Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

Autoři: Seyyed M. H. Haddad aff001;  Christopher J. M. Scott aff002;  Miracle Ozzoude aff002;  Melissa F. Holmes aff002;  Stephen R. Arnott aff003;  Nuwan D. Nanayakkara aff001;  Joel Ramirez aff002;  Sandra E. Black aff002;  Dar Dowlatshahi aff005;  Stephen C. Strother aff003;  Richard H. Swartz aff004;  Sean Symons aff008;  Manuel Montero-Odasso aff009;  ;  Robert Bartha aff001
Působiště autorů: Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada aff001;  L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada aff002;  Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada aff003;  Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada aff004;  Ottawa Hospital Research Institute, Ottawa, Ontario, Canada aff005;  Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada aff006;  Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Ontario, Canada aff007;  Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada aff008;  Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, University of Western Ontario, London, Ontario, Canada aff009;  Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada aff010
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226715


The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Klíčová slova:

Computational pipelines – Data acquisition – Data processing – Diffusion tensor imaging – Diffusion weighted imaging – Image processing – Magnetic resonance imaging – Graphics pipelines


1. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics. 2007;4: 316–29. doi: 10.1016/j.nurt.2007.05.011 17599699

2. Rajagopalan V, Jiang Z, Yue GH, Radic JS, Pioro EP, Wylie GR, et al. A Basic Introduction to Diffusion Tensor Imaging Mathematics and Image Processing Steps. Brain Disord Ther. 2017;6.

3. Hulkower MB, Poliak DB, Rosenbaum SB, Zimmerman ME, Lipton ML. A Decade of DTI in Traumatic Brain Injury: 10 Years and 100 Articles Later. Am J Neuroradiol. 2013;34: 2064–2074. doi: 10.3174/ajnr.A3395 23306011

4. Beaulieu C. The basis of anisotropic water diffusion in the nervous system—a technical review. NMR Biomed. 2002;15: 435–455. doi: 10.1002/nbm.782 12489094

5. Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage. 2018;166: 400–424. doi: 10.1016/j.neuroimage.2017.10.034 29079522

6. Budin F, Hoogstoel M, Reynolds P, Grauer M, O’Leary-Moore SK, Oguz I. Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics. Front Neuroinform. 2013;7: 15. doi: 10.3389/fninf.2013.00015 23964234

7. Cui Z, Zhong S, Xu P, He Y, Gong G. PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci. 2013;7: 42. doi: 10.3389/fnhum.2013.00042 23439846

8. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31: 1487–1505. doi: 10.1016/j.neuroimage.2006.02.024 16624579

9. Jahanshad N, Kochunov P V., Sprooten E, Mandl RC, Nichols TE, Almasy L, et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group. Neuroimage. 2013;81: 455–469. doi: 10.1016/j.neuroimage.2013.04.061 23629049

10. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013;80: 105–124. doi: 10.1016/j.neuroimage.2013.04.127 23668970

11. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 2010;23: 803–820. doi: 10.1002/nbm.1543 20886566

12. Jiang H, van Zijl PCM, Kim J, Pearlson GD, Mori S. DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed. 2006;81: 106–116. doi: 10.1016/j.cmpb.2005.08.004 16413083

13. Oguz I, Farzinfar M, Matsui J, Budin F, Liu Z, Gerig G, et al. DTIPrep: quality control of diffusion-weighted images. Front Neuroinform. 2014;8: 4. doi: 10.3389/fninf.2014.00004 24523693

14. Koay CG, Özarslan E, Pierpaoli C. Probabilistic Identification and Estimation of Noise (PIESNO): A self-consistent approach and its applications in MRI. J Magn Reson. 2009;199: 94–103. doi: 10.1016/j.jmr.2009.03.005 19346143

15. Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, et al. Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage. 2016;135: 311–323. doi: 10.1016/j.neuroimage.2016.04.041 27138209

16. Fortin J-P, Parker D, Tunç B, Watanabe T, Elliott MA, Ruparel K, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage. 2017;161: 149–170. doi: 10.1016/j.neuroimage.2017.08.047 28826946

17. Jenkins J, Chang L-C, Hutchinson E, Irfanoglu MO, Pierpaoli C. Harmonization of methods to facilitate reproducibility in medical data processing: Applications to diffusion tensor magnetic resonance imaging. 2016 IEEE International Conference on Big Data (Big Data). IEEE; 2016. pp. 3992–3994.

18. Suinesiaputra A, Sanghvi MM, Aung N, Paiva JM, Zemrak F, Fung K, et al. Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results. Int J Cardiovasc Imaging. 2017 [cited 1 Jan 2018]. doi: 10.1007/s10554-017-1225-9 28836039

19. Welikala RA, Foster PJ, Whincup PH, Rudnicka AR, Owen CG, Strachan DP, et al. Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort. Comput Biol Med. 2017;90: 23–32. doi: 10.1016/j.compbiomed.2017.09.005 28917120

20. Petersen SE, Sanghvi MM, Aung N, Cooper JA, Paiva JM, Zemrak F, et al. The impact of cardiovascular risk factors on cardiac structure and function: Insights from the UK Biobank imaging enhancement study. Fukumoto Y, editor. PLoS One. 2017;12: e0185114. doi: 10.1371/journal.pone.0185114 28973022

21. Farhan SMK, Bartha R, Black SE, Corbett D, Finger E, Freedman M, et al. The Ontario Neurodegenerative Disease Research Initiative (ONDRI). Can J Neurol Sci / J Can des Sci Neurol. 2017;44: 196–202. doi: 10.1017/cjn.2016.415 28003035

22. Lauzon CB, Asman AJ, Esparza ML, Burns SS, Fan Q, Gao Y, et al. Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging. Alexander DC, editor. PLoS One. 2013;8: e61737. doi: 10.1371/journal.pone.0061737 23637895

23. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8: 153–82. doi: 10.1007/s11682-013-9269-5 24399358

24. Kochunov P, Jahanshad N, Sprooten E, Nichols TE, Mandl RC, Almasy L, et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. Neuroimage. 2014;95: 136–150. doi: 10.1016/j.neuroimage.2014.03.033 24657781

25. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, et al. The Human Connectome Project: A data acquisition perspective. Neuroimage. 2012;62: 2222–2231. doi: 10.1016/j.neuroimage.2012.02.018 22366334

26. Farzinfar M, Oguz I, Smith RG, Verde AR, Dietrich C, Gupta A, et al. Diffusion imaging quality control via entropy of principal direction distribution. Neuroimage. 2013;82: 1–12. doi: 10.1016/j.neuroimage.2013.05.022 23684874

27. Chang L-C, Jones DK, Pierpaoli C. RESTORE: Robust estimation of tensors by outlier rejection. Magn Reson Med. 2005;53: 1088–1095. doi: 10.1002/mrm.20426 15844157

28. Graham MS, Drobnjak I, Zhang H. Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques. Neuroimage. 2016;125: 1079–1094. doi: 10.1016/j.neuroimage.2015.11.006 26549300

29. Graham MS, Drobnjak I, Jenkinson M, Zhang H. Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI. Yap P-T, editor. PLoS One. 2017;12: e0185647. doi: 10.1371/journal.pone.0185647 28968429

30. Drobnjak I, Gavaghan D, Süli E, Pitt-Francis J, Jenkinson M. Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magn Reson Med. 2006;56: 364–380. doi: 10.1002/mrm.20939 16841304

31. Drobnjak I, Pell GS, Jenkinson M. Simulating the effects of time-varying magnetic fields with a realistic simulated scanner. Magn Reson Imaging. 2010;28: 1014–1021. doi: 10.1016/j.mri.2010.03.029 20418038

32. NITRC: Simulated Diffusion-Weighted Datasets: Tool/Resource Info. [cited 10 Apr 2019].

33. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20: 45–57. doi: 10.1109/42.906424 11293691

34. Alexander DC, Barker GJ, Arridge SR. Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn Reson Med. 2002;48: 331–340. doi: 10.1002/mrm.10209 12210942

35. Montero-Odasso M, Pieruccini-Faria F, Bartha R, Black SE, Finger E, Freedman M, et al. Motor Phenotype in Neurodegenerative Disorders: Gait and Balance Platform Study Design Protocol for the Ontario Neurodegenerative Research Initiative (ONDRI). J Alzheimer’s Dis. 2017;59: 707–721. doi: 10.3233/JAD-170149 28671116

36. Dade L., Gao F., Kovacevic N, Roy P, Rockel C, O’Toole C., et al. Semiautomatic brain region extraction: a method of parcellating brain regions from structural magnetic resonance images. Neuroimage. 2004;22: 1492–1502. doi: 10.1016/j.neuroimage.2004.03.023 15275906

37. Ramirez J, Scott CJM, McNeely AA, Berezuk C, Gao F, Szilagyi GM, et al. Lesion Explorer: a video-guided, standardized protocol for accurate and reliable MRI-derived volumetrics in Alzheimer’s disease and normal elderly. J Vis Exp. 2014 [cited 10 Jul 2018]. doi: 10.3791/50887 24797507

38. Ramirez J, Gibson E, Quddus A, Lobaugh NJ, Feinstein A, Levine B, et al. Lesion Explorer: A comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue. Neuroimage. 2011;54: 963–973. doi: 10.1016/j.neuroimage.2010.09.013 20849961

39. Gibson E, Gao F, Black SE, Lobaugh NJ. Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T. J Magn Reson Imaging. 2010;31: 1311–1322. doi: 10.1002/jmri.22004 20512882

40. Ramirez J, Scott CJM, Black SE. A Short-Term Scan–Rescan Reliability Test Measuring Brain Tissue and Subcortical Hyperintensity Volumetrics Obtained Using the Lesion Explorer Structural MRI Processing Pipeline. Brain Topogr. 2013;26: 35–38. doi: 10.1007/s10548-012-0228-z 22562092

41. Kovacevic N, Lobaugh NJ, Bronskill MJ, Levine B, Feinstein A, Black SE. A robust method for extraction and automatic segmentation of brain images. Neuroimage. 2002;17: 1087–100. Available: doi: 10.1006/nimg.2002.1221

42. Ramirez J, Berezuk C, McNeely AA, Scott CJM, Gao F, Black SE. Visible Virchow-Robin Spaces on Magnetic Resonance Imaging of Alzheimer’s Disease Patients and Normal Elderly from the Sunnybrook Dementia Study. J Alzheimer’s Dis. 2014;43: 415–424. doi: 10.3233/JAD-132528 25096616

43. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30: 1323–1341. doi: 10.1016/j.mri.2012.05.001 22770690

44. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62: 782–790. doi: 10.1016/j.neuroimage.2011.09.015 21979382

45. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54: 2033–44. doi: 10.1016/j.neuroimage.2010.09.025 20851191

46. Cook PA, Bai Y, Nedjati-Gilani S, Seunarine KK, Hall MD, Parker GJM, et al. Camino: Open-Source Diffusion-MRI Reconstruction and Processing. 2006.

47. Rorden C, Brett M. Stereotaxic Display of Brain Lesions. Behav Neurol. 2000;12: 191–200. doi: 10.1155/2000/421719 11568431

48. Adluru N, Zhang H, Fox AS, Shelton SE, Ennis CM, Bartosic AM, et al. A diffusion tensor brain template for Rhesus Macaques. Neuroimage. 2012;59: 306–318. doi: 10.1016/j.neuroimage.2011.07.029 21803162

49. Mustra M, Delac K, Grgic M. Overview of the DICOM standard. 2008 50th International Symposium ELMAR. 2008. pp. 39–44.

50. Cox RW, Ashburner J, Breman H, Fissell K, Haselgrove C, Holmes CJ, et al. A (sort of) new image data format standard: NiFTI-1. 10th Annual Meeting of the Organization for Human Brain Mapping. 2004.

51. 3D Slicer. [cited 17 Apr 2018].

52. Kikinis R, Pieper SD, Vosburgh KG. 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer New York; 2014. pp. 277–289. doi: 10.1007/978-1-4614-7657-3_19

53. MRIcron Index Page. [cited 17 Apr 2018].

54. Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, et al. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. Neuroimage. 2006;30: 436–443. doi: 10.1016/j.neuroimage.2005.09.046 16300968

55. Collewet G, Davenel A, Toussaint C, Akoka S. Correction of intensity nonuniformity in spin-echo T(1)-weighted images. Magn Reson Imaging. 2002;20: 365–73. doi: 10.1016/s0730-725x(02)00502-7 12165356

56. Tustison NJ, Avants BB, Cook PA, Yuanjie Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: Improved N3 Bias Correction. IEEE Trans Med Imaging. 2010;29: 1310–1320. doi: 10.1109/TMI.2010.2046908 20378467

57. Iglesias JE, Liu Cheng-Yi, Thompson PM, Tu Zhuowen. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Trans Med Imaging. 2011;30: 1617–1634. doi: 10.1109/TMI.2011.2138152 21880566

58. Huang H, Ceritoglu C, Li X, Qiu A, Miller MI, van Zijl PCM, et al. Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping. Magn Reson Imaging. 2008;26: 1294–1302. doi: 10.1016/j.mri.2008.03.005 18499384

59. Bhushan C, Haldar JP, Joshi AA, Leahy RM. Correcting Susceptibility-Induced Distortion in Diffusion-Weighted MRI using Constrained Nonrigid Registration. Signal Inf Process Assoc Annu Summit Conf (APSIPA). Asia-Pacific Asia-Pacific Signal Inf Process Assoc Annu Summit Conf. 2012;2012.

60. Kybic J, Thevenaz P, Nirkko A, Unser M. Unwarping of unidirectionally distorted EPI images. IEEE Trans Med Imaging. 2000;19: 80–93. doi: 10.1109/42.836368 10784280

61. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17: 825–41. doi: 10.1016/s1053-8119(02)91132-8

62. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5: 143–56. Available: doi: 10.1016/s1361-8415(01)00036-6

63. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009;48: 63–72. doi: 10.1016/j.neuroimage.2009.06.060 19573611

64. Basu S, Fletcher T, Whitaker R. Rician noise removal in diffusion tensor MRI. Med Image Comput Comput Assist Interv. 2006;9: 117–25. Available:

65. Tristán-Vega A, Aja-Fernández S. DWI filtering using joint information for DTI and HARDI. Med Image Anal. 2010;14: 205–218. doi: 10.1016/ 20005152

66. Aja-Fernandez S, Niethammer M, Kubicki M, Shenton ME, Westin C-F. Restoration of DWI data using a Rician LMMSE estimator. IEEE Trans Med Imaging. 2008;27: 1389–403. doi: 10.1109/TMI.2008.920609 18815091

67. Polzehl J, Tabelow K. Structural adaptive smoothing Diffusion Tensor Imaging data: the R Package dti. Neuroimage. 2009;47: S51. doi: 10.1016/S1053-8119(09)70129-6

68. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20: 870–888. doi: 10.1016/S1053-8119(03)00336-7 14568458

69. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125: 1063–1078. doi: 10.1016/j.neuroimage.2015.10.019 26481672

70. Gallichan D, Andersson JLR, Jenkinson M, Robson MD, Miller KL. Reducing distortions in diffusion-weighted echo planar imaging with a dual-echo blip-reversed sequence. Magn Reson Med. 2010;64: n/a–n/a. doi: 10.1002/mrm.22318 20665782

71. Wang S, Peterson DJ, Gatenby JC, Li W, Grabowski TJ, Madhyastha TM, et al. Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI. 2017 [cited 17 Apr 2018].

72. Calhoun VD, Wager TD, Krishnan A, Rosch KS, Seymour KE, Nebel MB, et al. The Impact of T1 Versus EPI Spatial Normalization Templates for fMRI Data Analyses. Hum Brain Mapp. 2017;38: 5331–5342. doi: 10.1002/hbm.23737 28745021

73. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang M-C, et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage. 2009;46: 786–802. doi: 10.1016/j.neuroimage.2008.12.037 19195496

74. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12: 26–41. doi: 10.1016/ 17659998

75. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17: 143–155. doi: 10.1002/hbm.10062 12391568

76. Camino | UCL Camino Diffusion MRI Toolkit. [cited 20 Apr 2018].

77. Basser PJ, Mattiello J, Lebihan D. Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. J Magn Reson Ser B. 1994;103: 247–254. doi: 10.1006/JMRB.1994.1037 8019776

78. Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med. 2003;50: 1077–1088. doi: 10.1002/mrm.10609 14587019

79. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage. 2007;34: 144–155. doi: 10.1016/j.neuroimage.2006.09.018 17070705

80. Moonen XJEF, Foster-Dingley XJC, Van Den Berg-Huijsmans XAA, De Ruijter XW, De Craen XAJM, Van Der Grond XJ, et al. Influence of Small Vessel Disease and Microstructural Integrity on Neurocognitive Functioning in Older Individuals: The DANTE Study Leiden. [cited 20 Apr 2018]. doi: 10.3174/ajnr.A4934 27659190

81. Weickert J, Stuttgart BGT. Anisotropic Diffusion in Image Processing.

82. Palma CA, Cappabianco FAM, Ide JS, Miranda PAV. Anisotropic Diffusion Filtering Operation and Limitations—Magnetic Resonance Imaging Evaluation. IFAC Proc Vol. 2014;47: 3887–3892. doi: 10.3182/20140824-6-ZA-1003.02347

83. Correia S, Lee SY, Voorn T, Tate DF, Paul RH, Zhang S, et al. Quantitative Tractography Metrics of White Matter Integrity in Diffusion-Tensor MRI. [cited 23 Jul 2019]. doi: 10.1016/j.neuroimage.2008.05.022 18617421

84. Chen H-J, Gao Y-Q, Che C-H, Lin H, Ruan X-L. Diffusion Tensor Imaging With Tract-Based Spatial Statistics Reveals White Matter Abnormalities in Patients With Vascular Cognitive Impairment. Front Neuroanat. 2018;12: 53. doi: 10.3389/fnana.2018.00053 29997482

85. Biesbroek JM, Leemans A, den Bakker H, Duering M, Gesierich B, Koek HL, et al. Microstructure of Strategic White Matter Tracts and Cognition in Memory Clinic Patients with Vascular Brain Injury. Dement Geriatr Cogn Disord. 2017;44: 268–282. doi: 10.1159/000485376 29353280

86. DTI Protocols « ENIGMA. [cited 1 Oct 2019].

87. Bastiani M, Andersson JLR, Cordero-Grande L, Murgasova M, Hutter J, Price AN, et al. Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. Neuroimage. 2019;185: 750–763. doi: 10.1016/j.neuroimage.2018.05.064 29852283

Článek vyšel v časopise


2019 Číslo 12