Introduction of Diffusion MRI and Cuckoo Search Algorithm

  • Mohammad ShehabEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 877)


The brain is the most complex organ in the human body because it consists of about 100 billion neurons and one million billion (\(10^{15}\)) interconnections (Azevedo et al. 2009). This organ is the control for the sensorimotor such as walking and breathing, cognitive functions such as talking, reasoning, memory and more complex functions such as emotions and feelings. The brain is also a subject of many diseases that need surgery, which could result in either deterioration of the cited functions or even in permanent disability. Medical imaging, especially Magnetic Resonance Imaging (MRI), helps mapping the anatomical and functional aspects of the brain, considered as the substratum of the different functions.


  1. Abualigah, L. M., Sawaie, A. M., Khader, A. T., Rashaideh, H., Al-Betar, M. A., & Shehab. M. (2017b). \(\beta \)-hill climbing technique for the text document clustering. New Trends in Information Technology, 60.Google Scholar
  2. Alaya, I. B., Jribi, M., Ghorbel, F., Sappey-Marinier, D., & Kraiem, T (2017). Fast and accurate estimation of the hardi signal in diffusion mri using a nearest-neighbor interpolation approach. IRBM, 38(3), 156–166.Google Scholar
  3. Assemlal, H.-E., Tschumperlé, D., & Brun, L. (2009). Efficient and robust computation of pdf features from diffusion mr signal. Medical Image Analysis, 13(5), 715–729.CrossRefGoogle Scholar
  4. Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Lent, R., & Herculano-Houzel, S et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532–541.Google Scholar
  5. Basser, P. J., Mattiello, J., & LeBihan, D. (1994) Mr diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259.Google Scholar
  6. Bilgic, B., Setsompop, K., Cohen-Adad, J., Yendiki, A., Wald, L. L., & Adalsteinsson, E. (2012). Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries. Magnetic Resonance in Medicine, 68(6), 1747–1754.Google Scholar
  7. Çetingül, H.E., Plank, G., Trayanova, N. A., & Vidal, R. (2011). Estimation of local orientations in fibrous structures with applications to the purkinje system. IEEE Transactions on Biomedical Engineering, 58(6), 1762–1772.Google Scholar
  8. Craig, F., & Robynne, B. (2001). How your brain works.
  9. Cuevas, E., & Reyna-Orta, A. (2014). A cuckoo search algorithm for multimodal optimization. The Scientific World Journal.Google Scholar
  10. Daducci, A., Dal Palù, A., Lemkaddem, A., & Thiran, J.-P. (2015). Commit: convex optimization modeling for microstructure informed tractography. IEEE Transactions on Medical Imaging, 34(1), 246–257.CrossRefGoogle Scholar
  11. Deb, K. (2012). Optimization for engineering design: Algorithms and examples. PHI Learning Pvt. Ltd.Google Scholar
  12. Denis, L. B., & Breton, E. (1985). Imagerie de diffusion in-vivo par résonance magnétique nucléaire. Comptes-Rendus de l’Académie des Sciences, 93(5), 27–34.Google Scholar
  13. Fan, Q., Witzel, T., Nummenmaa, A., Van Dijk, K. R. A., Van Horn, J. D., Drews, M. K., Somerville, L. H., Sheridan, M. A., Santillana, R. M., Snyder, J. et al. (2016). Mgh–usc human connectome project datasets with ultra-high b-value diffusion mri. Neuroimage, 124, 1108–1114.CrossRefGoogle Scholar
  14. Feng, Y., Jia, K., & He, Y. (2014). An improved hybrid encoding cuckoo search algorithm for 0–1 knapsack problems. Computational Intelligence and Neuroscience, 2014, 1.Google Scholar
  15. Gass, A., Ay, H., Szabo, K., & Koroshetz, W. J. (2004). Diffusion-weighted mri for the small stuff: the details of acute cerebral ischaemia. The Lancet Neurology, 3(1), 39–45.Google Scholar
  16. Gong, G., He, Y., Concha, L., Lebel, C., Gross, D. W., Evans, A. C., & Beaulieu, C. (2009). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19(3), 524–536.Google Scholar
  17. Hagmann, P., Jonasson, L., Maeder, P., Thiran, J.-P., Van Wedeen, J., & Meuli, R. (2006). Understanding diffusion mr imaging techniques: From scalar diffusion-weighted imaging to diffusion tensor imaging and beyond 1. Radiographics, 26(suppl\(\_\)1), S205–S223.Google Scholar
  18. Iturria-Medina, Y., Canales-Rodriguez, E. J., Melie-Garcia, L., Valdes-Hernandez, P. A., Martinez-Montes, E., Alemán-Gómez, Y., et al. (2007). Characterizing brain anatomical connections using diffusion weighted mri and graph theory. Neuroimage, 36(3), 645–660.CrossRefGoogle Scholar
  19. James, K., & Russell, E. (1995). Particle swarm optimization. In Proceedings of 1995 IEEE International Conference on Neural Networks, pp. 1942–1948.Google Scholar
  20. Jones, D. K. & Pierpaoli, C. (2005). Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magnetic Resonance in Medicine, 53(5), 1143–1149.Google Scholar
  21. Kamalakannan, C., Suresh, P., Dash, S. S., & Panigrahi, B. K. (2014). Power Electronics and Renewable Energy Systems: Proceedings of ICPERES 2014, vol. 326. Springer.Google Scholar
  22. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.Google Scholar
  23. Khedr, M. E., Zaghloul, M. S., & El-Desouky, M. I. (2015). Wireless adhoc multi access networks optimization using ospf routing protocol based on cisco devices. International Journal of Computer Networks & Communications, 7(2), 59.Google Scholar
  24. Kuhnt, D., Bauer, M. H. A., Egger, J., Richter, M., Kapur, T., Sommer, J., Merhof, D., & Nimsky, C. (2013a). Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing: initial experience. Neurosurgery, 72(0 1), 165.Google Scholar
  25. Kuhnt, D., Bauer, M. H. A., Sommer, J., Merhof, D., & Nimsky, C. (2013b). Optic radiation fiber tractography in glioma patients based on high angular resolution diffusion imaging with compressed sensing compared with diffusion tensor imaging-initial experience. PLoS One, 8(7), e70973.Google Scholar
  26. Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986). Mr imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology, 161(2), 401–407.CrossRefGoogle Scholar
  27. Li, X., & Yin, M. (2016). A particle swarm inspired cuckoo search algorithm for real parameter optimization. Soft Computing, 20(4), 1389–1413.CrossRefGoogle Scholar
  28. Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: a review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18.Google Scholar
  29. Parker, G. J. M. (2014). Analysis of mr diffusion weighted images. The British Journal of Radiology.Google Scholar
  30. Pontabry, J., Rousseau, F., Oubel, E., Studholme, C., Koob, M., & Dietemann, J.-L. (2013). Probabilistic tractography using q-ball imaging and particle filtering: application to adult and in-utero fetal brain studies. Medical Image Analysis, 17(3), 297–310.Google Scholar
  31. Pujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., et al. (2015). The dti challenge: Toward standardized evaluation of diffusion tensor imaging tractography for neurosurgery. Journal of Neuroimaging, 25(6), 875–882.CrossRefGoogle Scholar
  32. Qiu, L., Hsu, W.-J., Huang, S.-Y., & Wang, H. (2002). Scheduling and routing algorithms for agvs: A survey. International Journal of Production Research, 40(3), 745–760.CrossRefGoogle Scholar
  33. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11(8), 5508–5518.CrossRefGoogle Scholar
  34. Rao, R. V., & Patel, V. (2013). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling, 37(3), 1147–1162.Google Scholar
  35. Romano, A., Dandrea, G., Minniti, G., Mastronardi, L., Ferrante, L., Fantozzi, L. M., et al. (2009). Pre-surgical planning and mr-tractography utility in brain tumour resection. European Radiology, 19(12), 2798.CrossRefGoogle Scholar
  36. Shehab, M., & Khader, A. T. (2018). Modified cuckoo search algorithm using a new selection scheme for unconstrained optimization problems, 14, 1.Google Scholar
  37. Shehab, M., Daoud, M. Sh., AlMimi, H. M., Abualigah, L. M., & Khader, A. T. (2019a). Hybridizing cuckoo search algorithm for extracting the odf maxima in spherical harmonic representation. International Journal of Bio-Inspired Computation, (in press).Google Scholar
  38. Shehab, M., Khader, A. T., & Al-Betar, M. A. (2016). New selection schemes for particle swarm optimization. IEEJ Transactions on Electronics, Information and Systems, 136(12), 1706–1711. Scholar
  39. Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017a). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing.Google Scholar
  40. Shehab, M., Khader, A. T., & Alia, M. A. (2019b). Enhancing cuckoo search algorithm by using reinforcement learning for constrained engineering optimization problems. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 812–816. IEEE.Google Scholar
  41. Shehab, M., Khader, A. T., & Laouchedi, M. (2017c). Modified cuckoo search algorithm for solving global optimization problems. In International Conference of Reliable Information and Communication Technology, pp. 561–570. Springer.Google Scholar
  42. Shehab, M., Khader, A. T., & Laouchedi, M. (2018a). A hybrid method based on cuckoo search algorithm for global optimization problems. Journal of ICT, 17(3), 469–491.Google Scholar
  43. Shehab, M., Khader, A. T., Al-Betar, M. A., & Abualigah, L. M. (2017b). Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In Information Technology (ICIT), 2017 8th International Conference on, pp. 36–43. IEEE.Google Scholar
  44. Shehab, M., Khader, A. T., Laouchedi, M., & Alomari, O. A. (2018b). Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. The Journal of Supercomputing, 1–28.Google Scholar
  45. Siddique, N., & Adeli, H. (2015). Nature inspired computing: An overview and some future directions. Cognitive Computation, 7(6), 706–714.CrossRefGoogle Scholar
  46. Sperl, J. I., Sprenger, T., Tan, Ek. T., Menzel, M. I., Hardy, C. J., & Marinelli, L. (2017) Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution. Magnetic Resonance in Medicine.Google Scholar
  47. Tariq, M., Schneider, T., Alexander, D. C., Claudia, A. G. (2016). Wheeler-Kingshott, and Hui Zhang. Bingham–noddi: Mapping anisotropic orientation dispersion of neurites using diffusion mri. NeuroImage, 133, 207–223.CrossRefGoogle Scholar
  48. Taylor, D. G., & Bushell, M. C. (1985). The spatial mapping of translational diffusion coefficients by the nmr imaging technique. Physics in Medicine and Biology, 30(4), 345.CrossRefGoogle Scholar
  49. Thomas, C., Frank, Q. Y., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014). Anatomical accuracy of brain connections derived from diffusion mri tractography is inherently limited. Proceedings of the National Academy of Sciences, 111(46), 16574–16579.Google Scholar
  50. Thottakara, P., Lazar, M., Johnson, S. C., & Alexander, A. L. (2006). Application of brodmann’s area templates for roi selection in white matter tractography studies. Neuroimage, 29(3), 868–878.Google Scholar
  51. Tomána, H., Tornaib, R., & Zicharc, M. (2007). Complex fiber visualization. Annales Mathematicae et Informaticae (pp. 103–109)., volume 34 Institute of Mathematics and Computer Science: Eszterházy Károly College.Google Scholar
  52. Topgaard, D. (2017). Multidimensional diffusion mri. Journal of Magnetic Resonance, 275, 98–113.CrossRefGoogle Scholar
  53. Tuch, D. S. (2004b). Q-ball imaging. Magnetic resonance in medicine, 52(6), 1358–1372.CrossRefGoogle Scholar
  54. Tuch, D. S., Weisskoff, R. M., Belliveau, J. W., & Wedeen, V. J. (1999). High angular resolution diffusion imaging of the human brain. In Proceedings of the 7th Annual Meeting of ISMRM, Philadelphia, volume 321.Google Scholar
  55. Vorburger, R. S. (2012). Probabilistic techniques in diffusion weighted imaging and fiber tractography.Google Scholar
  56. Wedeen, V. J., Davis, T. L., Weisskoff, R. M., Tootell, R., Rosen, B. R., & Belliveau, J. W. (1995). White matter connectivity explored by mri. (Vol. 69).Google Scholar
  57. Wedeen, V. J., Reese, T. G., Tuch, D. S., Weigel, M. R., Dou, J. G., Weiskoff, R. M., & Chessler, D. (2000). Mapping fiber orientation spectra in cerebral white matter with fourier-transform diffusion mri. In Proceedings of the 8th Annual Meeting of ISMRM, Denver, p. 82.Google Scholar
  58. Wedeen, J. Van, Wang, R. P., Schmahmann, J. D., Benner, T., Tseng, W. Y. I., Dai, G., Pandya, D. N., Hagmann, P., D’Arceuil, P., & de Crespigny, A. J. (2008). Diffusion spectrum magnetic resonance imaging (dsi) tractography of crossing fibers. Neuroimage, 41(4), 1267–1277.Google Scholar
  59. Weiss, C., Tursunova, I., Neuschmelting, V., Lockau, H., Nettekoven, C., Oros-Peusquens, A.-M. (2015). Gabriele Stoffels, Anne K Rehme, Andrea Maria Faymonville, N Jon Shah, et al. Improved ntms-and dti-derived cst tractography through anatomical roi seeding on anterior pontine level compared to internal capsule. NeuroImage: Clinical, 7, 424–437.CrossRefGoogle Scholar
  60. Yan, L. (2015). Operative Techniques in liver resection. Springer.Google Scholar
  61. Yang, X.-S. & Nature-Inspired Metaheuristic Algorithms. (2008). Luniver press. UK: Beckington.Google Scholar
  62. Yang, X.-S. (2014). Cuckoo search and firefly algorithm: Overview and analysis. In Cuckoo Search and Firefly Algorithm, pp. 1–26. Springer.Google Scholar
  63. Yang, X.-S. (2015). Nature-inspired algorithms: Success and challenges. In Engineering and Applied Sciences Optimization, pp. 129–143. Springer.Google Scholar
  64. Yang, X.-S., & Deb, S. (2009). Cuckoo search via lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, pp. 210–214. IEEE.Google Scholar
  65. Young, R. J., Tan, Ek. T., Peck, K. K., Jenabi, M., Karimi, S., Brennan, N., Rubel, J., Lyo, J., Shi, W., & Zhang, Z. et al. (2017). Comparison of compressed sensing diffusion spectrum imaging and diffusion tensor imaging in patients with intracranial masses. Magnetic Resonance Imaging, 36, 24–31, 2017.Google Scholar
  66. Zhao, X. C., Huang, P. Y., Liu, T. T., & Li, X. M. (2012). A hybrid clonal selection algorithm for quality of service-aware web service selection problem. Int J Innov Comput Inf Control, 8(12), 8527–8544.Google Scholar
  67. Zucchelli, M., Garyfallidis, E., Paquette, M., Merlet, S., Menegaz, G., & Descoteaux, M. (2014). Comparison between discrete and continuous propagator indices from cartesian q-space dsi sampling. In ISMRM: International Society for Magnetic Resonance in Medicine, p. 4294.Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science\Artificial Intelligence DepartmentAqaba University of TechnologyAqabaJordan

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