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Current Applications and Future Promises of Machine Learning in Diffusion MRI

  • Daniele RaviEmail author
  • Nooshin Ghavami
  • Daniel C. Alexander
  • Andrada Ianus
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) explores the random motion of diffusing water molecules in biological tissue and can provide information on the tissue structure at a microscopic scale. DW-MRI in used in many applications both in the brain and other parts of the body such as the breast and prostate, and novelcomputational methods are at the core of advancements in DW-MRI, both in terms of research and its clinical translation. This article reviews the ways in whichmachine learning anddeep learning is currently applied in DW-MRI. We will also discuss the more traditional methods used for processing diffusion MRI and the potential of deep learning in augmenting these existing methods in the future.

Keywords

Diffusion-weighted MRI Machine learning Deep learning 

Notes

Acknowledgements

DR and DCA are supported by a project that has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement number 666992. EPSRC grants EP/M020533/1 and EP/N018702/1 support AI and DCAs work on this topic. UCL EPSRC Centre for Doctor Training in Medical Imaging (EP/L016478/1) funds NG.

References

  1. 1.
    Stejskal, E.O., Tanner, T.E.: Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42, 288–292 (1965)CrossRefGoogle Scholar
  2. 2.
    Johansen-Berg, H., Behrens, T.E.: Diffusion MRI: From Quantitative Measurement to in vivo Neuroanatomy. Academic Press (2013)Google Scholar
  3. 3.
    Padhani, A.R., Liu, G., Mu-Koh, D., Chenevert, T.L., Thoeny, H.C., Takahara, T., Dzik-Jurasz, A., Ross, B.D., Van Cauteren, M., Collins, D., Hammoud, D.A., Rustin, G.J.S., Taouli, B., Choyke, P.L.: Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11, 102125 (2009)CrossRefGoogle Scholar
  4. 4.
    Schaefer, P.W., Grant, P.E., Gonzalez, R.G.: Diffusion-weighted MR imaging of the brain. Radiology 217, 331–345 (2000)CrossRefGoogle Scholar
  5. 5.
    Basser, P.J., Matiello, J., Le Bihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994)CrossRefGoogle Scholar
  6. 6.
    Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S.: Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316329 (2007)CrossRefGoogle Scholar
  7. 7.
    Wheeler-Kingshott, C.A.M., Hickman, S.J., Parker, G.J.M., Ciccarelli, O., Symms, M.R., Miller, D.H., Barker, G.J.: Investigating cervical spinal cord structure using axial diffusion tensor imaging. NeuroImage 16, 93102 (2002)CrossRefGoogle Scholar
  8. 8.
    Mukherjee, P., Berman, J.I., Chung, S.W., Hess, C.P., Henry, R.G.: Investigating cervical spinal cord structure using axial diffusion tensor imaging. Am. J. Neuroradiol. 29, 632–641 (2008)CrossRefGoogle Scholar
  9. 9.
    Yoshida, S., Oishi, K., Faria, A.V., Mori, S.: Diffusion tensor imaging of normal brain development. Pediatr. Radiol. 43, 1527 (2013)CrossRefGoogle Scholar
  10. 10.
    de Groot, M., Cremers, L.G.M., Ikram, M.A., Hofman, A., Krestin, G.P., van der Lugt, A., Niessen, W.J., Vernooij, M.W.: White matter degeneration with aging: longitudinal diffusion MR imaging analysis. Radiology 279, 532541 (2015)Google Scholar
  11. 11.
    Lerner, A., Mogensen, M.A., Kim, P.E., Shiroishi, M.S., Hwang, D.H., Law, M.: Clinical applications of diffusion tensor imaging. World Neurosurg. 82, 96–109 (2014)CrossRefGoogle Scholar
  12. 12.
    Grussu, F., Gandini Wheeler-Kingshott, C.A.M.: The Diffusion of Water (DTI). CRC Press (2018)Google Scholar
  13. 13.
    Tuch, D.S., Reese, T.G., Wiegell, M.R., Wedeen, V.J.: Diffusion mri of complex neural architecture. Neurotechnique 40, 885–895 (2003)Google Scholar
  14. 14.
    Alexander, D.C., Dyrby, T.B., Nilsson, M., Zhang, H.: Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. e3841 (2017)Google Scholar
  15. 15.
    Novikov, D.S., Fieremans, E., Jespersen, S.N., Kiselev, V.G.: Quantifying brain microstructure with diffusion MRI: theory and parameter estimation (2016). arXiv:1612.02059
  16. 16.
    Ghosh, A., Ianus, A., Alexander, D.C.: Advanced Diffusion Models. CRC Press (2018)Google Scholar
  17. 17.
    Alexander, D.C.: A general framework for experiment design in diffusion mri and its application in measuring direct tissue-microstructure features. Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med. 60(2), 439–448 (2008)CrossRefGoogle Scholar
  18. 18.
    Yang, Z., Chen, G., Shen, D., Yap, P.-T.: Robust fusion of diffusion mri data for template construction. Sci. Rep. 7(1), 12950 (2017)CrossRefGoogle Scholar
  19. 19.
    Reisert, M., Kellner, E., Dhital, B., Hennig, J., Kiselev, V.G.: Disentangling micro from mesostructure by diffusion MRI: a Bayesian approach. Neuroimage 147, 964–975 (2017)CrossRefGoogle Scholar
  20. 20.
    Nedjati-Gilani, G.L., Schneider, T., Hall, M.G., Cawley, N., Hill, I., Ciccarelli, O., Drobnjak, I., Wheeler-Kingshott, C.A.G., Alexander, D.C.: Machine learning based compartment models with permeability for white matter microstructure imaging. NeuroImage 150, 119–135 (2017)CrossRefGoogle Scholar
  21. 21.
    Alexander, D.C., Zikic, D., Zhang, J., Zhang, H., Criminisi, A.: Image quality transfer via random forest regression: applications in diffusion MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 225–232. Springer (2014)Google Scholar
  22. 22.
    Descoteaux, M., Deriche, R., Knosche, T.R., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)CrossRefGoogle Scholar
  23. 23.
    Ganepola, T., Nagy, Z., Alexander, D., Sereno, M.: An unsupervised group average cortical parcellation using HARDI data. In: An Unsupervised Group Average Cortical Parcellation using HARDI Data, vol. 2015, p. 221. Organization for Human Brain Mapping (2015)Google Scholar
  24. 24.
    Guo, Y., Cai, Y.-Q., Cai, Z.-L., Gao, Y.-G., An, N.-Y., Ma, L., Mahankali, S., Gao, J.-H.: Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J. Magn. Reson. Imaging 16(2), 172–178 (2002)CrossRefGoogle Scholar
  25. 25.
    Thoeny, H.C., Ross, B.D.: Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J. Magn. Reson. Imaging 32(1), 2–16 (2010)CrossRefGoogle Scholar
  26. 26.
    Yablonskiy, D.A., Sukstanskii, A.L.: Theoretical models of the diffusion weighted MR signal. NMR Biomed. 23(7), 661–681 (2010)CrossRefGoogle Scholar
  27. 27.
    Lenglet, C., Campbell, J.S., Descoteaux, M., Haro, G., Savadjiev, P., Wassermann, D., Anwander, A., Deriche, R., Pike, G.B., Sapiro, G., et al.: Mathematical methods for diffusion MRI processing. Neuroimage 45(1), S111–S122 (2009)CrossRefGoogle Scholar
  28. 28.
    Ravı, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.-Z.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4–21 (2017)CrossRefGoogle Scholar
  29. 29.
    Provenzale, J.M., Liang, L., DeLong, D., White, L.E.: Diffusion tensor imaging assessment of brain white matter maturation during the first postnatal year. Am. J. Roentgenol. 189(2), 476–486 (2007)CrossRefGoogle Scholar
  30. 30.
    Dzik-Jurasz, A., Domenig, C., George, M., Wolber, J., Padhani, A., Brown, G., Doran, S.: Diffusion MRI for prediction of response of rectal cancer to chemoradiation. The Lancet 360(9329), 307–308 (2002)CrossRefGoogle Scholar
  31. 31.
    Billiet, T., Vandenbulcke, M., Mädler, B., Peeters, R., Dhollander, T., Zhang, H., Deprez, S., Van den Bergh, B.R., Sunaert, S., Emsell, L.: Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol. Aging 36(6), 2107–2121 (2015)CrossRefGoogle Scholar
  32. 32.
    Nedjati-Gilani, G.L., Schneider, T., Hall, M.G., Wheeler-Kingshott, C.A., Alexander, D.C.: Machine learning based compartment models with permeability for white matter microstructure imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 257–264. Springer (2014)Google Scholar
  33. 33.
    Fick, R., Sepasian, N., Pizzolato, M., Ianus, A., Deriche, R.: Assessing the feasibility of estimating axon diameter using diffusion models and machine learning. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2017)Google Scholar
  34. 34.
    Mesaros, S., Rocca, M., Kacar, K., Kostic, J., Copetti, M., Stosic-Opincal, T., Preziosa, P., Sala, S., Riccitelli, G., Horsfield, M., et al.: Diffusion tensor MRI tractography and cognitive impairment in multiple sclerosis. Neurology, pp. WNL–0b013e31824d5859 (2012)Google Scholar
  35. 35.
    McWhinney, S.R., Tremblay, A., Chevalier, T.M., Lim, V.K., Newman, A.J.: Using cforest to analyze diffusion tensor imaging data: a study of white matter integrity in healthy aging. Brain Connect. 6(10), 747–758 (2016)CrossRefGoogle Scholar
  36. 36.
    Neher, P.F., Cote, M.-A., Houde, J.-C., Descoteaux, M., Maier-Hein, K.H.: Fiber tractography using machine learning. Neuroimage 158, 417–429 (2017)CrossRefGoogle Scholar
  37. 37.
    Mitra, J., Bourgeat, P., Fripp, J., Ghose, S., Rose, S., Salvado, O., Connelly, A., Campbell, B., Palmer, S., Sharma, G., et al.: Lesion segmentation from multimodal MRI using random forest following ischemic stroke. NeuroImage 98, 324–335 (2014)CrossRefGoogle Scholar
  38. 38.
    Alexander, D.C., Zikic, D., Ghosh, A., Tanno, R., Wottschel, V., Zhang, J., Kaden, E., Dyrby, T.B., Sotiropoulos, S.N., Zhang, H., et al.: Image quality transfer and applications in diffusion MRI. Neuroimage 152, 283–298 (2017)CrossRefGoogle Scholar
  39. 39.
    Tanno, R., Ghosh, A., Grussu, F., Kaden, E., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 265–273. Springer (2016)Google Scholar
  40. 40.
    Raftery, A.E.: Approximate bayes factors and accounting for model uncertainty in generalised linear models. Biometrika 83(2), 251–266 (1996)MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Park, I., Amarchinta, H.K., Grandhi, R.V.: A Bayesian approach for quantification of model uncertainty. Reliab. Eng. Syst. Saf. 95(7), 777–785 (2010)CrossRefGoogle Scholar
  42. 42.
    Friman, O., Farneback, G., Westin, C.-F.: A Bayesian approach for stochastic white matter tractography. IEEE Trans. Med. Imaging 25(8), 965–978 (2006)CrossRefGoogle Scholar
  43. 43.
    Emblem, K.E., Due-Tonnessen, P., Hald, J.K., Bjornerud, A., Pinho, M.C., Scheie, D., Schad, L.R., Meling, T.R., Zoellner, F.G.: Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional mri. J. Magn. Reson. Imaging 40(1), 47–54 (2014)CrossRefGoogle Scholar
  44. 44.
    Schnell, S., Saur, D., Kreher, B., Hennig, J., Burkhardt, H., Kiselev, V.G.: Fully automated classification of hardi in vivo data using a support vector machine. NeuroImage 46(3), 642–651 (2009)CrossRefGoogle Scholar
  45. 45.
    Feis, D.-L., Brodersen, K.H., von Cramon, D.Y., Luders, E., Tittgemeyer, M.: Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data. Neuroimage 70, 250–257 (2013)CrossRefGoogle Scholar
  46. 46.
    Artan, Y., Haider, M.A., Langer, D.L., van der Kwast, T.H., Evans, A.J., Yang, Y., Wernick, M.N., Trachtenberg, J., Yetik, I.S.: Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans. Image Process. 19(9), 2444–2455 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Ozer, S., Haider, M.A., Langer, D.L., van der Kwast, T.H., Evans, A.J., Wernick, M.N., Trachtenberg, J., Yetik, I.S.: Prostate cancer localization with multispectral mri based on relevance vector machines. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, ISBI’09, pp. 73–76. IEEE (2009)Google Scholar
  48. 48.
    Bagher-Ebadian, H., Jafari-Khouzani, K., Mitsias, P.D., Lu, M., Soltanian-Zadeh, H., Chopp, M., Ewing, J.R.: Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke. PloS one 6(8), e22626 (2011)CrossRefGoogle Scholar
  49. 49.
    Bertleff, M., Domsch, S., Weingärtner, S., Zapp, J., O’Brien, K., Barth, M., Schad, L.R.: Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3t. NMR Biomed. 30(12), e3833 (2017)CrossRefGoogle Scholar
  50. 50.
    Koppers, S., Haarburger, C., Merhof, D.: Diffusion MRI signal augmentation: from single shell to multi shell with deep learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 61–70. Springer (2016)Google Scholar
  51. 51.
    Golkov, V., Dosovitskiy, A., Sämann, P., Sperl, J.I., Sprenger, T., Czisch, M., Menzel, M.I., Gómez, P.A., Haase, A., Brox, T., et al.: q-space deep learning for twelve-fold shorter and model-free diffusion MRI scans. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 37–44. Springer (2015)Google Scholar
  52. 52.
    Hill, I.D., Palombo, M., Santin, M.D., Branzoli, F., Philippe, A.-C., Wassermann, D., Aigrot, M.-S., Stankoff, B., Zhang, H., Lehericy, S., et al.: Deep neural network based framework for in-vivo axonal permeability estimationGoogle Scholar
  53. 53.
    van der Burgh, H.K., Schmidt, R., Westeneng, H.-J., de Reus, M.A., van den Berg, L.H., van den Heuvel, M.P.: Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. NeuroImage Clin. 13, 361–369 (2017)Google Scholar
  54. 54.
    Roberts, T.A., Hipwell, B., Agliardi, G., d’Esposito, A., Taylor, V., Lythgoe, M.F., Walker-Samuel, S.: Deep learning diffusion fingerprinting to detect brain tumour response to chemotherapy, p. 193730 (2017) (bioRxiv)Google Scholar
  55. 55.
    Koppers, S., Merhof, D.: Direct estimation of fiber orientations using deep learning in diffusion imaging. In International Workshop on Machine Learning in Medical Imaging, pp. 53–60. Springer (2016)Google Scholar
  56. 56.
    Trebeschi, S., van Griethuysen, J.J., Lambregts, D.M., Lahaye, M.J., Parmer, C., Bakers, F.C., Peters, N.H., Beets-Tan, R.G., Aerts, H.J.: Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep. 7(1), 5301 (2017)CrossRefGoogle Scholar
  57. 57.
    Wasserthal, J., Neher, P., Maier-Hein, K.H.: Tractseg-fast and accurate white matter tract segmentation. NeuroImage 183, 239–253 (2018)CrossRefGoogle Scholar
  58. 58.
    Clark, T., Zhang, J., Baig, S., Wong, A., Haider, M.A., Khalvati, F.: Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J. Med. Imaging 4(4), 041307 (2017)CrossRefGoogle Scholar
  59. 59.
    Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D.: 3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 212–220. Springer (2016)Google Scholar
  60. 60.
    Lv, J., Huang, W., Zhang, J., Wang, X.: Performance of U-net based pyramidal lucas-kanade registration on free-breathing multi-b-value diffusion MRI of the kidney. Br. J. Radiol. 91(1086), 20170813 (2018)CrossRefGoogle Scholar
  61. 61.
    Tanno, R., Worrall, D.E., Ghosh, A., Kaden, E., Sotiropoulos, S.N., Criminisi, A., Alexander, D.C.: Bayesian image quality transfer with CNNs: Exploring uncertainty in dMRI super-resolution. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 611–619. Springer (2017)Google Scholar
  62. 62.
    Blumberg, S.B., Tanno, R., Kokkinos, I., Alexander, D.C.: Deeper image quality transfer: training low-memory neural networks for 3d images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 118–125. Springer (2018)Google Scholar
  63. 63.
    Abraham, B., Nair, M.S.: Computer-aided classification of prostate cancer grade groups from MRI images using texture features and stacked sparse autoencoder. Comput. Med. Imaging Graph. 69, 60–68 (2018)CrossRefGoogle Scholar
  64. 64.
    Benou, A., Veksler, R., Friedman, A., Raviv, T.R.: Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Med. Image Anal. 42, 145–159 (2017)CrossRefGoogle Scholar
  65. 65.
    Shehata, M., Khalifa, F., Hollis, E., Soliman, A., Hosseini-Asl, E., El-Ghar, M.A., El-Baz, M., Dwyer, A.C., El-Baz, A., Keynton, R.: A new non-invasive approach for early classification of renal rejection types using diffusion-weighted MRI. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 136–140. IEEE (2016)Google Scholar
  66. 66.
    Shehata, M., Khalifa, F., Soliman, A., Ghazal, M., Taher, F., El-Ghar, M.A., Dwyer, A., Gimel’farb, G., Keynton, R., El-Baz, A.: Computer-aided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI. IEEE Trans. Biomed. Eng. (2018)Google Scholar
  67. 67.
    Vasilev, A., Golkov, V., Lipp, I., Sgarlata, E., Tomassini, V., Jones, D.K., Cremers, D.: q-space novelty detection with variational autoencoders (2018). arXiv:1806.02997
  68. 68.
    Albay, E., Demir, U., Unal, G.: Diffusion MRI spatial super-resolution using generative adversarial networks. In: International Workshop on Predictive Intelligence in Medicine, pp. 155–163. Springer (2018)Google Scholar
  69. 69.
    Kohl, S., Bonekamp, D., Schlemmer, H.-P., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, J.-P., Maier-Hein, K.: Adversarial networks for the detection of aggressive prostate cancer (2017). arXiv:1702.08014
  70. 70.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)Google Scholar
  71. 71.
    Ravì, D., Szczotka, A.B., Shakir, D.I., Pereira, S.P., Vercauteren, T.: Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy (2018)Google Scholar

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Authors and Affiliations

  1. 1.University College LondonLondonUK

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