Objective
Modelling ultrasound speckle to characterise tissue properties has generated considerable
interest. As speckle is dependent on the underlying tissue architecture, modelling
it may aid in tasks such as segmentation or disease detection. For the transplanted
kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical
distribution best characterises such speckle. This applies to the regions of the transplanted
kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it
is unclear how these distributions vary by patient variables such as age, sex, body
mass index, primary disease or donor type. These traits may influence speckle modelling
given their influence on kidney anatomy. We investigate these two aims.
Methods
B-mode images from n = 821 kidney transplant recipients (one image per recipient)
were automatically segmented into the cortex, medulla and central echogenic complex
using a neural network. Seven distinct probability distributions were fitted to each
region's histogram, and statistical analysis was performed.
Discussion
The Rayleigh and Nakagami distributions had model parameters that differed significantly
between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck–Leibler
divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11,
p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08,
p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation.
Conclusion
We propose the Nakagami distribution be used to characterize transplanted kidneys
regionally independent of disease etiology and most patient characteristics.
Keywords
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References
- Diagnostic ultrasound imaging: inside out.2nd ed. Academic Press, San Diego, CA2014: 295-363
- Speckle in ultrasound B-mode scans.IEEE Trans Son Ultrason. 1978; 25: 1-6
- Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound.IEEE Trans Ultrason Ferroelectr Freq Control. 2016; 63: 336-351
- A critical review and uniformized representation of statistical distributions modeling the ultrasound echo envelope.Ultrasound Med Biol. 2010; 36: 1037-1051
- Quantitative ultrasound imaging of soft biological tissues: a primer for radiologists and medical physicists.Insights Imaging. 2021; 12: 127
- Fundamental correlation lengths of coherent speckle in medical ultrasonic images.IEEE Trans Ultrason Ferroelectr Freq Control. 1988; 35: 34-44
- Studies on the use of non-Rayleigh statistics for ultrasonic tissue characterization.Ultrasound Med Biol. 1996; 22: 873-882
- Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis.Sci Rep. 2016; 6: 33075
- Effects of fatty infiltration in human livers on the backscattered statistics of ultrasound imaging.Proc Inst Mech Eng Part H. 2015; 229: 419-428
- Breast cancer classification in automated breast ultrasound using multiview convolutional neural network with transfer learning.Ultrasound Med Biol. 2020; 46: 1119-1132
- Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network.J Digit Imaging. 2017; 30: 477-486
- Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.NPJ Digit Med. 2019; 2: 29
- Segmentation of kidney from ultrasound images based on texture and shape priors.IEEE Trans Med Imaging. 2005; 24: 45-57
- Ultrasound classification of breast masses using a comprehensive Nakagami imaging and machine learning framework.Ultrasonics. 2022; 124106744
- Quantitative ultrasound evaluation of tumor cell death response in locally advanced breast cancer patients receiving chemotherapy.Clin Cancer Res. 2013; 19: 2163-2174
- Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images.IEEE Trans Med Imaging. 2006; 25: 1483-1491
- Assessment of carotid artery plaque components with machine learning classification using homodyned-K parametric maps and elastograms.IEEE Trans Ultrason Ferroelectr Freq Control. 2019; 66: 493-504
- Clusters of ultrasound scattering parameters for the classification of steatotic and normal livers.Ultrasound Med Biol. 2021; 47: 3014-3027
- Quantitative ultrasound radiofrequency data analysis for the assessment of hepatic steatosis using the controlled attenuation parameter as a reference standard.Ultrasonography. 2020; 40: 136-146
- Quantitative ultrasound radiofrequency data analysis for the assessment of hepatic steatosis in nonalcoholic fatty liver disease using magnetic resonance imaging proton density fat fraction as the reference standard.Korean J Radiol. 2021; 22: 1077-1086
- Acoustic shadow detection: Study and statistics of B-mode and radiofrequency data.Ultrasound Med Biol. 2019; 45: 2248-2257
- ACR Appropriateness Criteria® Renal Transplant Dysfunction.J Am Coll Radiol. 2017; 14: S272-S281
- Kidney ultrasound for the nephrologist: a review.Kidney Med. 2022; 4100464
- ACR Appropriateness Criteria® on Renal Failure.Am J Med. 2014; 127 (e1): 1041-1048
- Trends in kidney transplantation over the past decade.Drugs. 2008; 68: 3-10
- Atlas of human anatomy.Elsevier Canada, Toronto2018
- Detection of structural changes in kidney parenchyma in patients with diffuse renal disease using quantitative ultrasound.Ultrason Imaging. 1990; 12: 123
- Modeling acoustic backscatter from kidney microstructure using an anisotropic correlation function.J Acoust Soc Am. 1995; 97: 649-655
- Sources of acoustic scattering in normal kidneys.Proc IEEE Int Symp Ultrason. 1990; 3: 1341-1344
- Ultrasonic measurement of glomerular diameters in normal adult humans.Ultrasound Med Biol. 1996; 22: 987-997
- Rayleigh-maximum-likelihood filtering for speckle reduction of ultrasound images.IEEE Trans Med Imaging. 2007; 26: 712-727
- Maximum likelihood segmentation of ultrasound images with Rayleigh distribution.IEEE Trans Ultrason Ferroelectr Freq Control. 2005; 52: 947-960
- Statistical properties of radio-frequency and envelope-detected signals with applications to medical ultrasound.J Opt Soc Am. 1987; 4: 910
- Statistics of speckle in ultrasound B-scans.IEEE Trans Son Ultrason. 1983; 30: 156-163
- Statistical properties of laser speckle patterns.in: Dainty JC Laser speckle and related phenomena. Springer, Berlin/Heidelberg1975: 9-75 (Topics in Applied Physics, vol. 9)
- Deviations from Rayleigh statistics in ultrasonic speckle.Ultrason Imaging. 1988; 10: 81-89
- Analysis of ultrasound image texture via generalized rician statistics.Opt Eng. 1986; 25256743
- Maximum-likelihood based estimation of the Nakagami m parameter.IEEE Commun Lett. 2001; 5: 101-103
- Quantitative ultrasonic detection of parenchymal structural change in diffuse renal disease.Invest Radiol. 1994; 29: 134-140
- Burr, Lomax, Pareto, and logistic distributions from ultrasound speckle.Ultrason Imaging. 2020; 42: 203-212
- nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.Nat Methods. 2021; 18: 203-211
Singla R, Ringstrom C, Hu G, Lessoway V, Reid J, Nguan C, et al. The open kidney ultrasound data set. arXiv 2206.06657. 2022.
- Performance comparison of three different estimators for the Nakagami m parameter using Monte Carlo simulation.IEEE Commun Lett. 2000; 4: 119-121
- Multiple significance tests: the Bonferroni method.BMJ. 1995; 310: 170
- A general statistical model for ultrasonic backscattering from tissues.IEEE Trans Ultrason Ferroelectr Freq Control. 2000; 47: 727-736
Article info
Publication history
Published online: February 24, 2023
Accepted:
January 19,
2023
Received in revised form:
December 21,
2022
Received:
September 13,
2022
Footnotes
Rohit Singla and Ricky Hu contributed equally to this work.
Identification
Copyright
© 2023 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
