DOI QR코드

DOI QR Code

INDUSTRIAL MATHEMATICS IN ULTRASOUND IMAGING

  • JANG, JAESEONG (DEPARTMENT OF COMPUTATIONAL SCIENCE AND ENGINEERING, YONSEI UNIVERSITY) ;
  • AHN, CHI YOUNG (DIVISION OF INTEGRATED MATHEMATICS, NATIONAL INSTITUTE FOR MATHEMATICAL SCIENCES)
  • Received : 2016.08.17
  • Accepted : 2016.08.29
  • Published : 2016.09.25

Abstract

Ultrasound imaging is a widely used tool for visualizing human body's internal organs and quantifying clinical parameters. Due to its advantages such as safety, non-invasiveness, portability, low cost and real-time 2D/3D imaging, diagnostic ultrasound industry has steadily grown. Since the technology advancements such as digital beam-forming, Doppler ultrasound, real-time 3D imaging and automated diagnosis techniques, there are still a lot of demands for image quality improvement, faster and accurate imaging, 3D color Doppler imaging and advanced functional imaging modes. In order to satisfy those demands, mathematics should be used properly and effectively in ultrasound imaging. Mathematics has been used commonly as mathematical modelling, numerical solutions and visualization, combined with science and engineering. In this article, we describe a brief history of ultrasound imaging, its basic principle, its applications in obstetrics/gynecology, cardiology and radiology, domestic-industrial products, contributions of mathematics and challenging issues in ultrasound imaging.

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

References

  1. Medical Equipment Market Size & Growth-Diagnostic Imaging[Ultrasound Systems] Market, Global 2006-2013, USD Constant Millions, Global Data, https://medical.globaldata.com.
  2. Medical Equipment Market Size & Growth-Diagnostic Imaging[Ultrasound Systems] Market, Global 2013-2020, USD Constant Millions, Global Data, https://medical.globaldata.com.
  3. Medical Equipment Market Size & Growth-Diagnostic Imaging[Ultrasound Systems] Company Shaare By Percentage, Global 2012, USD Constant Millions, Global Data, https://medical.globaldata.com.
  4. T. Szabo, Diagnostic Ultrasound Imaging: Inside Out, Academic Press, Boston University, 2004.
  5. D. H. Evans and W. N. McDicken, Doppler Ultrasound-Physics, Instrumentation and Signal Processing, 2nd ed., John Wiley and Sons, New York, 2000.
  6. J. Woo, A short history of the developments of ultrasound in obstetrics and gynecology. http://www.obultrasound.net/hydrophone.html, 1999.
  7. A.M. King, Development, advances and applications of diagnostic ultrasound in animals, The Veterinary Journal, 1713 (2006), 408-420.
  8. J. Curie and P. Curie, Development par pression de l'ectricite polaire dans les cristaux hemidres a faces inclinees, Compte Rendue de l' Acadamie Scientifique, 91 (1880), 294-295.
  9. K. T. Dussik, Uber die Moglichkeit, hochfrequente mechanische Schwingungen als diagnostisches Hilfsmittel zu verwerten, Zeitschrift fur die gesamte Neurologie und Psychiatrie, 174(1) (1942), 153-168. https://doi.org/10.1007/BF02877929
  10. G. D. Ludwig and F. W. Struthers, Considerations underlying the use of ultrasound to detect gall stones and foreign bodies in the tissues, United States Navy Medical Research Institute Report, 4 (1949), 1-27.
  11. I. Donald, J. Macvicar and T. G. Brown, Investigation of abdominal masses by pulsed ultrasound, Lancet, 1 (1958), 1189-1195.
  12. Y. Nimura, History of pulse and echo Doppler ultrasound in Japan, Cardiac Doppler Diagnosis, Martimus Nijoff Publishers, Boston, 1983.
  13. R. W. J. Felix, B. Sigel, R. J. Gibson, J. Williams and G. L. Popky, Pulsed Doppler ultrasound detection of flow disturbances in arteriosclerosis, J. Clin. Ultrasound, 4(4) (1976), 275-282. https://doi.org/10.1002/jcu.1870040409
  14. D. S. Evans and F. B. Cockett, Diagnosis of deep-vein thrombosis with an ultrasonic Doppler technique, Br. Med. J., 2 (1969), 802-804. https://doi.org/10.1136/bmj.2.5660.802
  15. P. N. T. Wells, A range-gated ultrasonic Doppler system, Medical and Biological Engineering, 7(6) (1969), 641-652. https://doi.org/10.1007/BF02551735
  16. G. R. Curry and D. N. White, Color coded ultrasonic differential velocity arterial scanner (Echoflow), Ultrasound Med. Biol., 4 (1978), 27-35. https://doi.org/10.1016/0301-5629(78)90004-2
  17. B. Sigel, A brief history of Doppler ultrasound in the diagnosis of peripheral vascular disease, Ultrasound Med. Biol., 24(2) (1998), 169-176. https://doi.org/10.1016/S0301-5629(97)00264-0
  18. J. F. Brinkley, S. K. Muramatsu, W. D. McCallum and R. L. Popp, In vitro evaluation of an ultrasonic threedimensional imaging and volume system, Ultrasonic Imaging, 4(2) (1982), 126-139. https://doi.org/10.1177/016173468200400203
  19. K. Baba, K. Satoh, S. Sakamoto, T. Okai and S. Ishii, Development of an ultrasonic system for threedimensional reconstruction of the fetus, Journal of Perinatal Medicine-Official Journal of the WAPM, 17(1) (1989), 19-24. https://doi.org/10.1515/jpme.1989.17.1.19
  20. J. Deng, J. E. Gardener, C. H. Rodeck and W. R. Lees, Fetal echocardiography in three and four dimensions, Ultrasound Med. Biol., 22(8) (1996), 979-986. https://doi.org/10.1016/S0301-5629(96)00119-6
  21. S. L. Kobal, S. S. Lee, R. Willner, F. E. A. Vargas, H. Luo, C. Watanabe, Y. Neuman, T. Miyamoto and R. J. Siegel, Hand-carried cardiac ultrasound enhances healthcare delivery in developing countries, Am. J. Cardiol., 94(4) (2004), 539-541. https://doi.org/10.1016/j.amjcard.2004.04.077
  22. H. Shmueli, Y. Burstein, I. Sagy, Z. H. Perry, R. Ilia, Y. Henkin, T. Shafat, N. Liel-Cohen and S. L. Kobal, Briefly Trained Medical Students Can Effectively Identify Rheumatic Mitral Valve Injury Using a Hand?Carried Ultrasound, Echocardiography, 30(6) (2013), 621-626. https://doi.org/10.1111/echo.12122
  23. J. S. Shanewise, A. T. Cheung, S. Aronson, W. J. Stewart, R. L. Weiss, J. B. Mark, R. M. Savage, P. Sears-Rogan, J. P. Mathew, M. A. Quinones, M. K. Cahalan MK and J. S. Savino, ASE/SCA guidelines for performing a comprehensive intraoperative multiplane transesophageal echocardiography examination: recommendations of the American Society of Echocardiography Council for Intraoperative Echocardiography and the Society of Cardiovascular Anesthesiologists Task Force for Certification in Perioperative Transesophageal Echocardiography, Anesth. Analg., 89(4) (1999), 870-884. https://doi.org/10.1213/00000539-199910000-00010
  24. H. F. Andersen, Transvaginal and transabdominal ultrasonography of the uterine cervix during pregnancy, Journal of Clinical Ultrasound, 19(2) (1991), 77-83. https://doi.org/10.1002/jcu.1870190204
  25. J. A. Jensen, D. Gandhi and W. D. O'Brien, Ultrasound fields in an attenuating medium, Proceedings of the IEEE 1993 Ultrasonics Symposium, 1993.
  26. A. Macovski, Ultrasonic imaging using arrays, Proceedings of the IEEE, 1979.
  27. J. A. Jensen, A Model for the Propagation and Scattering of Ultrasound in Tissue, J. Acoust. Soc. Am., 89 (1991), 182-191. https://doi.org/10.1121/1.400497
  28. J. A. Jensen and N. B. Svendsen, Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers, IEEE Trans. Ultrason., Ferroelec., Freq. Contr., 39 (1992), 262-267. https://doi.org/10.1109/58.139123
  29. J. A. Jensen, Field: A Program for Simulating Ultrasound Systems, the 10th Nordic-Baltic Conference on Biomedical Imaging Published in Medical & Biological Engineering & Computing, 34 (1996), 351-353.
  30. AIUM, AIUM Practice Parameter for the Performance of Obstetric Ultrasound Examinations, http://www.aium.org/resources/guidelines/obstetric.pdf, 2013.
  31. R. Rastogi, G. L. Meena, N. Rastogi and V. Rastogi, Interstitial ectopic pregnancy: A rare and difficult clinicosonographic diagnosis, J. Hum. Reprod. Sci., 1(2) (2008), 81-82. https://doi.org/10.4103/0974-1208.44116
  32. L. F. Goncalves, W. Lee, J. Espinoza and R. Romero,Three-and 4-Dimensional Ultrasound in Obstetric Practice Does It Help?, J. Ultrasound Med., 24(12) (2005), 1599-1624. https://doi.org/10.7863/jum.2005.24.12.1599
  33. N. J. Dudley, A systematic review of the ultrasound estimation of fetal weight, Ultrasound Obstet. Gynecol., 25(1) (2005), 80-89. https://doi.org/10.1002/uog.1751
  34. S. Feng, K. S. Zhou and W. Lee, Automatic fetal weight estimation using 3D ultrasonography, Proceedings of Medical Imaging 2012: Computer-Aided Diagnosis, California, USA 2012.
  35. I.-W. Lee, C.-H. Chang, Y.-C. Cheng, H.-C. Ko and F.-M. Chang, A review of three-dimensional ultrasound applications in fetal growth restriction, Journal of Medical Ultrasound, 20(3) (2012), 142-149. https://doi.org/10.1016/j.jmu.2012.07.002
  36. S. Yagel, S. M. Cohen, I. Shapiro and D. V. Valsky, 3D and 4D ultrasound in fetal cardiac scanning: a new look at the fetal heart, Ultrasound Obstet. Gynecol., 29(1) (2007), 81-95. https://doi.org/10.1002/uog.3912
  37. B. Messing, S. M. Cohen, D. V. Valsky, D. Rosenak, D. Hochner-Celnikier, S. Savchev and S. Yagel, Fetal cardiac ventricle volumetry in the second half of gestation assessed by 4D ultrasound using STIC combined with inversion mode, Ultrasound Obstet. Gynecol., 30(2) (2007), 142-151. https://doi.org/10.1002/uog.4036
  38. H. Laurichesse-Delmas, O. Grimaud, G. Moscoso and Y. Ville, Color Doppler study of the venous circulation in the fetal brain and hemodynamic study of the cerebral transverse sinus, Ultrasound in Obstetrics and Gynecology, 13(1) (1999), 34-42. https://doi.org/10.1046/j.1469-0705.1999.13010034.x
  39. R. K. Pooh and K. Pooh, K,. Transvaginal 3D and Doppler ultrasonography of the fetal brain, Seminars in perinatology 2001, 25(1) (2001), 38-43.
  40. W. Sepulveda, I. Rojas, J. A. Robert, C. Schnapp and J. L. Alcalde, Prenatal detection of velamentous insertion of the umbilical cord: a prospective color Doppler ultrasound study, Ultrasound in obstetrics & gynecology, 21(6) (2003), 564-569. https://doi.org/10.1002/uog.132
  41. D. E. Fitzgerald and J. E. Drumm, Non-invasive measurement of human fetal circulation using ultrasound: a new method, Br.Med. J., 2(6100) (1977), 1450-1451. https://doi.org/10.1136/bmj.2.6100.1450
  42. A. Dall'Asta, G. Paramasivam, C. C. Lees, Crystal Vue technique for imaging fetal spine and ribs, Ultrasound in Obstetrics & Gynecology, 47(3) (2016), 383-384. https://doi.org/10.1002/uog.15800
  43. T. Reynolds, The Echocardiographer's Pocket Reference, 4th Edition, Arizona Heart Institute, Phoenix, Arizona, USA, 2013.
  44. E. G. Grant, C. B. Benson, G. L. Moneta, A. V. Alexandrov et al., Carotid artery stenosis: gray-scale and Doppler US diagnosis-Society of Radiologists in Ultrasound Consensus Conference, Radiology, 229(2) (2003), 340-346. https://doi.org/10.1148/radiol.2292030516
  45. A. T. Stavros, D. Thickman, C. L. Rapp, M. A. Dennis, S. H. Parker and G. A. Sisney, Solid breast nodules:use of sonography to distinguish between benign and malignant lesions, Radiology, 196(1) (1995), 123-134. https://doi.org/10.1148/radiology.196.1.7784555
  46. M. L. Palmeri, M. H. Wang, J. J. Dahl, K. D. Frinkley and K. R. Nightingale, Quantifying hepatic shear modulus in vivo using acoustic radiation force, Ultrasound in Medicine & Biology, 34(4) (2008), 546-558. https://doi.org/10.1016/j.ultrasmedbio.2007.10.009
  47. M. Tanter, J. Bercoff, A. Athanasiou, T. Deffieux, J.-L. Gennisson, G. Montaldo, M. Muller, A. Tardivon and M. Fink, Quantitative assessment of breast lesion viscoelasticity: initial clinical results using supersonic shear imaging, Ultrasound in Medicine & Biology, 34(9) (2008), 1373-1386. https://doi.org/10.1016/j.ultrasmedbio.2008.02.002
  48. B. Lucas, T. Kanade, An iterative image restoration technique with an application to stereo vision, Proceedings of DARPA IU Workshop, (1981), 121-130.
  49. J. L. Barron, D. J. Fleet, S. S. Beauchemin, Performance of optical flow techniques, Int. J. Comput. Vision, 12(1) (1994), 43-77. https://doi.org/10.1007/BF01420984
  50. Q. Duan, E. D. Angelini, S. L. Herz, C. M. Ingrassia, K. D. Costa, J. W. Holmes, S. Homma and A. F. Laine, Region-Based Endocardium Tracking on Real-Time Three-Dimensional Ultrasound, Ultrasound Med. Biol., 35(2) (2009), 256-265. https://doi.org/10.1016/j.ultrasmedbio.2008.08.012
  51. K. Y. E. Leung, M. G. Danilouchkine, M. van Stralen, N. de Jong, A. F. van der Steen and J. G. Bosch, Left ventricular border tracking using cardiac motion models and optical flow, Ultrasound Med. Biol., 37(4) (2011), 605-616. https://doi.org/10.1016/j.ultrasmedbio.2011.01.010
  52. C. Y. Ahn, Robust Myocardial Motion Tracking for Echocardiography: Variational Framework Integrating Local-to-Global Deformation, Computational and Mathematical Methods in Medicine, 2013 (2013), 974027.
  53. G.-R. Hong, G. Pedrizzetti, G. Tonti, P. Li, Z. Wei, J. K. Kim, A. Baweja, S. Liu, N. Chung, H. Houle, J. Narula, and M. A. Vannan, Characterization and Quantification of Vortex Flow in the Human Left Ventricle by Contrast Echocardiography Using Vector Particle Image Velocimetry, JACC: Cardiovascular Imaging, 1(6) (2008), 705-717. https://doi.org/10.1016/j.jcmg.2008.06.008
  54. P. P. Sengupta, G. Pedrizzetti, P. J. Kilner, A. Kheradvar, T. Ebbers, G. Tonti, A. G. Fraser and J. Narula, Emerging Trends in CV Flow Visualization, JACC: Cardiovascular Imaging, 5(3) (2012), 305-316. https://doi.org/10.1016/j.jcmg.2012.01.003
  55. D. R. Munoz, M. Markl, J. L. Moya Mur, A. Barker, C. Fernandez-Golfin, P. Lancellotti and J. L. Z. Gomez, Intracardiac flow visualization: current status and future directions, European Heart Journal-Cardiovascular Imaging, 14 (2013), 1029-1038. https://doi.org/10.1093/ehjci/jet086
  56. H. Gao, P. Claus, M.-S. Amzulescu, I. Stankovic, J. D'Hooge and J.-U. Voigt, How to optimize intracardiac blood flow tracking by echocardiographic particle image velocimetry? Exploring the influence of data acquisition using computer-generated data sets, European Heart Journal Cardiovascular Imaging, 13(6) (2012), 490-499. https://doi.org/10.1093/ejechocard/jer285
  57. H. Gao, B. Heyde and J. D'Hooge, 3D Intra-cardiac flow estimation using speckle tracking: a feasibility study in synthetic ultrasound data, Proceedings of the IEEE International Ultrasonics Symposium (IUS'13), Prague, Czech Republic, July 2013.
  58. D. Garcia, J. C. Del Alamo, D. Tanne, R. Yotti, C. Cortina, E. Bertrand, J. C. Antoranz, E. Perez-David, R. Rieu, F. Fernandez-Aviles and others, Two-dimensional intraventricular flow mapping by digital processing conventional color-doppler echocardiography images, IEEE Transactions on Medical Imaging, 29(10) (2010), 1701-1713. https://doi.org/10.1109/TMI.2010.2049656
  59. S. Ohtsuki and M. Tanaka, The flow velocity distribution from the Doppler information on a plane in three-Dimensional flow, Journal of Visualization, 9(1) (2006), 69-82. https://doi.org/10.1007/BF03181570
  60. M. Arigovindan, M. Suhling, C. Jansen, P. Hunziker and M. Unser, Full motion and flow field recovery from echo Doppler data, IEEE Transactions on Medical Imaging, 26(1) (2007), 31-45. https://doi.org/10.1109/TMI.2006.884201
  61. A. Gomez, K. Pushparajah, J. M. Simpson, D. Giese, T. Schaeffter and G. Penney, A sensitivity analysis on 3D velocity reconstruction from multiple registered echo Doppler views, Medical Image Analysis, 17(6) (2013), 616-631. https://doi.org/10.1016/j.media.2013.04.002
  62. A. Gomez, A. de Vecchi, M. Jantsch, W. Shi, K. Pushparajah, J. M. Simpson, N. P. Smith, D. Rueckert, T. Schaeffter and G. P. Penney, 4D blood flow reconstruction over the entire ventricle from wall motion and blood velocity derived from ultrasound data, IEEE Trans. on Medical Imaging, 34(11) (2015), 2298-2308. https://doi.org/10.1109/TMI.2015.2428932
  63. F. Mehregan, F. Tournoux, S. Muth, Pibarot, Philippe and R. Rieu, G. Cloutier and D. Garcia, Doppler vortography:a color doppler approach to quantification of intraventricular blood flow vortices, Ultrasound in Medicine and Biology, 40(1) (2014), 210-221. https://doi.org/10.1016/j.ultrasmedbio.2013.09.013
  64. J. Jang, C. Y. Ahn, K. Jeon, J. Heo, D. Lee, C. Joo, J.-i. Choi and J. K. Seo, A reconstruction method of blood flow velocity in left ventricle using color flow ultrasound, Computational and Mathematical Methods in Medicine, 2015 (2015), 108274.
  65. G. K. Batchelor An Introduction to Fluid Dynamics, Cambridge Mathematical Library, Cambridge University Press, Cambridge, UK, 2000.
  66. A. Sarvazyan, T. J. Hall, M. W. Urban, M. Fatemi, S. R. Aglyamov and B. S. Garra, An overview of elastography -an emrging branch of medical imaging, Current Medical Imaging Reviews, 7 (2011), 255-282. https://doi.org/10.2174/157340511798038684
  67. J. Bercoff, M. Tanter and M. Fink, Supersonic shear imaging: a new technique for soft tissue elasticity mapping, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 51 (2004), 396-409. https://doi.org/10.1109/TUFFC.2004.1295425
  68. K. Nightingale, S. McAleavey and G. Trahey, Shear-wave generation using acoustic radiation force: in vivo and ex vivo results, Ultrasound in Medicine & Biology, 29(12) (2003), 1715-1723. https://doi.org/10.1016/j.ultrasmedbio.2003.08.008
  69. N. C. Rouze, M. H.Wang, M. L. Palmeri and K. R. Nightingale, Robust estimation of time-of-flight shear wave speed using a radon sum transformation, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 57(12) (2010), 2662-2670. https://doi.org/10.1109/TUFFC.2010.1740
  70. P. Song, A. Manduca, H. Zhao, M. W. Urban, J. F. Greenleaf and S. Chen, Fast shear compounding using robust 2-d shear wave speed calculation and multi-directional filtering, Ultrasound in Medicine & Biology, 40(6) (2014), 1343-1355. https://doi.org/10.1016/j.ultrasmedbio.2013.12.026
  71. L. Ji, J. R.McLaughlin, D. Renzi and J.-R. Yoon, Interior elastodynamics inverse problems: shear wave speed reconstruction in transient elastography, Inverse Problems, 19(6) (2003), S1. https://doi.org/10.1088/0266-5611/19/6/051
  72. J. McLaughlin and D. Renzi, Using level set based inversion of arrival times to recover shear wave speed in transient elastography and supersonic imaging, Inverse Problems, 22(2) (2006), 707. https://doi.org/10.1088/0266-5611/22/2/019
  73. J. McLaughlin and D. Renzi, Shear wave speed recovery in transient elastography and supersonic imaging using propagating fronts, Inverse Problems, 22(2) (2006), 681. https://doi.org/10.1088/0266-5611/22/2/018
  74. E. Y. Ko, S-Detect TM in Breast Ultrasound : Initial Experience, White Paper, WP201411-SMC-SDetect, Samsung Medison, 2014.
  75. K. Lee, J. Yoo and S. Kim, A novel semi-automatic method for biometric measurements of the fetal brain, White Paper, WP201412-5D-CNS, Samsung Medison, 2014.
  76. V. F. Duda and C. Kohler, An improved quantification tool for breast $ElastoScan^{TM}$ : $E-Breast^{TM}$, White Paper, WP201503-$E-Breast^{TM}$, Samsung Medison, 2015.
  77. D. J. Lim and M. H. Kim, Experiences of Intrinsic Compression Ultrasound Elastography (-$E-Thyroid^{TM}$) in Differentiating Benign From Malignant Thyroid Nodule, White Paper, WP201504-$E-Thyroid^{TM}$, Samsung Medison, 2015.
  78. W. K. Jeong, Liver stiffness measurement using S-Shearwave : initial experience, White Paper, CS201505-SShearwave, Samsung Medison, 2015.
  79. J. H. Yoon, H. J. Chang, J.W. Kim and N. Chung The value of multi-directional movement of carotid artery as a novel surrogate marker for acute ischemic stroke assessed by Arterial Analysis, White Paper, WP201506-ArterialAnalysis, Samsung Medison, 2015.
  80. A. Martegani and L. Aiani, Technological advancements improve the sensitivity of CEUS diagnostics, White Paper, WP201507-CEUS, Samsung Medison, 2015.
  81. Samsung Applies Deep Learning Technology to Diagnostic Ultrasound Imaging, Samsung Newsroom, https://news.samsung.com.
  82. R. A. Castellino, Computer aided detection (CAD): an overview, Cancer Imaging, 5(1) (2005), 17-19. https://doi.org/10.1102/1470-7330.2005.0018
  83. A. Jalalian, S. B. Mashohor, H. R.Mahmud, M. I. B. Saripan, A. R. B. Ramli and B. Karasfi, Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review, Clin. Imaging, 37(3) (2013), 420-426. https://doi.org/10.1016/j.clinimag.2012.09.024
  84. K. Doi, Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Comput. Med. Imaging Graph., 31(4) (2007), 198-211. https://doi.org/10.1016/j.compmedimag.2007.02.002
  85. B. van Ginneken, C. M. Schaefer-Prokop and M. Prokop, Computer-aided diagnosis: how to move from the laboratory to the clinic, Radiology, 261(3) (2011), 719-732. https://doi.org/10.1148/radiol.11091710
  86. B. Lei, E. L. Tan, S. Chen, L. Zhuo, S. Li, D. Ni and T. Wang, Automatic recognition of fetal facial standard plane in ultrasound image via fisher vector, PloS one, 10(5) (2015), e0121838. https://doi.org/10.1371/journal.pone.0121838
  87. S. Joo, Y. S. Yang, W. K. Moon and H. C. Kim, Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features, IEEE Trans. Med. Imaging, 23(10) (2004), 1292-1300. https://doi.org/10.1109/TMI.2004.834617
  88. S. H. Kim, J.M. Lee, K. G. Kim, J. H. Kim, J. Y. Lee, J. K. Han and B. I. Choi, Computer-aided image analysis of focal hepatic lesions in ultrasonography: preliminary results, Abdom. Imaging, 34(2) (2009), 183-191. https://doi.org/10.1007/s00261-008-9383-9
  89. R. Llobet, J. C. Perez-Cortes, A. H. Toselli and A. Juan, Computer-aided detection of prostate cancer International Journal of Medical Informatics, 76(7) (2007), 547-556. https://doi.org/10.1016/j.ijmedinf.2006.03.001
  90. D. R. Chen, R. F. Chang,W. J. Kuo, M. C. Chen and Y. L. Huang, Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks, Ultrasound Med. Biol., 28(10) (2002), 1301-1310. https://doi.org/10.1016/S0301-5629(02)00620-8
  91. Y. L. Huang and D. R. Chen, Watershed segmentation for breast tumor in 2-D sonography, Ultrasound Med. Biol., 30(5) (2004), 625-632. https://doi.org/10.1016/j.ultrasmedbio.2003.12.001
  92. D. L. Sandulescu, D. Dumitrescu, I. Rogoveanu and A. Saftoiu, Hybrid ultrasound imaging techniques (fusion imaging), World J. Gastroenterol., 17(1) (2011), 49-52. https://doi.org/10.3748/wjg.v17.i1.49
  93. W. Wein, S. Brunke, A. Khamene, M. R. Callstrom and N. Navab, Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention, Med. Image Anal., 12(5) (2008), 577-585. https://doi.org/10.1016/j.media.2008.06.006
  94. T. Lange, N. Papenberg, S. Heldmann, J. Modersitzki, B. Fischer, H. Lamecker and P. M. Schlag, 3D ultrasound-CT registration of the liver using combined landmark-intensity information, Int. J. Comput. Assist. Radiol. Surg., 4(1) (2009), 79-88. https://doi.org/10.1007/s11548-008-0270-1
  95. M. W. Lee, Fusion imaging of real-time ultrasonography with CT or MRI for hepatic intervention, Ultrasonography, 33(4) (2014), 227-239. https://doi.org/10.14366/usg.14021
  96. C. Ewertsen, A. Saftoiu, L. G. Gruionu, S. Karstrup and M. B. Nielsen, Real-time image fusion involving diagnostic ultrasound, Am. J. Roentgenol., 200(3) (2013), W249-W255. https://doi.org/10.2214/AJR.12.8904
  97. B. C. Porter, D. J. Rubens, J. G. Strang, J. Smith, S. Totterman and K. J. Parker, Three-dimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers, IEEE Trans. Med. Imaging, 20(4) (2001) 354-359. https://doi.org/10.1109/42.921484
  98. A. Roche, S. Pennec, G. Malandain and N. Ayache, Rigid registration of 3-D ultrasound with MR images:a new approach combining intensity and gradient information, IEEE Trans. Med. Imaging, 20(10) (2001), 1038-1049. https://doi.org/10.1109/42.959301
  99. J. Jang, C. Y. Ahn, J.-I. Choi and J. K. Seo, Inverse Problem for Color Doppler Ultrasound-Assisted Intracardiac Blood Flow Imaging, Computational and Mathematical Methods in Medicine, 2016 (2016), 6371078.
  100. M. H. Wang, M. L. Palmeri, V. M. Rotemberg, N. C. Rouze and K. R. Nightingale, Improving the robustness of time-of-flight based shear wave speed reconstruction methods using ransac in human liver in vivo, Ultrasound in Medicine & Biology, 36(5) (2010), 802-813. https://doi.org/10.1016/j.ultrasmedbio.2010.02.007