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International Conference on Image Analysis and Recognition

ICIAR 2019: Image Analysis and Recognition pp 407–417Cite as

Predicting Structural Properties of Cortical Bone by Combining Ultrasonic Attenuation and an Artificial Neural Network (ANN): 2-D FDTD Study

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11662))

Abstract

The goal of this paper is to predict the micro-architectural parameters of cortical bone such as pore diameter (ϕ) and porosity (ν) from ultrasound attenuation measurements using an artificial neural network (ANN). Slices from a 3-D CT scan of human femur are obtained. The micro-architectural parameters of porosity such as average pore size and porosity are calculated using image processing. When ultrasound waves propagate in porous structures, attenuation is observed due to scattering. Two-dimensional finite-difference time-domain simulations are carried out to obtain frequency dependent attenuation in those 2D structures. An artificial neural network (ANN) is then trained with the input feature vector as the frequency dependent attenuation and output as pore diameter (ϕ) and porosity (ν). The ANN is composed of one input layer, 3 hidden layers and one output layer, all of which are fully connected. 340 attenuation data sets were acquired and trained over 2000 epochs with a batch size of 32. Data was split into train, validation and test. It was observed that the ANN predicted the micro-architectural parameters of the cortical bone with high accuracies and low losses with a minimum R2 (goodness of fit) value of 0.95. ANN approaches could potentially help inform the solution of inverse-problems to retrieve bone porosity from ultrasound measurements. Ultimately, those inverse-problems could be used for the non-invasive diagnosis and monitoring of osteoporosis.

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Acknowledgement

This work was partially supported by National Institutes of Health grant no. R03EB022743. The authors’ also acknowledge Dr Quentin Grimal, Sorbonne University for providing the high resolution CT scans and Dr Maciej A. Mazurowski, Duke Radiology Dept., for consulting with us during the development of the ANN model.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, NC State University, Raleigh, NC, USA

    Kaustav Mohanty, Omid Yousefian, Yasamin Karbalaeisadegh, Micah Ulrich & Marie Muller

Authors
  1. Kaustav Mohanty
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  2. Omid Yousefian
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  3. Yasamin Karbalaeisadegh
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  4. Micah Ulrich
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  5. Marie Muller
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Correspondence to Marie Muller .

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Editors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. University of Waterloo, Waterloo, ON, Canada

    Alfred Yu

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Mohanty, K., Yousefian, O., Karbalaeisadegh, Y., Ulrich, M., Muller, M. (2019). Predicting Structural Properties of Cortical Bone by Combining Ultrasonic Attenuation and an Artificial Neural Network (ANN): 2-D FDTD Study. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_37

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  • DOI: https://doi.org/10.1007/978-3-030-27202-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27201-2

  • Online ISBN: 978-3-030-27202-9

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