DTIPrep: Quality control of diffusion-weighted images

Frontiers in Neuroinformatics

Volumn 8, Issue JAN, 2014, Pages

  Oguz, Ipek ^a   Farzinfar, Mahshid ^b   Matsui, Joy ^c   Budin, Francois ^b   Liu, Zhexing ^d   Gerig, Guido ^e   Johnson, Hans J ^c   Styner, Martin ^b  

^a University of Iowa   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF IOWA   (United States)

^d SOUTHERN MEDICAL UNIVERSITY   (China)

^e UNIVERSITY OF UTAH   (United States)

Author keywords

Diffusion MRI; Diffusion tensor imaging; Open source; Preprocessing; Quality control; Software

Indexed keywords

ALGORITHM; ARTICLE; ARTIFACT; AUTISM; BRAIN REGION; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; DIFFUSION TENSOR IMAGING; DIFFUSION WEIGHTED IMAGING; ENTROPY; FRACTIONAL ANISOTROPY; HEAD MOVEMENT; HUMAN; IMAGE RECONSTRUCTION; IMAGING SOFTWARE; INSTRUMENT VALIDATION; META ANALYSIS; NEUROIMAGING; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SCANNER; QUALITY CONTROL; REPRODUCIBILITY; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; SIMULATION; SOUND INTENSITY; STATISTICAL BIAS; TRACTOGRAPHY; VIBRATION; WHITE MATTER;

EID: 84893424631     PISSN: 16625196     EISSN: None     Source Type: Journal    
DOI: 10.3389/fninf.2014.00004     Document Type: Article

References (50)

1. Barkovich, A. J. (2000). Concepts of myelin and myelination in neuroradiology. AJNR Am. J. Neuroradiol. 21, 1099-1109.
2. Basser, P., and Pierpaoli, C. (1995). "Elucidating tissue structure by diffusion tensor MRI, " in SMR/ESMRMB (Nice, France), 900.
3. Basser, P. J., and Pierpaoli, C. (1998). A simplified method to measure the diffusion tensor from seven MR images. Magn. Reson. Med. 39, 928-934. doi: 10.1002/mrm.1910390610
4. Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., and Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34, 144-155. doi: 10.1016/j.neuroimage.2006.09.018
5. Boucharin, A., Oguz, I., Vachet, C., Shi, Y., Sanchez, M., and Styner, M. (2011). Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation. Proc. SPIE 7962, 79620S. doi: 10.1117/12.878291
6. Chang, L.-C., Jones, D. K., and Pierpaoli, C. (2005). Restore: robust estimation of tensors by outlier rejection. Magn. Reson. Med. 53, 1088-1095. doi: 10.1002/mrm.20426
7. Ciccarelli, O., Werring, D. J., Wheeler-Kingshott, C. A., Barker, G. J., Parker, G. J., Thompson, A. J., et al. (2001). Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations. Neurology 56, 926-933. doi: 10.1212/WNL.56.7.926
8. Coleman, L. G., Oguz, I., Lee, J., Styner, M., and Crews, F. (2012). Postnatal day 7 ethanol treatment causes persistent reductions in adult mouse brain volume and cortical neurons with sex specific effects on neurogenesis. Alcohol 46, 603-612. doi: 10.1016/j.alcohol.2012.01.003
9. Cox, R. W., and Hyde, J. S. (1997). Software tools for analysis and visualization of fmri data. NMR Biomed. 10, 171-178. doi: 10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L
10. Dumas, E. M., van den Bogaard, S. J. A., Ruber, M. E., Reilman, R. R., Stout, J. C., Craufurd, D., et al. (2012). Early changes in white matter pathways of the sensorimotor cortex in premanifest Huntington's disease. Hum. Brain Mapp. 33, 203-212. doi: 10.1002/hbm.21205
11. Escolar, M. L., Poe, M. D., Smith, J. K., Gilmore, J. H., Kurtzberg, J., Lin, W., et al. (2009). Diffusion tensor imaging detects abnormalities in the corticospinal tracts of neonates with infantile Krabbe disease. AJNR Am. J. Neuroradiol. 30, 1017-1021. doi: 10.3174/ajnr.A1476
12. Farzinfar, M., Dietrich, C., Smith, R., Li, Y., Gupta, A., Liu, Z., et al. (2012). Entropy based DTI quality control via regional orientation distribution. Proc. IEEE Int. Symp. Biomed. Imaging 2012, 22-26. doi: 10.1109/ISBI.2012.6235474
13. Farzinfar, M., Li, Y., Verde, A. R., Oguz, I., Gerig, G., and Styner, M. A. (2013a). DTI quality control assessment via error estimation from monte carlo simulations. Proc. Soc. Photo Opt. Instrum. Eng. 8669, 1667549. doi: 10.1117/12.2006925
14. Farzinfar, M., Oguz, I., Smith, R. G., Verde, A. R., Dietrich, C., Gupta, A., et al. (2013b). Diffusion imaging quality control via entropy of principal direction distribution. Neuroimage 82C, 1-12. doi: 10.1016/j.neuroimage.2013.05.022
15. Friston, K., Ashburner, J., Kiebel, S., Nichols, T., and Penny, W., editors (2007). Statistical Parametric Mapping: The Analysis of Functional Brain Images. London: Academic Press.
16. Gallichan, D., Scholz, J., Bartsch, A., Behrens, T. E., Robson, M. D., and Miller, K. L. (2010). Addressing a systematic vibration artifact in diffusion-weighted MRI. Hum. Brain Mapp. 31, 193-202. doi: 10.1002/hbm.20856
17. Geng, X., Gouttard, S., Sharma, A., Gu, H., Styner, M., Lin, W., et al. (2012). Quantitative tract-based white matter development from birth to age 2 years. Neuroimage 61, 542-557. doi: 10.1016/j.neuroimage.2012.03.057
18. Gerig, G., Oguz, I., Gouttard, S., Lee, J., An, H., Lin, W., et al. (2011). Synergy of image analysis for animal and human neuroimaging supports translational research on drug abuse. Front Psychiatry 2:53. doi: 10.3389/fpsyt.2011.00053
19. Gilmore, J. H., Kang, C., Evans, D. D., Wolfe, H. M., Smith, J. K., Lieberman, J. A., et al. (2010). Prenatal and neonatal brain structure and white matter maturation in children at high risk for schizophrenia. Am. J. Psychiatry 167, 1083-1091. doi: 10.1176/appi.ajp.2010.09101492
20. Goodlett, C., Fletcher, P., Lin, W., and Gerig, G. (2007). Quantification of measurement error in dti: theoretical predictions and validation. Med. Image Comput. Comput. Assist. Interv. 10, 10-17.
21. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biol. 6:e159. doi: 10.1371/journal.pbio.0060159
22. Hazlett, H. C., Gu, H., McKinstry, R. C., Shaw, D. W. W., Botteron, K. N., Dager, S. R., et al. (2012). Brain Volume findings in 6-month-old infants at high familial risk for autism. Am. J. Psychiatry 169, 601-608. doi: 10.1176/appi.ajp.2012.11091425
23. Hess, C. P., Mukherjee, P., Han, E. T., Xu, D., and Vigneron, D. B. (2006). Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn. Reson. Med. 56, 104-17. doi: 10.1002/mrm.20931
24. Hiltunen, J., Hari, R., Jousmäki, V., Müller, K., Sepponen, R., and Joensuu, R. (2006). Quantification of mechanical vibration during diffusion tensor imaging at 3T. Neuroimage 32, 93-103. doi: 10.1016/j.neuroimage.2006.03.004
25. Jones, D. K. (2004). The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a monte carlo study. Magn. Reson. Med. 51, 807-815. doi: 10.1002/mrm.20033
26. Jones, D. K., Horsfield, M. A., and Simmons, A. (1999). Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42, 515-525. doi: 10.1002/(SICI)1522-2594(199909)42:3<515::AID-MRM14>3.3.CO;2-H
27. Lauzon, C. B., Caffo, B. C., and Landman, B. A. (2012). Towards automatic quantitative quality control for MRI. Proc. Soc. Photo Opt. Instrum. Eng. 8314, pii: 910819. doi: 10.1117/12.910819
28. Li, Q., Cheung, C., Wei, R., Cheung, V., Hui, E. S., You, Y., et al. (2010). Voxel-based analysis of postnatal white matter microstructure in mice exposed to immune challenge in early or late pregnancy. Neuroimage 52, 1-8. doi: 10.1016/j.neuroimage.2010.04.015
29. Liu, K., Hangzhou, Z., and Liu, X. (2011). Vibrationally Decoupled Patient Table for Use in Magnetic Resonance System. US Patent 7,924,008 B2.
30. Liu, Z., Wang, Y., Gerig, G., Gouttard, S., Tao, R., Fletcher, T., et al. (2010). Quality control of diffusion weighted images. Proc. SPIE 7628, 9. doi: 10.1117/12.844748
31. Lucas, B. C., Bogovic, J. A., Carass, A., Bazin, P.-L., Prince, J. L., Pham, D. L., et al. (2010). The java image science toolkit (jist) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 8, 5-17. doi: 10.1007/s12021-010-9065-y
32. McKinstry, R. C., Miller, J. H., Snyder, A. Z., Mathur, A., Schefft, G. L., Almli, C. R., et al. (2002). A prospective, longitudinal diffusion tensor imaging study of brain injury in newborns. Neurology 59, 824-833. doi: 10.1212/WNL.59.6.824
33. Mori, S., and Zijl, P. V. (2002). Fiber tracking: principles and strategies-a technical review. NMR Biomed. 15, 468-480. doi: 10.1002/nbm.781
34. Oguz, I., Boucharin, A., Lu, W., Vachet, C., Budin, F., Shi, Y., et al. (2012a). "A minimum cost approach to connectivity from orientation distribution functions via efficient multi-directional graph propagation, " in Proceedings of CDMRI Workshop, MICCAI 2012, 210-221.
35. Oguz, I., McMurray, M. S., Styner, M., and Johns, J. M. (2012b). The translational role of diffusion tensor image analysis in animal models of developmental pathologies. Dev. Neurosci. 34, 5-19. doi: 10.1159/000336825
36. Pannek, K., Mathias, J. L., Bigler, E. D., Brown, G., Taylor, J. D., and Rose, S. E. (2011). The average pathlength map: a diffusion MRI tractography-derived index for studying brain pathology. Neuroimage 55, 133-141. doi: 10.1016/j.neuroimage.2010.12.010
37. Parnell, S. E., O'Leary-Moore, S. K., Godin, E. A., Dehart, D. B., Johnson, B. W., Johnson, G. A., et al. (2009). Magnetic resonance microscopy defines ethanol-induced brain abnormalities in prenatal mice: effects of acute insult on gestational day 8. Alcohol. Clin. Exp. Res. 33, 1001-1011. doi: 10.1111/j.1530-0277.2009.00921.x
38. Pierpaoli, C., and Basser, P. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893-906. doi: 10.1002/mrm.1910360612
39. Pierpaoli, C., Walker, L., Irfanoglu, M., Barnett, A., Basser, P., Chang, L., et al. (2010). "TORTOISE: an integrated software package for processing of diffusion MRI data, " in ISMRM 18th Annual Meeting (Stockholm, Sweden), 1597. Available online at: https://science.nichd.nih.gov/confluence/display/nihpd/TORTOISE
40. Rose, S. E., Chen, F., Chalk, J. B., Zelaya, F. O., Strugnell, W. E., Benson, M., et al. (2000). Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J. Neurol. Neurosurg. Psychiatry 69, 528-530. doi: 10.1136/jnnp.69.4.528
41. Shrager, R. I., and Basser, P. J. (1998). Anisotropically weighted MRI. Magn. Reson. Med. 40, 160-165. doi: 10.1002/mrm.1910400121
42. Tristán-Vega, A., and Aja-Fernández, S. (2010). Dwi filtering using joint information for dti and hardi. Med. Image Anal. 14, 205-218. doi: 10.1016/j.media.2009.11.001
43. Tuch, D. S. (2004). Q-ball imaging. Magn. Reson. Med. 52, 1358-1372. doi: 10.1002/mrm.20279
44. Wolff, J. J., Gu, H., Gerig, G., Elison, J. T., Styner, M., Gouttard, S., et al. (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am. J. Psychiatry 169, 589-600. doi: 10.1176/appi.ajp.2011.11091447
45. Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., et al. (2009). Bayesian analysis of neuroimaging data in FSL. Neuroimage 45, S173-S186. doi: 10.1016/j.neuroimage.2008.10.055
46. Yan, C., Gong, G., Wang, J., Wang, D., Liu, D., Zhu, C., et al. (2011). Sex-and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cereb. Cortex 21, 449-458. doi: 10.1093/cercor/bhq111
47. Zhai, G., Lin, W., Wilber, K., Gerig, G., and Gilmore, J. (2003). Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-t head-only mr imaging unit. Radiology 229, 673-681. doi: 10.1148/radiol.2293021462
48. Zhang, J., Miller, M. I., Plachez, C., Richards, L. J., Yarowsky, P., van Zijl, P., et al. (2005). Mapping postnatal mouse brain development with diffusion tensor microimaging. Neuroimage 26, 1042-1051. doi: 10.1016/j.neuroimage.2005.03.009
49. Zhang, J., van Zijl, P. C. M., and Mori, S. (2002). Three-dimensional diffusion tensor magnetic resonance microimaging of adult mouse brain and hippocampus. Neuroimage 15, 892-901. doi: 10.1006/nimg.2001.1012
50. Zhang, T., Guo, L., Li, K., Jing, C., Yin, Y., Zhu, D., et al. (2012). Predicting functional cortical ROIs via DTI-derived fiber shape models. Cereb. Cortex 22, 854-864. doi: 10.1093/cercor/bhr152