, we determined that the selected features acquired from Nakagami parametric images generated using a column window of length 0.75 mm provides the best results for characterization of breast lesions. This optimal feature set provided a classification accuracy of 93.08%, an AUC of 0.9712, a False Negative Rate (FNR) of 0%, and a very low False Positive Rate (FPR) of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnosis of breast cancer by helping to reduce false positive diagnoses.
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Affiliation(s)
- Ahmad Chowdhury
- Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Rezwana R Razzaque
- Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Sabiq Muhtadi
- Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh.
| | - Ahmad Shafiullah
- Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Ehsan Ul Islam Abir
- Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Brian S Garra
- Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States
| | - S Kaisar Alam
- Imagine Consulting LLC, Dayton, NJ, United States; Prep Excellence LLC, Dayton, NJ, United States; The Center for Computational Biomedicine Imaging and Modelling (CBIM), Rutgers University, NJ, Piscataway, United States
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13
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Yao R, Zhang Y, Wu K, Li Z, He M, Fengyue B. Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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14
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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: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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15
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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] [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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16
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Markov DA, Petrucco L, Kist AM, Portugues R. A cerebellar internal model calibrates a feedback controller involved in sensorimotor control. Nat Commun 2021; 12:6694. [PMID: 34795244 PMCID: PMC8602262 DOI: 10.1038/s41467-021-26988-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/28/2021] [Indexed: 11/18/2022] Open
Abstract
Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions. In contrast, internal models learn the relationship between the motor output and its sensory consequences and can be used to recalibrate behaviors. Here, we present multiple unpredictable perturbations in visual feedback to larval zebrafish performing the optomotor response and show that they react to these perturbations through a feedback control mechanism. In contrast, if a perturbation is long-lasting, fish adapt their behavior by updating a cerebellum-dependent internal model. We use modelling and functional imaging to show that the neuronal requirements for these mechanisms are met in the larval zebrafish brain. Our results illustrate the role of the cerebellum in encoding internal models and how these can calibrate neuronal circuits involved in reactive behaviors depending on the interactions between animal and environment.
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Affiliation(s)
- Daniil A Markov
- Sensorimotor Control Research Group, Max Planck Institute of Neurobiology, 82152, Martinsried, Germany
| | - Luigi Petrucco
- Sensorimotor Control Research Group, Max Planck Institute of Neurobiology, 82152, Martinsried, Germany
- Institute of Neuroscience, Technical University of Munich, 80802, Munich, Germany
| | - Andreas M Kist
- Sensorimotor Control Research Group, Max Planck Institute of Neurobiology, 82152, Martinsried, Germany
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Ruben Portugues
- Sensorimotor Control Research Group, Max Planck Institute of Neurobiology, 82152, Martinsried, Germany.
- Institute of Neuroscience, Technical University of Munich, 80802, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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17
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Chan HJ, Zhou Z, Fang J, Tai DI, Tseng JH, Lai MW, Hsieh BY, Yamaguchi T, Tsui PH. Ultrasound Sample Entropy Imaging: A New Approach for Evaluating Hepatic Steatosis and Fibrosis. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 9:1800612. [PMID: 34786215 PMCID: PMC8580366 DOI: 10.1109/jtehm.2021.3124937] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/20/2021] [Accepted: 10/10/2021] [Indexed: 02/05/2023]
Abstract
Objective: Hepatic steatosis causes nonalcoholic fatty liver disease and may progress to fibrosis. Ultrasound is the first-line approach to examining hepatic steatosis. Fatty droplets in the liver parenchyma alter ultrasound radiofrequency (RF) signal statistical properties. This study proposes using sample entropy, a measure of irregularity in time-series data determined by the dimension [Formula: see text] and tolerance [Formula: see text], for ultrasound parametric imaging of hepatic steatosis and fibrosis. Methods: Liver donors and patients were enrolled, and their hepatic fat fraction (HFF) ([Formula: see text]), steatosis grade ([Formula: see text]), and fibrosis score ([Formula: see text]) were measured to verify the results of sample entropy imaging using sliding-window processing of ultrasound RF data. Results: The sample entropy calculated using [Formula: see text] 4 and [Formula: see text] was highly correlated with the HFF when a small window with a side length of one pulse was used. The areas under the receiver operating characteristic curve for detecting hepatic steatosis that was [Formula: see text]mild, [Formula: see text]moderate, and [Formula: see text]severe were 0.86, 0.90, and 0.88, respectively, and the area was 0.87 for detecting liver fibrosis in individuals with significant steatosis. Discussion/Conclusions: Ultrasound sample entropy imaging enables the identification of time-series patterns in RF signals received from the liver. The algorithmic scheme proposed in this study is compatible with general ultrasound pulse-echo systems, allowing clinical fibrosis risk evaluations of individuals with developing hepatic steatosis.
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Affiliation(s)
- Hsien-Jung Chan
- Department of Medical Imaging and Radiological SciencesCollege of Medicine, Chang Gung UniversityTaoyuan333323Taiwan
| | - Zhuhuang Zhou
- Department of Biomedical EngineeringFaculty of Environment and LifeBeijing University of TechnologyBeijing100124China
| | - Jui Fang
- X-Dimension Center for Medical Research and TranslationChina Medical University HospitalTaichung40447Taiwan
| | - Dar-In Tai
- Department of Gastroenterology and HepatologyChang Gung Memorial Hospital at LinkouTaoyuan333423Taiwan
| | - Jeng-Hwei Tseng
- Department of Medical Imaging and InterventionChang Gung Memorial Hospital at LinkouTaoyuan333423Taiwan
| | - Ming-Wei Lai
- Division of Pediatric GastroenterologyDepartment of PediatricsChang Gung Memorial Hospital at LinkouTaoyuan333423Taiwan
| | - Bao-Yu Hsieh
- Department of Medical Imaging and Radiological SciencesCollege of Medicine, Chang Gung UniversityTaoyuan333323Taiwan
- Department of Medical Imaging and InterventionChang Gung Memorial Hospital at LinkouTaoyuan333423Taiwan
| | - Tadashi Yamaguchi
- Center for Frontier Medical EngineeringChiba UniversityChiba263-8522Japan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological SciencesCollege of Medicine, Chang Gung UniversityTaoyuan333323Taiwan
- Division of Pediatric GastroenterologyDepartment of PediatricsChang Gung Memorial Hospital at LinkouTaoyuan333423Taiwan
- Institute for Radiological Research, Chang Gung UniversityTaoyuan333323Taiwan
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18
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Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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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19
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Tehrani AKZ, Amiri M, Rosado-Mendez IM, Hall TJ, Rivaz H. Ultrasound Scatterer Density Classification Using Convolutional Neural Networks and Patch Statistics. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2021; 68:2697-2706. [PMID: 33900913 DOI: 10.1109/tuffc.2021.3075912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Quantitative ultrasound (QUS) can reveal crucial information on tissue properties, such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or underdeveloped speckle (UDS), respectively. Conventionally, the scatterer density has been classified using estimated statistical parameters of the amplitude of backscattered echoes. However, if the patch size is small, the estimation is not accurate. These parameters are also highly dependent on imaging settings. In this article, we adapt convolutional neural network (CNN) architectures for QUS and train them using simulation data. We further improve the network's performance by utilizing patch statistics as additional input channels. Inspired by deep supervision and multitask learning, we propose a second method to exploit patch statistics. We evaluate the networks using simulation data and experimental phantoms. We also compare our proposed methods with different classic and deep learning models and demonstrate their superior performance in the classification of tissues with different scatterer density values. The results also show that we are able to classify scatterer density in different imaging parameters with no need for a reference phantom. This work demonstrates the potential of CNNs in classifying scatterer density in ultrasound images.
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20
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Niu S, Huang J, Li J, Liu X, Wang D, Wang Y, Shen H, Qi M, Xiao Y, Guan M, Li D, Liu F, Wang X, Xiong Y, Gao S, Wang X, Yu P, Zhu J. Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data. Quant Imaging Med Surg 2021; 11:2052-2061. [PMID: 33936986 DOI: 10.21037/qims-20-919] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background It is challenging to differentiate between phyllodes tumors (PTs) and fibroadenomas (FAs). Artificial intelligence (AI) can provide quantitative information regarding the morphology and textural features of lesions. This study attempted to use AI to evaluate the ultrasonic images of PTs and FAs and to explore the diagnostic performance of AI features in the differential diagnosis of PTs and FAs. Methods A total of 40 PTs and 290 FAs <5 cm in maximum diameter found in female patients were retrospectively analyzed. All tumors were segmented by doctors, and the features of the lesions were collated, including circularity, height-to-width ratio, margin spicules, margin coarseness (MC), margin indistinctness, margin lobulation (ML), internal calcification, angle between the long axis of the lesion and skin, energy, grey entropy, and grey mean. The differences between PTs and FAs were analyzed, and the diagnostic performance of AI features in the differential diagnosis of PTs and FAs was evaluated. Results Statistically significant differences (P<0.05) were found in the height-to-width ratio, ML, energy, and grey entropy between the PTs and FAs. Receiver operating characteristic (ROC) curve analysis of single features showed that the area under the curve [(AUC) 0.759] of grey entropy was the largest among the four features with statistically significant differences, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.925, 0.459, 0.978, and 0.190, respectively. When considering the combinations of the features, the combination of height-to-width ratio, margin indistinctness, ML, energy, grey entropy, and internal calcification was the most optimal of the combinations of features with an AUC of 0.868, and a sensitivity, specificity, PPV, and NPV of 0.734, 0.900, 0.982, and 0.316, respectively. Conclusions Quantitative analysis of AI can identify subtle differences in the morphology and textural features between small PTs and FAs. Comprehensive consideration of multiple features is important for the differential diagnosis of PTs and FAs.
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Affiliation(s)
- Sihua Niu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Jianhua Huang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jia Li
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Xueling Liu
- Department of Ultrasound, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning, China
| | - Dan Wang
- Department of Ultrasound, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning, China
| | - Yingyan Wang
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Huiming Shen
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Min Qi
- Department of Ultrasound, Zhongda Hospital Southeast University, Nanjing, China
| | - Yi Xiao
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Mengyao Guan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Diancheng Li
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Feifei Liu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Xiuming Wang
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Yu Xiong
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Siqi Gao
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Xue Wang
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Ping Yu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
| | - Jia'an Zhu
- Department of Ultrasound, Peking University People's Hospital, Beijing, China
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21
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Chang TY, Chang SH, Lin YH, Ho WC, Wang CY, Jeng WJ, Wan YL, Tsui PH. Utility of quantitative ultrasound in community screening for hepatic steatosis. ULTRASONICS 2021; 111:106329. [PMID: 33338730 DOI: 10.1016/j.ultras.2020.106329] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/10/2020] [Accepted: 12/01/2020] [Indexed: 06/12/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Quantitative ultrasound facilitates clinical grading of hepatic steatosis (the early stage of NAFLD). However, the utility of quantitative ultrasound as a first-line method for community screening of hepatic steatosis remains unclear. Therefore, this study aimed to investigate the utility of quantitative ultrasound to screen for hepatic steatosis and for metabolic evaluation at the community level. In total, 278 participants enrolled from a community satisfied the study criteria. Each subject underwent anthropometric and biochemical examinations, and abdominal ultrasound imaging was performed to measure the controlled attenuation (CAP), integrated backscatter (IB), and information Shannon entropy (ISE). The assessment outcomes were compared with the fatty liver index (FLI), hepatic steatosis index (HSI), metabolic syndrome (MetS), and insulin resistance to evaluate the screening performance through the area under the receiver operating characteristic curve (AUROC) and Delong's test. Ultrasound ISE, CAP, and IB were effective in screening hepatic steatosis, MetS, and insulin resistance. In screening for hepatic steatosis, the AUROCs of ISE, CAP, and IB were 0.85, 0.83, and 0.80 (the cutoff FLI = 60), respectively, and 0.84, 0.75, 0.77 (the cutoff HSI = 36), respectively, and those for the evaluation of MetS and insulin resistance were 0.79, 0.75, 0.79, respectively, and 0.83, 0.76, 0.78, respectively. Delong's test revealed that ISE outperformed CAP and IB for the detection of hepatic steatosis and insulin resistance (P < .05). Based on the present results, ultrasound ISE is a potential imaging biomarker during first-line community screening of hepatic steatosis and insulin resistance.
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Affiliation(s)
- Tu-Yung Chang
- Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Shu-Hung Chang
- Graduate Institute of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan, Taiwan; Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ying-Hsiu Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wen-Chao Ho
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan; Department of Nursing & Graduate Institute of Nursing, Asia University, Taichung, Taiwan
| | - Chiao-Yin Wang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wen-Juei Jeng
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, 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; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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22
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Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy. Sci Rep 2021; 11:2501. [PMID: 33510306 PMCID: PMC7844231 DOI: 10.1038/s41598-021-82141-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 01/06/2021] [Indexed: 02/07/2023] Open
Abstract
To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.
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23
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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] [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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Tsai YW, Zhou Z, Gong CSA, Tai DI, Cristea A, Lin YC, Tang YC, Tsui PH. Ultrasound Detection of Liver Fibrosis in Individuals with Hepatic Steatosis Using the Homodyned K Distribution. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:84-94. [PMID: 33109381 DOI: 10.1016/j.ultrasmedbio.2020.09.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 08/15/2020] [Accepted: 09/13/2020] [Indexed: 02/07/2023]
Abstract
Acoustic structure quantification (ASQ) based on the analysis of ultrasound backscattered statistics has been reported to detect liver fibrosis without significant hepatic steatosis. This study proposed using ultrasound parametric imaging based on the parameter α of the homodyned K (HK) distribution for staging liver fibrosis in patients with significant hepatic steatosis. Raw ultrasound image data were acquired from patients (n = 237) to construct B-mode and HK α parametric images, which were compared with the focal disturbance (FD) ratio obtained from ASQ on the basis of histologic evidence (METAVIR fibrosis score and hepatic steatosis severity). The data were divided into group I (n = 173; normal to mild hepatic steatosis) and group II (n = 64; with moderate to severe hepatic steatosis) for statistical analysis through one-way analysis of variance and receiver operating characteristic (ROC) curve analysis. The results showed that the HK α parameter monotonically decreased as the liver fibrosis stage increased (p < .05); concurrently, the FD ratio increased (p < .05). For group I, the areas under the ROC (AUROCs) obtained using the FD ratio and the α parameter (AUROCFD and AUROCα) were, respectively, 0.56 and 0.55, 0.68 and 0.68, 0.64 and 0.64 and 0.62 and 0.62 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. The values of AUROCFD and AUROCα for group II were, respectively, 0.88 and 0.91, 0.81 and 0.81, 0.77 and 0.76 and 0.78 and 0.73 for diagnosing liver fibrosis ≥F1, ≥F2, ≥F3 and ≥F4. As opposed to previous studies, ASQ was found to fail in characterizing liver fibrosis in group I; however, it was workable for identifying liver fibrosis in patients with significant hepatic steatosis (group II). Compared with ASQ, HK imaging provided improved diagnostic performance in the early detection of liver fibrosis coexisting with moderate to severe hepatic steatosis. Ultrasound HK imaging is recommended as a strategy to evaluate early fibrosis risk in patients with significant hepatic steatosis.
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Affiliation(s)
- Yu-Wei Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Cihun-Siyong Alex Gong
- Department of Electrical Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Ophthalmology, 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
| | - Anca Cristea
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Yu-Ching Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, at Keelung and Chang Gung University, Taiwan
| | - Ya-Chun Tang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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Wang D, Liu D, Sang Y, Zhang Y, Wan M, Diederich CJ. In vivo Nakagami-m parametric imaging of microbubble-enhanced ultrasound regulated by RF and VF processing techniques. Med Phys 2020; 47:5659-5668. [PMID: 32965033 DOI: 10.1002/mp.14474] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 07/28/2020] [Accepted: 08/19/2020] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Application of the Nakagami statistical model and associated m parameter has the potential to suppress artifacts from adjustable system parameters and operator selections typical in echo amplitude-coded microbubble-enhanced ultrasound (MEUS). However, the feasibility of applying m estimation and determination of the associated Nakagami distribution features for in vivo MEUS remain to be investigated. Sensitivity and discriminability of m-coded MEUS are often limited since raw envelopes are regulated by complex radiofrequency (RF) and video-frequency (VF) processing. This study aims to develop an improved imaging approach for the m parameter estimation which can overcome the above limitations in in vivo condition. METHOD The regulation effects of RF processing of pulse-inversion (PI) harmonic detection techniques and VF processing of logarithmic compression in Nakagami distributions were investigated in MEUS. A window-modulated compounding moment estimator was developed to estimate the MEUS m values. The sensitivity and discriminability of m-coded MEUS were quantified with contrast-to-tissue ratio (CTR), contrast-to-noise ratio (CNR), and axial and lateral resolutions, which were validated through in vivo perfusion experiments on rabbit kidneys. RESULTS Regulated by RF and VF processing, the distributions of MEUS obeyed the Nakagami statistical model. The Nakagami-fitted correlation coefficient was 0.996 ± 0.003 (P < 0.05 in the t test and P < 0.001 in the Kolmogorov-Smirnov test). Among each of the m-coded MEUS methods, the logarithmic m-coded PI-MEUS scheme effectively characterized the peripheral rim perfusion features and details within the renal cortex. The CTR and CNR in this region reached 7.9 ± 1.5 dB and 34.4 ± 1.7 dB, respectively, which were higher than those of standard amplitude-coded MEUS; and the axial and lateral resolutions were 1.02 ± 0.02 and 0.91 ± 0.02 mm, respectively, which were slightly longer than those of amplitude-coded MEUS. CONCLUSIONS The Nakagami statistical model could characterize MEUS even when the envelope distributions were regulated by RF and VF processing. The logarithmic m-coded PI-MEUS scheme significantly improved the sensitivity, discriminability, and robustness of m estimation in MEUS. The scheme provides an option to remove artifacts in echo amplitude-coded MEUS and to distinctly characterize the inherent microvasculature enhanced by microbubbles, with potential to improve and expand the role of MEUS in diagnostic ultrasound.
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Affiliation(s)
- Diya Wang
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.,Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an, 710049, P. R. China
| | - Dong Liu
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
| | - Yuchao Sang
- Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an, 710049, P. R. China
| | - Yu Zhang
- Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an, 710049, P. R. China
| | - Mingxi Wan
- Department of Biomedical Engineering, School of Life Science and Technology, Xi' an Jiaotong University, Xi'an, 710049, P. R. China
| | - Chris J Diederich
- Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA
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Alpar O. Nakagami imaging with related distributions for advanced thermogram pseudocolorization. J Therm Biol 2020; 93:102704. [PMID: 33077125 DOI: 10.1016/j.jtherbio.2020.102704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/29/2020] [Accepted: 08/17/2020] [Indexed: 11/25/2022]
Abstract
Pseudocoloring algorithms embedded in the software of thermal cameras gradually colorize original intensity thermograms generated by detecting temperatures and contrast. Maximum and minimum based algorithms, however, executed by thresholding, applied to intensity thermograms for revealing and coloring the outliers instead. Although the common pseudocoloring protocols employed for general purposes may provide crucial information on the superficial contrast between radiation emitted by various sources; their common kernel is not sufficient for detecting and differentiating high radiated regions from surrounding areas, which is mandatory for recognition of abnormalities. Therefore, we propose novel imaging methodology based on Nakagami and related distributions, including gamma, Rayleigh, Weibull, chi-square and exponential, for enhancing thermal images and also for creating adequate discrimination. We initially define the boundaries of tumor and surrounding area in a synthetically generated breast thermogram already diagnosed as retroareolar tumor. Using Nakagami and transformations supported by mathematical foundations, we conducted several experiments to find the discrimination factor of the pseudocoloring techniques by calculating difference of average contrast between the tumor and the surrounding area. The performance is greatly encouraging that we achieved considerably better discrimination factor, designated for this study, up to 106.80 compared to the results of existing built-in pseudocolorization methods computed as 11.56.
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Affiliation(s)
- Orcan Alpar
- Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec, Kralove, 50003, Czech Republic.
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Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A. BMC Cancer 2020; 20:959. [PMID: 33008320 PMCID: PMC7532640 DOI: 10.1186/s12885-020-07413-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
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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, SWITZERLAND) 2020; 22:E1006. [PMID: 33286775 PMCID: PMC7597079 DOI: 10.3390/e22091006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [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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Fang F, Fang J, Li Q, Tai DI, Wan YL, Tamura K, Yamaguchi T, Tsui PH. Ultrasound Assessment of Hepatic Steatosis by Using the Double Nakagami Distribution: A Feasibility Study. Diagnostics (Basel) 2020; 10:diagnostics10080557. [PMID: 32759867 PMCID: PMC7459679 DOI: 10.3390/diagnostics10080557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/28/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Ultrasound imaging is a first-line assessment tool for hepatic steatosis. Properties of tissue microstructures correlate with the statistical distribution of ultrasound backscattered signals, which can be described by the Nakagami distribution (a widely adopted approximation of backscattered statistics). The double Nakagami distribution (DND) model, which combines two Nakagami distributions, was recently proposed for using high-frequency ultrasound to analyze backscattered statistics corresponding to lipid droplets in the fat-infiltrated liver. This study evaluated the clinical feasibility of the DND model in ultrasound parametric imaging of hepatic steatosis by conducting clinical experiments using low-frequency ultrasound dedicated to general abdominal examinations. A total of 204 patients were recruited, and ultrasound image raw data were acquired using a 3.5 MHz array transducer for DND parametric imaging using the sliding window technique. The DND parameters were compared with hepatic steatosis grades identified histologically. A receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance. The results indicated that DND parametric imaging constructed using a sliding window with the side length of five times the pulse length of the transducer provided stable and reliable DND parameter estimations and visualized changes in the backscattered statistics caused by hepatic steatosis. The DND parameter increased with the hepatic steatosis grade. The areas under the ROC curve for identifying hepatic steatosis were 0.76 (≥mild), 0.81 (≥moderate), and 0.82 (≥severe). When using low-frequency ultrasound, DND imaging allows the clinical detection of hepatic steatosis and reflects information associated with lipid droplets in the fat-infiltrated liver.
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Affiliation(s)
- Feng Fang
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (F.F.); (Q.L.)
| | - Jui Fang
- x-Dimension Center for Medical Research and Translation, China Medical University Hospital, Taichung 404332, Taiwan;
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (F.F.); (Q.L.)
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 33305, Taiwan;
| | - Yung-Liang Wan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
| | - Kazuki Tamura
- Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, Shizuoka 431-3192, Japan;
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
- Correspondence: (T.Y.); (P.-H.T.)
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
- Correspondence: (T.Y.); (P.-H.T.)
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Koh RGL, Behr M, Kirkwood M, Kumbhare D. Effect of Neighbourhood Size in Entropy Mapping of Ultrasound Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2015-2018. [PMID: 33018399 DOI: 10.1109/embc44109.2020.9176526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Image filtering is a technique that can create additional visual representations of the original image. Entropy filtering is a specific application that can be used to highlight randomness of pixel grayscale intensities within an image. These image map created from filtering are based on the number of surrounding neighbourhood of pixels considered. However, there is no standard procedure for determining the correct "neighbourhood size" to use. We investigated the effects of neighbourhood size on the entropy calculation and provide a standardized approach for determining an appropriate neighbourhood size in entropy filtering in a musculoskeletal application. Ten healthy subjects showing no symptoms related to neuromuscular disease were recruited and ultrasound images of their trapezius muscle were acquired. The muscle regions in the images were manually isolated and regions of interest with varying neighbourhood sizes (increasing by 2 pixels) from 3x3 to 61X61 pixels were extracted. The entropy, relative signal entropy over noise entropy, statistical effect size as well as the percentage change of the effect size and instantaneous slope of the effect size was examined. The analysis showed that a neighbourhood size within the range of 21-25 pixels provides the maximum amount of information gained and coincides with a percentage change of the effect size of less than 5% and instantaneous slopes < 0.05.
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Yan D, Li Q, Lin CW, Shieh JY, Weng WC, Tsui PH. Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging. ENTROPY 2020; 22:e22070715. [PMID: 33286487 PMCID: PMC7517253 DOI: 10.3390/e22070715] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/16/2020] [Accepted: 06/25/2020] [Indexed: 12/14/2022]
Abstract
Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.
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Affiliation(s)
- Dong Yan
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (D.Y.); (Q.L.)
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China; (D.Y.); (Q.L.)
| | - Chia-Wei Lin
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu 30059, Taiwan;
| | - Jeng-Yi Shieh
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei 100229, Taiwan;
| | - Wen-Chin Weng
- Department of Pediatrics, National Taiwan University Hospital, and College of Medicine, National Taiwan University, Taipei 100233, Taiwan
- Department of Pediatric Neurology, National Taiwan University Children’s Hospital, Taipei 100226, Taiwan
- Correspondence: (W.-C.W.); (P.-H.T.)
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
- Correspondence: (W.-C.W.); (P.-H.T.)
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Yang K, Li Q, Liu HL, Chen CK, Huang CW, Chen JR, Tsai YW, Zhou Z, Tsui PH. Frequency-domain CBE imaging for ultrasound localization of the HIFU focal spot: a feasibility study. Sci Rep 2020; 10:5468. [PMID: 32214201 PMCID: PMC7096526 DOI: 10.1038/s41598-020-62363-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 03/10/2020] [Indexed: 11/25/2022] Open
Abstract
High-intensity focused ultrasound (HIFU) is a well-accepted tool for noninvasive thermal therapy. To control the quality of HIFU treatment, the focal spot generated in tissues must be localized. Ultrasound imaging can monitor heated regions; in particular, the change in backscattered energy (CBE) allows parametric imaging to visualize thermal information in the tissue. Conventional CBE imaging constructed in the spatial domain may be easily affected by noises when the HIFU focal spot is visualized. This study proposes frequency-domain CBE imaging to improve noise tolerance and image contrast in HIFU focal spot monitoring. Phantom experiments were performed in a temperature-controlled environment. HIFU of 2.12 MHz was applied to the phantoms, during which a clinical scanner equipped with a 3-MHz convex array transducer was used to collect raw image data consisting of backscattered signals for B-mode, spatial-, and frequency-domain CBE imaging. Concurrently, temperature changes were measured at the focal spot using a thermocouple for comparison with CBE values by calculating the correlation coefficient r. To further analyze CBE image contrast levels, a contrast factor was introduced, and an independent t-test was performed to calculate the probability value p. Experimental results showed that frequency-domain CBE imaging performed well in thermal distribution visualization, enabling quantitative detection of temperature changes. The CBE value calculated in the frequency domain also correlated strongly with that obtained using the conventional spatial-domain approach (r = 0.97). In particular, compared with the image obtained through the conventional method, the contrast of the CBE image obtained using the method based on frequency-domain analysis increased by 2.5-fold (4 dB; p < 0.05). Frequency-domain computations may constitute a new strategy when ultrasound CBE imaging is used to localize the focal spot in HIFU treatment planning.
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Affiliation(s)
- Kun Yang
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Hao-Li Liu
- Department of Electrical Engineering, Chang-Gung University, Taoyuan, Taiwan
| | - Chin-Kuo Chen
- Department of Otolaryngology - Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Cheng-Wei Huang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Jheng-Ru Chen
- Department of Medical Imaging and Radiological 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
| | - Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan. .,Medical Imaging Research Center, 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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Zhou Z, Fang J, Cristea A, Lin YH, Tsai YW, Wan YL, Yeow KM, Ho MC, Tsui PH. Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: An animal study. ULTRASONICS 2020; 101:106001. [PMID: 31505328 DOI: 10.1016/j.ultras.2019.106001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 08/26/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
Ultrasound is the first-line tool for screening hepatic steatosis. Statistical distributions can be used to model the backscattered signals for liver characterization. The Nakagami distribution is the most frequently adopted model; however, the homodyned K (HK) distribution has received attention due to its link to physical meaning and improved parameter estimation through X- and U-statistics (termed "XU"). To assess hepatic steatosis, we proposed HK parametric imaging based on the α parameter (a measure of the number of scatterers per resolution cell) calculated using the XU estimator. Using a commercial system equipped with a 7-MHz linear array transducer, phantom experiments were performed to suggest an appropriate window size for α imaging using the sliding window technique, which was further applied to measuring the livers of rats (n = 66) with hepatic steatosis induced by feeding the rats a methionine- and choline-deficient diet. The relationships between the α parameter, the stage of hepatic steatosis, and histological features were verified by the correlation coefficient r, one-way analysis of variance, and regression analysis. The phantom results showed that the window side length corresponding to five times the pulse length supported a reliable α imaging. The α parameter showed a promising performance for grading hepatic steatosis (p < 0.05; r2 = 0.68). Compared with conventional Nakagami imaging, α parametric imaging provided significant information associated with fat droplet size (p < 0.05; r2 = 0.53), enabling further analysis and evaluation of severe hepatic steatosis.
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Affiliation(s)
- Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Jui Fang
- 3D Printing Medical Research Center, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Anca Cristea
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Ying-Hsiu Lin
- Department of Medical Imaging and Radiological 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
| | - Yung-Liang Wan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Kee-Min Yeow
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Ming-Chih Ho
- Department of Surgery, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan.
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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Lee YL, Huang YL, Chu SY, Chan WH, Cheng MH, Lin YH, Chang TY, Yeh CK, Tsui PH. Characterization of limb lymphedema using the statistical analysis of ultrasound backscattering. Quant Imaging Med Surg 2020; 10:48-56. [PMID: 31956528 PMCID: PMC6960425 DOI: 10.21037/qims.2019.10.12] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 10/08/2019] [Indexed: 11/06/2022]
Abstract
BACKGROUND Lymphedema is a disease in which tissue swelling is caused by interstitial fluid retention in subcutaneous tissue. It is caused by a compromised lymphatic system. Lymphoscintigraphy is the current and primary modality used to assess lymphatic system dysfunction. Ultrasound elastography is a complementary tool used for evaluating the tissue stiffness of the lymphedematous limb. Tissue stiffness implies the existence of changes in tissue microstructures. However, ultrasound features related to tissue microstructures are neglected in clinical assessments of lymphedematous limbs. In this study, we aimed to evaluate the lymphedematous diagnostic values of ultrasound Nakagami and entropy imaging, which are, respectively, model- and nonmodel-based backscattered statistical analysis methods for scatterer characterization. METHODS A total of 60 patients were recruited, and lymphoscintigraphy was used to score the patient's clinical severity of each of their limb lymphedema (0: normal; 1: partial lymphatic obstruction; and 2: total lymphatic obstruction). We performed ultrasound examinations to acquire ultrasound backscattered signals for B-mode, Nakagami, and entropy imaging. The envelope amplitude, Nakagami, and entropy values, as a function of the patients' lymphatic obstruction grades, were expressed in terms of their median and interquartile range (IQR). The values were then used in both an independent t test and a receiver operating characteristic (ROC) curve analysis. RESULTS For each increase in a patient's score from 0 to 2, the envelope amplitude values were 405.44 (IQR: 238.72-488.17), 411.52 (IQR: 298.53-644.25), and 476.37 (IQR: 348.86-648.16), respectively. The Nakagami parameters were 0.16 (IQR: 0.14-0.22), 0.26 (IQR: 0.23-0.34), and 0.24 (IQR: 0.16-0.36), respectively, and the entropy values were 4.55 (IQR: 4.41-4.66), 4.86 (IQR: 4.78-4.99), and 4.87 (IQR: 4.81-4.97), respectively. The P values between the normal control and lymphedema groups obtained from B-mode and Nakagami analysis were larger than 0.05; whereas that of entropy imaging was smaller than 0.05. The areas under the ROC curve for B-mode, Nakagami, and entropy imaging were 0.64 (sensitivity: 70%; specificity: 47.5%), 0.75 (sensitivity: 70%; specificity: 75%), and 0.94 (sensitivity: 95%; specificity: 87.5%), respectively. CONCLUSIONS The current findings demonstrated the diagnostic values of ultrasound Nakagami and entropy imaging techniques. In particular, the use of non-model-based entropy imaging enables for improved performance when characterizing limb lymphedema.
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Affiliation(s)
- Ya-Lun Lee
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Yen-Ling Huang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Sung-Yu Chu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Wen-Hui Chan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Ming-Huei Cheng
- Division of Reconstructive Microsurgery, Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung University, College of Medicine, Taoyuan, Taiwan
| | - Ying-Hsiu Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tu-Yung Chang
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Chih-Kuang Yeh
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- 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
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Wang D, Sang Y, Zhang X, Hu H, Lu S, Zhang Y, Fu C, Cloutier G, Wan M. Numerical and experimental investigation of impacts of nonlinear scattering encapsulated microbubbles on Nakagami distribution. Med Phys 2019; 46:5467-5477. [PMID: 31536640 DOI: 10.1002/mp.13833] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 08/06/2019] [Accepted: 09/12/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The Nakagami statistical model and Nakagami shape parameter m have been widely used in linear tissue characterization and preliminarily characterized the envelope distributions of nonlinear encapsulated microbubbles (EMBs). However, the Nakagami distribution of nonlinear scattering EMBs lacked a systematical investigation. Thus, this study aimed to investigate the Nakagami distribution of EMBs and illustrate the impact of EMBs' nonlinearity on the Nakagami model. METHOD A group of simulated EMB phantoms and in vitro EMB dilutions with an increasing concentration distribution under various EMB nonlinearities, as regulated by acoustic parameters, were characterized by using the window-modulated compounding Greenwood-Durand estimator. RESULTS Raw envelope histograms of simulated and in vitro EMBs were well matched with the Nakagami distribution with a high correlation coefficient of 0.965 ± 0.021 (P < 0.005). The mean values and gradients of m parameters of simulated and in vitro EMBs were smaller than those of linear scatterers due to the stronger nonlinearity. These m values exhibited a quasi-linear improvement with the increase in second harmonic nonlinear-to-linear component ratio regulated by pulse lengths and excitation frequencies at low- and high-concentration conditions. CONCLUSIONS The Nakagami distribution was suitable for the EMBs characterization but the corresponding m parameter was affected by the EMBs' nonlinearity. These validations provided support and nonlinear impact assessment for the EMBs' characterization using the Nakagami statistical model in the future.
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Affiliation(s)
- Diya Wang
- University of Montreal Hospital Research Center, Montreal, QC, H2X 0A9, Canada.,Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Yuchao Sang
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Xinyu Zhang
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Hong Hu
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Shukuan Lu
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Yu Zhang
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
| | - Chaoying Fu
- Center Lab of Longhua Branch and Department of Infectious disease, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, Shenzhen, 518120, China.,Institut National de la Recherche Scientifique (INRS) EMT Center, Varennes, QC, J3X 1S2, Canada
| | - Guy Cloutier
- University of Montreal Hospital Research Center, Montreal, QC, H2X 0A9, Canada
| | - Mingxi Wan
- Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 71049, P. R. China
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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 IN MEDICINE & BIOLOGY 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] [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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Klimonda Z, Karwat P, Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Litniewski J. Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue. Sci Rep 2019; 9:7963. [PMID: 31138822 PMCID: PMC6538710 DOI: 10.1038/s41598-019-44376-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 05/16/2019] [Indexed: 12/17/2022] Open
Abstract
The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.
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Affiliation(s)
- Ziemowit Klimonda
- Institute of Fundamental Technological Research, Department of Ultrasound, Pawińskiego 5b, 02-106, Warsaw, Poland.
| | - Piotr Karwat
- Institute of Fundamental Technological Research, Department of Ultrasound, Pawińskiego 5b, 02-106, Warsaw, Poland
| | - Katarzyna Dobruch-Sobczak
- Institute of Fundamental Technological Research, Department of Ultrasound, Pawińskiego 5b, 02-106, Warsaw, Poland.,Maria Skłodowska-Curie Memorial Cancer Centre and Institute of Oncology, Wawelska 15b, 02-034, Warsaw, Poland
| | | | - Jerzy Litniewski
- Institute of Fundamental Technological Research, Department of Ultrasound, Pawińskiego 5b, 02-106, Warsaw, Poland
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Steifer T, Lewandowski M. Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome. ENTROPY 2018; 20:e20120893. [PMID: 33266617 PMCID: PMC7512475 DOI: 10.3390/e20120893] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [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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40
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Zhou Z, Tai DI, Wan YL, Tseng JH, Lin YR, Wu S, Yang KC, Liao YY, Yeh CK, Tsui PH. Hepatic Steatosis Assessment with Ultrasound Small-Window Entropy Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:1327-1340. [PMID: 29622501 DOI: 10.1016/j.ultrasmedbio.2018.03.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/21/2018] [Accepted: 03/01/2018] [Indexed: 02/06/2023]
Abstract
Nonalcoholic fatty liver disease is a type of hepatic steatosis that is not only associated with critical metabolic risk factors but can also result in advanced liver diseases. Ultrasound parametric imaging, which is based on statistical models, assesses fatty liver changes, using quantitative visualization of hepatic-steatosis-caused variations in the statistical properties of backscattered signals. One constraint with using statistical models in ultrasound imaging is that ultrasound data must conform to the distribution employed. Small-window entropy imaging was recently proposed as a non-model-based parametric imaging technique with physical meanings of backscattered statistics. In this study, we explored the feasibility of using small-window entropy imaging in the assessment of fatty liver disease and evaluated its performance through comparisons with parametric imaging based on the Nakagami distribution model (currently the most frequently used statistical model). Liver donors (n = 53) and patients (n = 142) were recruited to evaluate hepatic fat fractions (HFFs), using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate and severe), using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct B-mode, small-window entropy and Nakagami images to correlate with HFF analyses and fatty liver stages. The diagnostic values of the imaging methods were evaluated using receiver operating characteristic curves. The results demonstrated that the entropy value obtained using small-window entropy imaging correlated well with log10(HFF), with a correlation coefficient r = 0.74, which was higher than those obtained for the B-scan and Nakagami images. Moreover, small-window entropy imaging also resulted in the highest area under the receiver operating characteristic curve (0.80 for stages equal to or more severe than mild; 0.90 for equal to or more severe than moderate; 0.89 for severe), which indicated that non-model-based entropy imaging-using the small-window technique-performs more favorably than other techniques in fatty liver assessment.
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Affiliation(s)
- Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China; Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan, Taiwan
| | - Yung-Liang Wan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Jeng-Hwei Tseng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Ru Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Kuen-Cheh Yang
- Department of Family Medicine, National Taiwan University Hospital, Beihu Branch, Taipei, Taiwan
| | - Yin-Yin Liao
- Department of Biomedical Engineering, Hungkuang University, Taichung, Taiwan
| | - Chih-Kuang Yeh
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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