MR Image Postprocessing for Multiple Sclerosis Research

Neuroimaging Clinics of North America

Volumn 18, Issue 4, 2008, Pages 637-649

  Horsfield, Mark A ^a  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Image analysis; MR imaging; Multiple sclerosis; Postprocessing; Visualization

Indexed keywords

AGING; BRAIN ATROPHY; BRAIN BLOOD FLOW; CEREBROSPINAL FLUID; COMPUTER PROGRAM; DIFFERENTIAL DIAGNOSIS; DIFFUSION TENSOR IMAGING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRAY MATTER; HISTOGRAM; HUMAN; IMAGING; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PARENCHYMA; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIOFREQUENCY; REVIEW; SIGNAL TRANSDUCTION; SPINAL CORD ATROPHY; WHITE MATTER;

BIOMEDICAL RESEARCH; BRAIN; CEREBROVASCULAR CIRCULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MULTIPLE SCLEROSIS; REPRODUCIBILITY OF RESULTS;

EID: 54949141813     PISSN: 10525149     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.nic.2008.06.006     Document Type: Review

References (81)

1. Bydder G.M., Steiner R.E., Young I.R., et al. Clinical NMR imaging of the brain: 140 cases. AJR Am J Roentgenol 139 2 (1982) 215-236
2. Rinck P.A., Bielke G., and Meves M. Modified spin-echo sequence in tumor diagnosis. Proceedings of the Society of Magnetic Resonance in Medicine. Magn Reson Med 1 2 (1984) 236
3. Miller D.H., Rudge P., Johnson G., et al. Serial gadolinium enhanced magnetic resonance imaging in multiple sclerosis. Brain 111 4 (1988) 927-939
4. Wicks D.A.G., Barker G.J., and Tofts P.S. Correction of intensity nonuniformity in MR images of any orientation. Magn Reson Imaging 11 2 (1993) 183-196
5. Belaroussi B., Milles J., Carme S., et al. Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal 10 2 (2006) 234-246
6. Horsfield M.A., Rovaris M., Rocca M.A., et al. Whole-brain atrophy in multiple sclerosis measured by two segmentation processes from various MRI sequences. J Neurol Sci 216 1 (2003) 169-177
7. Brechbuhler C., Gerig G., and Szekely G.S. Compensation of spatial inhomogeneity in MRI based on a parametric bias estimate. Visualization in Biomedical Computing 1131 (1996) 141-146
8. Sled J.G., Zijdenbos A.P., and Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17 1 (1998) 87-97
9. Gispert J.D., Reig S., Pascau J., et al. Method for bias field correction of brain T1 weighted magnetic resonance images minimizing segmentation error. Hum Brain Mapp 22 2 (2004) 133-144
10. Hill D.L.G., Batchelor P.G., Holden M., et al. Medical image registration. Phys Med Biol 46 3 (2001) R1-R45
11. Fox N.C., and Freeborough P.A. Brain atrophy progression measured from registered serial MRI: validation and application to Alzheimer's disease. J Magn Reson Imaging 7 6 (1997) 1069-1075
12. Studholme C., Hill D.L.G., and Hawkes D.J. Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med Phys 24 1 (1997) 25-35
13. Collins D.L., Neelin P., Peters T.M., et al. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18 2 (1994) 192-205
14. Woods R.P., Grafton S.T., Holmes C.J., et al. Automated image registration: I. General methods and intrasubject, intramodality validation. J Comput Assist Tomogr 22 1 (1998) 139-152
15. Pluim J.P.W., Maintz J.B.A., and Viergever M.A. Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22 8 (2003) 986-1004
16. Talairach J., and Tournoux P. Co-planar stereotaxic atlas of the human brain (1988), Thieme Medical Publishers, New York
17. Lancaster J.L., Tordesillas-Gutierrez D., Martinez M., et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum Brain Mapp 28 11 (2007) 1194-1205
18. Mechelli A., Price C.J., Friston K.J., et al. Voxel-based morphometry of the human brain: methods and applications. Current Medical Imaging Reviews 1 2 (2005) 105-113
19. Sepulcre J., Sastre-Garriga J., Cercignani M., et al. Regional gray matter atrophy in early primary progressive multiple sclerosis-a voxel-based morphometry study. Arch Neurol 63 8 (2006) 1175-1180
20. Rohde G.K., Aldroubi A., Dawant B.M., et al. The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans Med Imaging 22 11 (2003) 1470-1479
21. Pagani E., Horsfield M.A., Rocca M.A., et al. Assessing atrophy of the major white matter fiber bundles of the brain from diffusion tensor MRI data. Magn Reson Med 58 3 (2007) 527-534
22. Filippi M., Horsfield M.A., Ader H.J., et al. Guidelines for using quantitative measures of brain magnetic resonance imaging abnormalities in monitoring the treatment of multiple sclerosis. Ann Neurol 43 4 (1998) 499-506
23. Tomassini V., Paolillo A., Russo P., et al. Predictors of long-term clinical response to interferon beta therapy in relapsing multiple sclerosis. J Neurol 253 3 (2006) 287-293
24. Barkhof F., Karas G.B., and van Walderveen M.A.A. T1 hypointensities and axonal loss. Neuroimaging Clin N Am 10 4 (2000) 739-752
25. Grimaud J., Lai M., Thorpe J., et al. Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. Magn Reson Imaging 14 5 (1996) 495-505
26. Cader S., Cifelli A., Abu-Omar Y., et al. Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain 129 2 (2006) 527-537
27. Kikinis R., Shenton M.E., Gerig G., et al. Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 2 2 (1992) 619-629
28. Bedell B.J., Narayana P.A., and Wolinsky J.S. A dual approach for minimizing false lesion classifications on magnetic resonance images. Magn Reson Med 37 1 (1997) 94-102
29. Zijdenbos A., Forghani R., and Evans A. Automatic quantification of MS lesions in 3D MRI brain data sets: validation of INSECT. Lect Notes Comput Sci 1496 (1998) 439-448
30. Anbeek P., Vincken K.L., van Osch M.J.P., et al. Probabilistic segmentation of white lesions in MR imaging. Neuroimage 21 3 (2004) 1037-1044
31. van Leemput K., Maes F., Vandermeulen D., et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 20 8 (2001) 677-688
32. Warfield S., Dengler J., Zaers J., et al. Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions. J Image Guid Surg 1 6 (1995) 326-338
33. Udupa J.K., Wei L., Samarasekera S., et al. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Trans Med Imaging 16 (1997) 598-609
34. Sajja B.R., Datta S., He R.J., et al. Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng 442 1 (2006) 142-151
35. Horsfield M.A., Bakshi R., Rovaris M., et al. Incorporating domain knowledge into the fuzzy connectedness framework: application to brain lesion volume estimation in multiple sclerosis. IEEE Trans Med Imaging 26 12 (2007) 1670-1680
36. Fisher E., Rudick R.A., Simon J.H., et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology 59 9 (2002) 1412-1420
37. Good C.D., Johnsrude I.S., Ashburner J., et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14 1 (2001) 21-36
38. Smith S.M., Zhang Y.Y., Jenkinson M., et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17 1 (2002) 479-489
39. Rudick R.A., Fisher E., Lee J.C., et al. Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Neurology 53 8 (1999) 1698-1704
40. Freeborough P.A., and Fox N.C. Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. J Comput Assist Tomogr 22 5 (1998) 838-843
41. Fox N.C., Jenkins R., Leary S.M., et al. Progressive cerebral atrophy in MS-a serial study using registered, volumetric MRI. Neurology 54 4 (2000) 807-812
42. Smith S.M. Fast robust automated brain extraction. Hum Brain Mapp 17 3 (2002) 143-155
43. Lin X., Tench C.R., Turner B., et al. Spinal cord atrophy and disability in multiple sclerosis over four years: application of a reproducible automated technique in monitoring disease progression in a cohort of the interferon beta-1a (Rebif) treatment trial. J Neurol Neurosurg Psychiatry 74 8 (2003) 1090-1094
44. Losseff N.A., Webb S.L., and O'Riordan J.I. Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. Brain 19 3 (1996) 701-708
45. Horsfield M.A. Magnetization transfer imaging in multiple sclerosis. J Neuroimaging 15 4 (2005) 58S-67S
46. Barker G.J., Tofts P.S., and Gass A. An interleaved sequence for accurate and reproducible clinical measurement of magnetization transfer ratio. Magn Reson Imaging 14 4 (1996) 403-411
47. Inglese M., Horsfield M.A., and Filippi M. Scan-rescan variation of measures derived from brain magnetization transfer ratio histograms obtained in healthy volunteers by use of a semi-interleaved magnetization transfer sequence. AJNR Am J Neuroradiol 22 4 (2001) 681-684
48. Barbosa S., Blumhardt L.D., Roberts N., et al. Magnetic resonance relaxation time mapping in multiple sclerosis-normal-appearing white matter and the invisible lesion load. Magn Reson Imaging 12 (1994) 33-42
49. Neema M., Stankiewicz J., Arora A., et al. T1-and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. J Neuroimaging 17 Suppl 1 (2007) 16S-21S
50. Kidd D., Barker G.J., Tofts P.S., et al. The transverse magnetization decay characteristics of longstanding lesions and normal-appearing white matter in multiple sclerosis. J Neurol 244 2 (1997) 125-130
51. Butler J.P., Reeds J.A., and Dawson S.V. Estimating solutions of first kind integral equations with nonnegative constraints and optimal smoothing. Society for Industrial and Applied Mathematics Journal on Numerical Analysis 18 3 (1981) 381-397
52. Whittall K.P., MacKay A.L., Graeb D.A., et al. In vivo measurement of T2 distributions and water contents in normal human brain. Magn Reson Med 37 1 (1997) 34-43
53. Laule C., Kozlowski P., Leung E., et al. Myelin water imaging of multiple sclerosis at 7 T: correlations with histopathology. Neuroimage 40 4 (2008) 1575-1580
54. Rovaris M., Gass A., Bammer R., et al. Diffusion MRI in multiple sclerosis. Neurology 65 10 (2005) 1526-1532
55. Horsfield M.A., Larsson H.B.W., Jones D.K., et al. Diffusion magnetic resonance imaging in multiple sclerosis. J Neurol Neurosurg Psychiatry 64 1 (1998) S80-S84
56. Basser P.J., Mattiello J., and LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 66 1 (1994) 259-267
57. Turner R., LeBihan D., Maier J., et al. Echo-planar imaging of intravoxel incoherent motion. Radiology 177 2 (1990) 407-414
58. Horsfield M.A. Mapping eddy current induced fields for the correction of diffusion weighted echo planar images. Magn Reson Imaging 17 9 (1999) 1335-1345
59. Zhuang J.C., Hrabe J., Kangarlu A., et al. Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients. J Magn Reson Imaging 24 5 (2006) 1188-1193
60. Andersson J.L.R., and Skare S. A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI. Neuroimage 16 1 (2002) 177-199
61. Mori S., Crain B.J., Chacko V.P., et al. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45 2 (1999) 265-269
62. Jones D.K., Travis A.R., Eden G., et al. PASTA: pointwise assessment of streamline tractography attributes. Magn Reson Med 53 6 (2005) 1462-1467
63. Smith S.M., Jenkinson M., Johansen-Berg H., et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31 4 (2006) 1487-1505
64. Jones D.K. Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magn Reson Med 49 1 (2003) 7-12
65. Pagani E., Filippi M., Rocca M.A., et al. A method for obtaining tract specific diffusion tensor MRI measurements in the presence of disease: application to patients with clinically isolated syndromes suggestive of multiple sclerosis. Neuroimage 26 1 (2005) 258-265
66. Tuch D.S. Q-Ball imaging. Magn Reson Med 52 6 (2004) 1358-1372
67. Tournier J.D. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23 3 (2004) 1176-1185
68. Ostergaard L., Weisskoff R.M., Chesler D.A., et al. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. 1. Mathematical approach and statistical analysis. Magn Reson Med 36 5 (1996) 715-725
69. Boxerman J.L., Schmainda K.M., and Weisskoff R.M. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27 4 (2006) 859-867
70. Gunther M., Bock M., and Schad L.R. Arterial spin labeling in combination with a look-locker sampling strategy: inflow turbo-sampling EPI-FAIR (ITS-FAIR). Magn Reson Med 46 5 (2001) 974-984
71. Petersen E.T., Zimine I., Ho Y.C., et al. Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol 79 944 (2006) 688-701
72. Adhya S., Johnson G., Herbert J., et al. Pattern of hemodynamic impairment in multiple sclerosis: dynamic susceptibility contrast perfusion MR imaging at 3.0 T. Neuroimage 33 4 (2006) 1029-1035
73. Rashid W., Parkes L.M., Ingle G.T., et al. Abnormalities of cerebral perfusion in multiple sclerosis. J Neurol Neurosurg Psychiatry 75 9 (2004) 1288-1293
74. Inglese M., Adhya S., Johnson G., et al. Perfusion magnetic resonance imaging correlates of neuropsychological impairment in multiple sclerosis. J Cereb Blood Flow Metab 28 1 (2008) 164-171
75. Zijdenbos A.P., Forghani R., and Evans A.C. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21 10 (2002) 1280-1291
76. Xinapse Systems Limited. Jim image analysis software page. Available at:. http://www.xinapse.com Accessed March 10, 2008
77. 3D Slicer. Available at:. http://www.slicer.org Accessed March 10, 2008
78. Gering D.T., Nabavi A., Kikinis R., et al. An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging 13 6 (2001) 967-997
79. U.S. Food and Drug Administration. Good clinical practice in FDA-related clinical trials page. Available at:. http://www.fda.gov/oc/gcp/ Accessed March 10, 2008
80. The Association of Electrical and Medical Equipment Manufacturers. Digital imaging and communications in medicine page. Available at:. http://dicom.nema.org Accessed March 10, 2008
81. Liu L.F., Meier D., Polgar-Turcsanyi M., et al. Multiple sclerosis medical image analysis and information management. J Neuroimaging 15 4 (2005) 103S-117S