Three-dimensional Visualization of Ultrasound Backscatter Statistics by Window-modulated Compounding Nakagami Imaging

In this study, the window-modulated compounding (WMC) technique was integrated into three-dimensional (3D) ultrasound Nakagami imaging for improving the spatial visualization of backscatter statistics. A 3D WMC Nakagami image was produced by summing and averaging a number of 3D Nakagami images (number of frames denoted as N) formed using sliding cubes with varying side lengths ranging from 1 to N times the transducer pulse. To evaluate the performance of the proposed 3D WMC Nakagami imaging method, agar phantoms with scatterer concentrations ranging from 2 to 64 scatterers/mm3 were made, and six stages of fatty liver (zero, one, two, four, six, and eight weeks) were induced in rats by methionine-choline-deficient diets (three rats for each stage, total n = 18). A mechanical scanning system with a 5-MHz focused single-element transducer was used for ultrasound radiofrequency data acquisition. The experimental results showed that 3D WMC Nakagami imaging was able to characterize different scatterer concentrations. Backscatter statistics were visualized with various numbers of frames; N = 5 reduced the estimation error of 3D WMC Nakagami imaging in visualizing the backscatter statistics. Compared with conventional 3D Nakagami imaging, 3D WMC Nakagami imaging improved the image smoothness without significant image resolution degradation, and it can thus be used for describing different stages of fatty liver in rats.

[1]  D. Rubens,et al.  Imaging the elastic properties of tissue: the 20 year perspective , 2011, Physics in medicine and biology.

[2]  K. Shung,et al.  Diagnostic Ultrasound: Imaging and Blood Flow Measurements , 2005 .

[3]  Chiao-Yin Wang,et al.  Monitoring Radiofrequency Ablation Using Ultrasound Envelope Statistics and Shear Wave Elastography in the Periablation Period: An In Vitro Feasibility Study , 2016, PloS one.

[4]  Xiaofeng Yang,et al.  Ultrasonic Nakagami-parameter characterization of parotid-gland injury following head-and-neck radiotherapy: a feasibility study of late toxicity. , 2014, Medical physics.

[5]  Po-Hsiang Tsui,et al.  Effects of Estimators on Ultrasound Nakagami Imaging in Visualizing the Change in the Backscattered Statistics from a Rayleigh Distribution to a Pre-Rayleigh Distribution. , 2015, Ultrasound in medicine & biology.

[6]  Po-Hsiang Tsui Minimum Requirement of Artificial Noise Level for Using Noise-Assisted Correlation Algorithm to Suppress Artifacts in Ultrasonic Nakagami Images , 2012, Ultrasonic imaging.

[7]  Hiroyuki Hachiya,et al.  A New Modeling for the Changes in the Distribution of Scatterers in Cirrhotic Liver , 2000 .

[8]  Ricardo Marroquim,et al.  Generation of 3D ultrasound biomicroscopic images: technique validation and in vivo volumetric imaging of rat lateral gastrocnemius , 2015 .

[9]  Yung-Sheng Chen,et al.  Three-dimensional ultrasonic Nakagami imaging for tissue characterization , 2010, Physics in medicine and biology.

[10]  Constantin C Coussios,et al.  Heterogeneous tissue characterization using ultrasound: a comparison of fractal analysis backscatter models on liver tumors , 2016, Ultrasound in medicine & biology.

[11]  Jonathan Mamou,et al.  Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[12]  Po-Hsiang Tsui,et al.  Artifact Reduction of Ultrasound Nakagami Imaging by Combining Multifocus Image Reconstruction and the Noise-Assisted Correlation Algorithm , 2015, Ultrasonic imaging.

[13]  Yan-Wei Lee,et al.  Relationship Between Ultrasound Backscattered Statistics and the Concentration of Fatty Droplets in Livers: An Animal Study , 2013 .

[14]  Miguel Caixinha,et al.  Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques. , 2016, Ultrasound in medicine & biology.

[15]  Hsiang-Yang Ma,et al.  Effects of fatty infiltration in human livers on the backscattered statistics of ultrasound imaging , 2015, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[16]  Hiroyuki Hachiya,et al.  A pilot approach for quantitative assessment of liver fibrosis using ultrasound: preliminary results in 79 cases. , 2006, Journal of hepatology.

[17]  Stefan Neubauer,et al.  Quantitative 3-Dimensional Echocardiography for Accurate and Rapid Cardiac Phenotype Characterization in Mice , 2004, Circulation.

[18]  Jonathan Mamou,et al.  Quantitative Ultrasound in Soft Tissues , 2013, Springer Netherlands.

[19]  Yung-Sheng Chen,et al.  Early detection of liver fibrosis in rats using 3-D ultrasound Nakagami imaging: a feasibility evaluation. , 2014, Ultrasound in medicine & biology.

[20]  Thomas L. Szabo,et al.  Diagnostic Ultrasound Imaging: Inside Out , 2004 .

[21]  John J. Krolewski,et al.  Quantitative volumetric imaging of normal, neoplastic and hyperplastic mouse prostate using ultrasound , 2015, BMC Urology.

[22]  Sheng-Min Huang,et al.  Beamforming effects on generalized Nakagami imaging , 2015, Physics in medicine and biology.

[23]  Catherine Theodoropoulos,et al.  New approaches in small animal echocardiography: imaging the sounds of silence. , 2011, American journal of physiology. Heart and circulatory physiology.

[24]  B. Garra,et al.  AN OVERVIEW OF ELASTOGRAPHY - AN EMERGING BRANCH OF MEDICAL IMAGING. , 2011, Current medical imaging reviews.

[25]  Hiroyuki Hachiya,et al.  B-mode ultrasound with algorithm based on statistical analysis of signals: evaluation of liver fibrosis in patients with chronic hepatitis C. , 2009, AJR. American journal of roentgenology.

[26]  J. Alison Noble,et al.  Nakagami imaging with small windows , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[27]  Yung-Sheng Chen,et al.  Use of nakagami statistics and empirical mode decomposition for ultrasound tissue characterization by a nonfocused transducer. , 2009, Ultrasound in medicine & biology.

[28]  K. Parker,et al.  Deviations from Rayleigh Statistics in Ultrasonic Speckle , 1988, Ultrasonic imaging.

[29]  Hiroyuki Hachiya,et al.  Modeling of the Cirrhotic Liver Considering the Liver Lobule Structure , 1999 .

[30]  Mingxi Wan,et al.  Enhanced Lesion‐to‐Bubble Ratio on Ultrasonic Nakagami Imaging for Monitoring of High‐Intensity Focused Ultrasound , 2014, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[31]  Chiung-Nien Chen,et al.  Ultrasound window-modulated compounding Nakagami imaging: Resolution improvement and computational acceleration for liver characterization. , 2016, Ultrasonics.

[32]  Chung-Chih Lin,et al.  Monitoring Radiofrequency Ablation Using Real-Time Ultrasound Nakagami Imaging Combined with Frequency and Temporal Compounding Techniques , 2015, PloS one.

[33]  Hsiang-Yang Ma,et al.  Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis , 2016, Scientific Reports.

[34]  Chien-Cheng Chang,et al.  Imaging local scatterer concentrations by the Nakagami statistical model. , 2007, Ultrasound in medicine & biology.

[35]  P. Shankar Statistical modeling of scattering from biological media , 2002 .

[36]  T J Hall,et al.  Parametric Ultrasound Imaging from Backscatter Coefficient Measurements: Image Formation and Interpretation , 1990, Ultrasonic imaging.

[37]  P. Shankar A general statistical model for ultrasonic backscattering from tissues , 2000 .

[38]  Chien-Cheng Chang,et al.  Using ultrasound Nakagami imaging to assess liver fibrosis in rats. , 2012, Ultrasonics.

[39]  Guy Cloutier,et al.  Unifying Concepts of Statistical and Spectral Quantitative Ultrasound Techniques , 2016, IEEE Transactions on Medical Imaging.

[40]  Hsiang-Yang Ma,et al.  Window-modulated compounding Nakagami imaging for ultrasound tissue characterization. , 2014, Ultrasonics.

[41]  Hamid Behnam,et al.  Nakagami imaging for detecting thermal lesions induced by high-intensity focused ultrasound in tissue , 2014, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[42]  Hiroyuki Hachiya,et al.  Stability of Quantitative Evaluation Method of Liver Fibrosis Using Amplitude Distribution Model of Fibrotic Liver , 2011 .

[43]  Raj Vuppalanchi,et al.  Presence and significance of microvesicular steatosis in nonalcoholic fatty liver disease. , 2011, Journal of hepatology.

[44]  Po-Hsiang Tsui,et al.  Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals , 2016, Entropy.

[45]  Jae Young Lee,et al.  Hepatic Steatosis: Assessment with Acoustic Structure Quantification of US Imaging. , 2016, Radiology.

[46]  J Alison Noble,et al.  Modeling of errors in Nakagami imaging: illustration on breast mass characterization. , 2014, Ultrasound in medicine & biology.

[47]  Xiaofeng Yang,et al.  Quantitative Ultrasonic Nakagami Imaging of Neck Fibrosis After Head and Neck Radiation Therapy. , 2015, International journal of radiation oncology, biology, physics.