References

• 1. 1)
     • 14. Walid, I.E., Fan, Z., Prashin, U., et al: ‘White matter tractography for neurosurgical planning: a topography-based review of the current state of the art’, Neuroimage Clin., 2017, 15, pp. 659–672.
  2. 2)
     • 58. Jones, D.K., Horsfield, M.A., Simmons, A.: ‘Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging’, Magn. Reson. Med., 1999, 42, pp. 515–525.
  3. 3)
     • 29. Basser, P.J., Pajevic, S.: ‘Statistical artifacts in diffusion tensor MRI caused by background noise’, Magn. Reson. Med., 2000, 44, pp. 41–50.
  4. 4)
     • 55. Moseley, M.E.: ‘Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system’, Radiology, 1990, 176, pp. 439–445.
  5. 5)
     • 68. Pajevic, S., Pierpaoli, C.: ‘Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain’, Magn. Reson. Med., 1999, 42, pp. 526–540.
  6. 6)
     • 34. Mori, S.: ‘Introduction to diffusion tensor imaging’ (Elsevier, Amsterdam, 2007).
  7. 7)
     • 27. Gudbjartsson, H., Patz, S.: ‘The Rician distribution of noisy MRI data’, Magn. Reson. Med., 1995, 34, pp. 910–914.
  8. 8)
     • 49. Hao, Z., Dong, Z., Hua, Z., et al: ‘Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: a review’, Med. Phys., 2017, 44, pp. 1168–1185.
  9. 9)
     • 4. Jones, D.K., Leemans, A.: ‘Diffusion tensor imaging’, Magn. Reson. Neuroimaging, 2011, 711, pp. 127–144.
  10. 10)
      • 35. Akram, A., Michael, U.: ‘Wavelets in medicine and biology’ (Elsevier, Amsterdam, 2007).
  11. 11)
      • 53. Martin-Fernandez, F., MuÃśoz-Moreno, E., Cammoun, L., et al: ‘Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data’, Med. Image Anal., 2009, 13, pp. 19–35.
  12. 12)
      • 40. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, pp. 629–639.
  13. 13)
      • 73. Anderson, W.: ‘Measurement of fiber orientation distributions using high angular resolution diffusion imaging’, Magn. Reson. Med., 2005, 54, pp. 1194–1206.
  14. 14)
      • 56. Basser, P., Mattiello, J., LeBihan, D.: ‘Estimation of the effective self-diffusion tensor from NMR spin echo’, J. Magn. Reson. B, 1994, 103, pp. 247–254.
  15. 15)
      • 60. Frank, L.: ‘Characterization of anisotropy in high angular resolution diffusion-weighted MRI’, Magn. Reson. Med., 2002, 47, pp. 1083–1099.
  16. 16)
      • 62. Mori, S., Zijl, P.V.: ‘Fiber tracking: principles and strategies as a technical review’, NMR Biomed., 2002, 15, pp. 468–480.
  17. 17)
      • 10. Mattiello, J., Basser, P.J., Le Bihan, D.: ‘Analytical expressions for the b matrix in NMR diffusion imaging and spectroscopy’, J. Magn. Reson., 1994, 108, pp. 131–141.
  18. 18)
      • 66. Zalesky, A.: ‘DT-MRI fiber tracking: a shortest paths approach’, IEEE Trans. Med. Imaging, 2008, 27, pp. 1458–1471.
  19. 19)
      • 51. Wirestam, R., Bibic, A., Lätt, J., et al: ‘Denoising of complex MRI data by wavelet-domain filtering: application to high-b-value diffusion-weighted imaging’, Magn. Reson. Med., 2006, 56, pp. 1114–1120.
  20. 20)
      • 64. Friman, O., Farneback, G., Westin, C.F.: ‘A Bayesian approach for white matter tractography’, IEEE Trans. Med. Imaging, 2006, 25, pp. 965–978.
  21. 21)
      • 2. Basser, P.J., Jones, D.K.: ‘Diffusion-tensor MRI: theory, experimental design and data analysis – a technical review’, NMR Biomed., 2002, 15, pp. 456–467.
  22. 22)
      • 19. Basser, P.J., Pierpaoli, C.: ‘A simplified method to measure the diffusion tensor from seven MR images’, Magn. Reson. Med., 1998, 39, pp. 928–934.
  23. 23)
      • 67. Descoteaux, M., Deriche, R., Knosche, T.R., et al: ‘Deterministic and probabilistic tractography based on complex fibre orientation distributions’, IEEE Trans. Med. Imaging, 2009, 28, pp. 269–286.
  24. 24)
      • 37. McGraw, T., Vemuri, B.C., Chen, Y., et al: ‘DT-MRI denoising and neuronal fiber tracking’, Magn. Reson. Med., 2004, 8, pp. 95–111.
  25. 25)
      • 47. Descoteaux, M., Wiest-Daesslé, N., Prima, S., et al: ‘Impact of Rician adapted non-local means filtering on HARDI’, Proc. MICCAI, 2008, 11, pp. 122–130.
  26. 26)
      • 43. Deepak, M., Santanu, C., Mukul, S., et al: ‘Edge probability and pixel relativity-based speckle reducing anisotropic diffusion’, IEEE Trans. Image Process., 2018, 27, pp. 649–664.
  27. 27)
      • 21. Ming-Chung, C., Fong, K.E., Mori, S.: ‘Effects of b-value and echo time on magnetic resonance diffusion tensor imaging-derived parameters at 1.5 T: a voxel-wise study’, J. Med. Biol. Eng., 2013, 33, pp. 45–50.
  28. 28)
      • 22. Kim, H.J., Choi, C.G., Lee, D.H., et al: ‘High-b-value diffusion-weighted MR imaging of hyperacute ischemic stroke at 1.5 T’, Am. J. Neuroradiol., 2005, 26, pp. 208–215.
  29. 29)
      • 12. Pierpaoli, C., Jezzard, P., Basser, P.J., et al: ‘Diffusion tensor MR imaging of the human brain’, Radiology, 1996, 201, pp. 637–648.
  30. 30)
      • 6. Breton, M.A., DeKosky, S.T., James, R.C., et al: ‘Diffusion tensor imaging (DTI) findings in adult civilian, military, and sport-related mild traumatic brain injury (mTBI): a systematic critical review’, Brain Imaging Behav., 2018, 12, pp. 585–612.
  31. 31)
      • 38. Chen, B., Hsu, E.: ‘Noise removal in magnetic resonance diffusion tensor imaging’, Magn. Reson. Med., 2005, 54, pp. 393–407.
  32. 32)
      • 28. Bastin, M.E., Armitage, P.A., Marshall, I.: ‘A theoretical study of the effect of experimental noise on the measurement of anisotropy in diffusion imaging’, Magn. Reson. Med., 1998, 16, pp. 773–785.
  33. 33)
      • 1. Le Bihan, D., Jean-François, M., Poupon, C., et al: ‘Diffusion tensor imaging: concepts and applications’, Magn. Reson. Imaging, 2001, 13, pp. 534–546.
  34. 34)
      • 17. Evangelia, T., Randall, E., Nader, P.: ‘Using probabilistic tractography to target the subcallosal cingulate cortex in patients with treatment resistant depression’, Psychiatry Res., Neuroimaging, 2017, 261, pp. 72–74.
  35. 35)
      • 18. Mattiello, J., Basser, P.J., Le Bihan, D.: ‘The b matrix in diffusion tensor echo-planar imaging’, Magn. Reson. Med., 1997, 37, pp. 292–300.
  36. 36)
      • 23. Assaf, Y., Ben-Bashat, D., Chapman, J., et al: ‘High b-value q-space analyzed diffusion-weighted MRI: application to multiple sclerosis’, Magn. Reson. Med., 2002, 47, pp. 115–126.
  37. 37)
      • 33. Jones, D.K., Basser, P.J.: ‘Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data’, Magn. Reson. Med., 2004, 52, pp. 979–993.
  38. 38)
      • 48. Manjón, J.V., Coupé, P., Martí-Bonmatí, L., et al: ‘Adaptive non-local means denoising of MR images with spatially varying noise levels’, Magn. Reson. Imaging, 2010, 31, pp. 192–203.
  39. 39)
      • 59. Frank, L.: ‘Anisotropy in high angular resolution diffusion-weighted MRI’, Magn. Reson. Med., 2001, 45, pp. 935–939.
  40. 40)
      • 24. Jones, D.K.: ‘The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: an Monte Carlo study’, Magn. Reson. Med., 2004, 51, pp. 807–815.
  41. 41)
      • 57. Beaulieu, C., Allen, P.S.: ‘Determinants of anisotropic water diffusion in nerves’, Magn. Reson. Med., 1994, 31, pp. 394–400.
  42. 42)
      • 46. Coupé, P., Yger, P., Prima, S., et al: ‘An optimized blockwise nonlocal means denoising filter for 3D magnetic resonance images’, IEEE Trans. Med. Imaging, 2008, 27, pp. 425–441.
  43. 43)
      • 31. Dietrich, O., Heiland, S., Sartor, K.: ‘Noise correction for the exact determination of apparent diffusion coefficients at low SNR’, Magn. Reson. Med., 2001, 45, pp. 448–453.
  44. 44)
      • 25. Zhang, N., Zhen-Sheng, D., Fang, W., et al: ‘The effect of different number of diffusion gradients on SNR of diffusion tensor-derived measurement maps’, J. Biomed. Sci. Eng., 2009, 2, pp. 96–101.
  45. 45)
      • 7. Mohammed, K., Emanuele, S., Ben, A.H.: ‘A multicomponent approach to nonrigid registration of diffusion tensor images’, Appl. Intell., 2017, 46, pp. 241–253.
  46. 46)
      • 41. Ding, Z., Gore, J.C., Anderson, A.W.: ‘Reduction of noise in diffusion tensor images using anisotropic smoothing’, Magn. Reson. Med., 2005, 53, pp. 485–490.
  47. 47)
      • 36. Parker, G.M.J., Schnabel, J.A., Symms, M.R., et al: ‘Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging’, Magn. Reson. Med., 2000, 11, pp. 702–710.
  48. 48)
      • 26. Ni, H., Kavcic, V., Zhu, T., et al: ‘Effects of number of diffusion gradients on derived diffusion tensor imaging indices in human brain’, Am. J. Neuroradiol., 2006, 27, pp. 1776–1781.
  49. 49)
      • 42. Xu, Q., Anderson, A., Gore, J., et al: ‘Efficient anisotropic filtering of diffusion tensor images’, Magn. Reson. Imaging, 2010, 28, pp. 200–211.
  50. 50)
      • 8. Xianhua, Z., Shanshan, H., Weisheng, L.: ‘Color perception of diffusion tensor images using hierarchical manifold learning’, Pattern Recognit., 2017, 63, pp. 583–592.
  51. 51)
      • 32. Anderson, A.W.: ‘Theoretical analysis of the effects of noise on diffusion tensor imaging’, Magn. Reson. Med., 2001, 46, pp. 1174–1188.
  52. 52)
      • 54. Stejskal, E., Tanner, J.: ‘Spin diffusion measurements: spin echoes in the presence of time-dependent field gradient’, J. Chem. Phys., 1965, 42, pp. 282–292.
  53. 53)
      • 63. Parker, G.J.M., Wheeler-Kingshott, C.A.M., Barker, G.J.: ‘Estimating distributed anatomical connectivity using fast marching methods and diffusion tensor imaging’, IEEE Trans. Med. Imaging, 2002, 21, pp. 505–512.
  54. 54)
      • 72. Dong, Q., Welsh, R.C., Chenevert, T.L., et al: ‘Clinical applications of diffusion tensor imaging’, Magn. Reson. Imaging, 2004, 19, pp. 6–18.
  55. 55)
      • 11. Basser, P.J., Pierpaoli, C.: ‘Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI’, J. Magn. Reson., 1996, 111, pp. 209–219.
  56. 56)
      • 45. Wiest-Daesslé, N., Prima, S., Coupé, P., et al: ‘Nonlocal means variants for denoising of diffusion-weighted and diffusion tensor MRI’, Proc. MICCAI, 2007, 10, pp. 344–351.
  57. 57)
      • 30. Skare, S., Li, T., Nordell, B., et al: ‘Noise considerations in the determination of diffusion tensor anisotropy’, Magn. Reson. Med., 2000, 18, pp. 659–669.
  58. 58)
      • 16. Po-Shan, W., Chien-Li, Y., Chia-Feng, L., et al: ‘The involvement of supratentorial white matter in multiple system atrophy: a diffusion tensor imaging tractography study’, Acta Neurol. Belg., 2017, 117, pp. 213–220.
  59. 59)
      • 13. Matteo, B., Michiel, C., Krikor, D., et al: ‘Improved tractography using asymmetric fibre orientation distributions’, Neuroimage, 2017, 158, pp. 205–218.
  60. 60)
      • 9. Lazar, M., Weinstein, D.M., Tsuruda, J.S., et al: ‘White matter tractography using diffusion tensor deflection’, Hum. Brain Mapp., 2003, 18, pp. 306–321.
  61. 61)
      • 50. Nowak, R.D.: ‘Wavelet-based Rician noise removal for magnetic resonance imaging’, IEEE Trans. Image Process., 1999, 8, pp. 1408–1419.
  62. 62)
      • 52. Coupé, P., Hellier, P., Prima, S., et al: ‘3D wavelet subbands mixing for image denoising’, Int. J. Biomed. Imaging, 2008, p. 11.
  63. 63)
      • 70. Horsfield, M.A., Jones, D.K.: ‘Applications of diffusion-weighed and diffusion tensor MRI to white matter diseases’, NMR Biomed., 2002, 15, pp. 570–577.
  64. 64)
      • 15. Miguel, G., Claudio, R., Josselin, H., et al: ‘Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography’, Neuroimage, 2017, 147, pp. 703–725.
  65. 65)
      • 61. Basser, P., Pajevic, S., Pierpaoli, C., et al: ‘In vivo fiber tractography using DT-MRI data’, Magn. Reson. Med., 2000, 44, pp. 625–632.
  66. 66)
      • 20. Papadakis, N.G., Xing, D., Huang, C.L., et al: ‘A comparative study of acquisition schemes for diffusion tensor imaging using MRI’, J. Magn. Reson., 1999, 137, pp. 67–82.
  67. 67)
      • 65. Jiang, H., van Zijl, P.C.M., Kim, J., et al: ‘DTI studio: a resource program for diffusion tensor computation and fiber bundle tracking’, Comput. Methods Programs Med., 2006, 81, pp. 106–116.
  68. 68)
      • 3. Mori, S., Zhang, J.: ‘Principles of diffusion tensor imaging and its applications to basic neuroscience research’, Neuron, 2006, 51, pp. 527–539.
  69. 69)
      • 5. Ryan, P.C., Bastinb David, E., Laidlawa, H.: ‘A comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging’, Neuroimage, 2017, 146, pp. 100–112.
  70. 70)
      • 44. Buades, A., Coll, B., Morel, J.M.: ‘A non-local algorithm for image denoising’. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 2005, vol. 2, pp. 60–65.
  71. 71)
      • 69. Westin, C.F., Maier, S.E., Mamata, H., et al: ‘Processing and visualization for diffusion tensor MRI’, Med. Image Anal., 2002, 6, pp. 93–108.
  72. 72)
      • 39. McGraw, T., Vemuri, B., Ozarslan, E., et al: ‘Variational denoising of diffusion weighted MRI’, Inverse Probl. Imaging, 2009, 3, pp. 625–648.
  73. 73)
      • 71. Masutani, Y., Aoki, S., Abe, O., et al: ‘MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization’, Eur. J. Radiol., 2003, 46, pp. 53–66.