Top > IJAT > Estimation of Appropriate Breast Compression for Robotized Mam ...

single-au.php

IJAT Vol.11 No.3 pp. 490-500
doi: 10.20965/ijat.2017.p0490
(2017)

Paper:

Views over last 60 days: 756

Estimation of Appropriate Breast Compression for Robotized Mammographic Imaging

Alex Jahya, Matteo Zoppi†, and Rezia Molfino

PMAR Robotics, University of Genoa
Via Opera Pia 15A, 16145 Genoa, Italy

†Corresponding author

Received:
October 2, 2016
Accepted:
January 30, 2017
Online released:
April 28, 2017
Published:
May 5, 2017
Creative Commons License
Keywords:
mammography, automated breast compression, breast modeling
Abstract
The paper discusses a doppler ultrasound system for breast stiffening estimation during breast compression in mammographic screening procedures developed using automatic (robotized) mammography units. These units can be considered robots as they are automated, instruct the patient and supervise that the procedure develops correctly. The paper addresses the problem, for the robotized mammographer, to determine automatically the amount of compression of the breast to ensure proper imaging while limiting the pain for the patient to the minimum inevitable. This is one of the key issues to solve to make robotic mammographers. The physical principle used is sonoelastography in a doppler arrangement. Two algorithms have been developed able to detect vibrational displacement of breast tissue by processing the echo signals. From the displacement and phase of the vibrating tissue, the value of the elastic modulus of the breast tissue can be derived and hence its strain value in the region of interest.
Cite this article as:
A. Jahya, M. Zoppi, and R. Molfino, “Estimation of Appropriate Breast Compression for Robotized Mammographic Imaging,” Int. J. Automation Technol., Vol.11 No.3, pp. 490-500, 2017.
Data files:
References
  1. [1] A. Poulos and G. Llewellyn, “Mammography discomfort: A Holistic perspective Derived from Women’s Experiences,” Radiology, Vol.11, pp. 17-25, 2005.
  2. [2] A. Poulos and G. Llewellyn, “Mammography Pain and Discomfort: A Cognitive Behavioral Perspective,” The J. of Pain, Vol.56, No.3, pp. 247-260, 1994.
  3. [3] A. Poulos and D. McLean, “The Application of Breast Compression in Mammography: A New Perspective,” Int. J. of Radiography, Vol.10, No.1016, pp. 131-137, 2004.
  4. [4] C. Swann, D. Kopans, K. McCarthy, G. White, and D. Hall, “Mammographic Density and Physical Assessment of the Breast,” American Roentgen Ray Society, Vol.148, pp. 525-526, 1986.
  5. [5] K. Chida, Y. Komatsu, M. Sai, A. Nakagami, T. Yamada, T. Yamashita, I. Morri, T. Ishibashi, and S. Maruoka, “Reduced Compression Mammography to Reduce Breast Pain,” Clinical Imaging, Vol.33, pp. 7-10, 2009.
  6. [6] F. P.-C. Huang, “A Pledge from Automation to Intellimation,” Int. J. Automation Technol., Vol.3, No.2, pp. 204-205, 2009.
  7. [7] K. Tanaka, S. Mu, and S. Nakashima, “Meal-Assistance Robot Using Ultrasonic Motor with Eye Interface,” Int. J. Automation Technol., Vol.8, No.2, pp. 186-192, 2014.
  8. [8] M. S. Sabel, “Essentials of Breast Surgery,” Mosby Inc., an affiliate of Elsevier Inc., 2009.
  9. [9] K. Parker, S. Huang, R. Musulin, and R. Lerner, “Tissue Response to Mechanical Vibrations for Sonoelasticity Imaging,” Ultrasound in Med. and Biol., Vol.16, No.3, pp. 241-246, 1990.
  10. [10] A. Sarvazyan, “Shear Acoustic Properties of Soft Biological Tissues in Medical Diagnostics,” J. Acoustic Society Am. Proc., Vol.125, No.93, p.2329, 1993.
  11. [11] Y. Fung, “Biomechanics: Mechanical Properties of Living Tissue,” Springer-Verlag, 1993.
  12. [12] T. Krouskop, T. Wheeler, F. Kallel, B. Garra, and T. Hall, “Elastic Moduli of Breast and Prostate Tissues under Compression,” Ultrasonic Imaging, Vol.20, pp. 260-274, 1998.
  13. [13] P. Wellman, R. Howe, E. Dalton, and K. Kern, “Breast Tissue Stiffness in Compression is Correlated to Histological Diagnosis,” Harvard University Technical Report, pp. 1-15, 1999.
  14. [14] R. Muthupillai, D. Lomas, P. Rossman et al., “Magnetic Resonance Elastography by Direct VIsualizatino of Propagating Acoustic Strain Waves,” Science, Vol.269, pp. 1854-1857, 1995.
  15. [15] M. Zhang and A. Mak, “Estimating the effective Young’s Modulus of Soft Tissues from Indentation Tests - Nonlinear Finite Element Analysis of Effects of Friction and Large Deformation,” Medical Engineering Physics, Vol.19, No.6, pp. 512-517, 1997.
  16. [16] W. Hayes, L. Keer, G. Herrmann, and L. Mockros, “A Mathematical Analysis for Indentation Tests of Articular Cartilage,” J Biomechanics, Vol.5, pp. 541-551, 1972.
  17. [17] M. Sridhar and M. Insana, “Ultrasonic measurements of breast viscoelasticity,” Medical Physics, pp. 1-28, 2007.
  18. [18] A. Samani, J. Bishop, C. Luginbuhl, and D. Plewes, “Measuring the Elastic Modulus of ex vivo small tissue samples,” Physics in Medicine and Biology, Vol.48, pp. 2183-2198, 2003.
  19. [19] C. Tanner et al., “A method for the comparison of biomechanical breast models,” IEEE Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, pp. 11-18, 2001.
  20. [20] S. Azar, N. Metaxas, and D. M. Schnall, “A Finite Model of the Breast for Predicting Mechanical Deformations during Biopsy Procedure,” IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, South Carolina, USA, pp. 38-45, 2000.
  21. [21] J. Schnabel, C. Tanner, A. C. Smith, A. Degendard, and M. Leach, “Validation of Nonrigid Image Registration Using Finite-Element Methods: Application to Breast MR Images,” IEEE Trans. on Medical Imaging, Vol.22, No.2, pp. 238-247, 2003.
  22. [22] J. Li, Y. Cui, R. English, and J. A. Noble, “Ultrasound Estimation of Breast Tissue Biomechanical Properties using a Similarity-Based Non-Linear Optimization Approach,” J. Strain Analysis, Vol.44, pp. 363-374, 2009.
  23. [23] N. Koizumi, K. Oota, D. Lee, H. Tsukihara, A. Nomiya, K. Yoshinaka, T. Azuma, A. Sugita, Y. Homma, Y. Matsumoto, and M. Mitsuiishi, “System identification method for non-invasive ultrasound theragnostic system incorporating mechanical oscillation part,” Int. J. Automation Technol., Vol.8, No.1, pp. 110-119, 2014.
  24. [24] N. Koizumi, H. Tsukihara, S. Takamoto, H. Hashizume, and M. Mitsuiishi, “Robot vision technology for technologizing and digitalization of medical diagnostic and therapeutic skills,” Int. J. Automation Technol., Vol.3, No.5, pp. 541-550, 2014.
  25. [25] Y. Yamakoshi, J. Sato, and T. Sato, “Ultrasonic Imaging of Internal Vibration of Soft Tissue under Forced Vibration,” IEEE Trans. on Ultrasonic, Ferroelectrics and Frequency Control, Vol.37, No.2, pp. 45-53, 1990.
  26. [26] M. Malinauskas, T. Krouskop, and P. Barry, “NonInvasive Measurement of the Stiffness of Tissue in the Above-Knee Amputation Limb,” J. of Rehabilitation Research, Vol.26, No.3, pp. 45-52, 1989.
  27. [27] R. Dickinson and C. Hill., “Measurement of Soft Tissue Motion using Correlation between A-scans,” Ultrasound in Medicine and Biology., Vol.8, pp. 263-271, 1982.
  28. [28] T. Krouskop, D. Dougherty, and S. Levinson, “A Pulsed Doppler Ultrasonic System for making NonInvasive Measurements of the Mechanical Properties of Soft Tissue,” F. G. Evans (ed.), Studies on the Anatomy and Function of Bone and Joints, Vol.24, pp. 1-8, 1987.
  29. [29] H. Oestreicher, “Field and Impedance of an Oscillating Sphere in Viscoelastic Medium with an Application to Biophysics,” J. of Accoustic of American Society, Vol.23, pp. 707-714, 1951.
  30. [30] P. E. S. Wayne, R. Hedrick, and D. L. Hykes, “Ultrasound Physics and Instrumentation,” 3rd ed., St. Louis: Mosby Year Book Inc., 1995.
  31. [31] K. Shung, M. Smith, and M. Benjamin, “Principal of Medical Imaging,” San Diego Academic Press, 1992.
  32. [32] S. Holland, S. Ophanoudakis, and C. Jaffe, “Frequency Dependent Attenuation Effects in Pulsed Doppler Ultrasound: Experimental Results,” IEEE Trans. Biomedical Engineering (BME), Vol.31, No.9, pp. 626-631, 1984.
  33. [33] D. H. Evans and W. N. McDicken, “Doppler Ultrasound: Physics, Instrumentation and Signal Processing,” 2nd ed., New York, John Willey and Sons, 2000.
  34. [34] J. Ophir and T. Lin, “A Calibration Free Method for Measurement of Sound Speed in Biological Tissue Samples,” IEEE Trans. on Ultrasonic, Ferroelectrics and Frequency Control, Vol.35, No.5, pp. 573-577, 1988.
  35. [35] C. Sumi, K. Nakayama, and M. Kubota, “Fine Elasticity Imaging Utilizing the Refined Iterative Rf-Echo Phase matching Algorithm,” IEEE Trans. on Ultrasonic, Ferroelectrics and Frequency Control, Vol.35, No.5, pp. 573-577, 1988.
  36. [36] K. Avinash and A. Dines, “Signal Processing of Broadband Pulsed Ultrasound: Measurement of Attenuation of Soft Biological Tissues,” IEEE Trans. on Biomedical Eng. (BME), Vol.25, No.4, pp. 321-344, 2007.
  37. [37] H. Otaki and Y. Yamahoshi, “Tissue Displacement Measurement using Ultrasonic Wave Adaptive Digital Detection Method,” Japanese J. of Applied Physics, Vol.34, pp. 2835-2839, 1995.
  38. [38] A. B. Carlson., “Communication Systems: an Introduction to Signals and Noise in Electrical Communication,” 3rd ed., New York: McGraw-Hill Book Company, 1986.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024