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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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Surowiec RK, Does MD, Nyman JS. In Vivo Assessment of Bone Quality Without X-rays. Curr Osteoporos Rep 2024; 22:56-68. [PMID: 38227178 PMCID: PMC11050740 DOI: 10.1007/s11914-023-00856-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/22/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW This review summarizes recent advances in the assessment of bone quality using non-X-ray techniques. RECENT FINDINGS Quantitative ultrasound (QUS) provides multiple measurements of bone characteristics based on the propagation of sound through bone, the attenuation of that sound, and different processing techniques. QUS parameters and model predictions based on backscattered signals can discriminate non-fracture from fracture cases with accuracy comparable to standard bone mineral density (BMD). With advances in magnetic resonance imaging (MRI), bound water and pore water, or a porosity index, can be quantified in several long bones in vivo. Since such imaging-derived measurements correlate with the fracture resistance of bone, they potentially provide new BMD-independent predictors of fracture risk. While numerous measurements of mineral, organic matrix, and bound water by Raman spectroscopy correlate with the strength and toughness of cortical bone, the clinical assessment of person's bone quality using spatially offset Raman spectroscopy (SORS) requires advanced spectral processing techniques that minimize contaminating signals from fat, skin, and blood. Limiting exposure of patients to ionizing radiation, QUS, MRI, and SORS has the potential to improve the assessment of fracture risk and track changes of new therapies that target bone matrix and micro-structure.
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Affiliation(s)
- Rachel K Surowiec
- Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Dr., West Lafayette, IN, 47907, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA
| | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, TN, 37232, USA
- Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Ave. S., Nashville, TN, 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, 1161 21st Ave. S., Nashville, TN, 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, 400 24th Ave. S., Nashville, TN, 37212, USA
| | - Jeffry S Nyman
- Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, TN, 37232, USA.
- Department of Orthopaedic Surgery, Vanderbilt University Medical Center, 1215 21st Ave. S., Suite 4200, Nashville, TN, 37232, USA.
- Vanderbilt Center for Bone Biology, Vanderbilt University Medical Center, 1211 Medical Center Dr., Nashville, TN, 37212, USA.
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, 37212, USA.
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Gao R, Tsui PH, Wu S, Tai DI, Bin G, Zhou Z. Ultrasound Entropy Imaging Based on the Kernel Density Estimation: A New Approach to Hepatic Steatosis Characterization. Diagnostics (Basel) 2023; 13:3646. [PMID: 38132230 PMCID: PMC10742695 DOI: 10.3390/diagnostics13243646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
In this paper, we present the kernel density estimation (KDE)-based parallelized ultrasound entropy imaging and apply it for hepatic steatosis characterization. A KDE technique was used to estimate the probability density function (PDF) of ultrasound backscattered signals. The estimated PDF was utilized to estimate the Shannon entropy to construct parametric images. In addition, the parallel computation technique was incorporated. Clinical experiments of hepatic steatosis were conducted to validate the feasibility of the proposed method. Seventy-two participants and 204 patients with different grades of hepatic steatosis were included. The experimental results show that the KDE-based entropy parameter correlates with log10 (hepatic fat fractions) measured by magnetic resonance spectroscopy in the 72 participants (Pearson's r = 0.52, p < 0.0001), and its areas under the receiver operating characteristic curves for diagnosing hepatic steatosis grades ≥ mild, ≥moderate, and ≥severe are 0.65, 0.73, and 0.80, respectively, for the 204 patients. The proposed method overcomes the drawbacks of conventional histogram-based ultrasound entropy imaging, including limited dynamic ranges and histogram settings dependence, although the diagnostic performance is slightly worse than conventional histogram-based entropy imaging. The proposed KDE-based parallelized ultrasound entropy imaging technique may be used as a new ultrasound entropy imaging method for hepatic steatosis characterization.
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Affiliation(s)
- Ruiyang Gao
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (R.G.); (S.W.)
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan;
- Research Center for Radiation Medicine, Chang Gung University, Taoyuan 333323, Taiwan
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (R.G.); (S.W.)
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333423, Taiwan;
| | - Guangyu Bin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (R.G.); (S.W.)
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (R.G.); (S.W.)
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