Reliability of Longitudinal Brain Volume Loss Measurements between 2 Sites in Patients with Multiple Sclerosis: Comparison of 7 Quantification Techniques

References

1. 1.↵
   1. Filippi M,
   2. Mastronardo G,
   3. Rocca MA,
   4. et al

   . Quantitative volumetric analysis of brain magnetic resonance imaging from patients with multiple sclerosis. J Neurol Sci 1998;158:148–53

   CrossRefPubMedWeb of Science
2. 2.↵
   1. Simon JH,
   2. Jacobs LD,
   3. Campion MK,
   4. et al

   . A longitudinal study of brain atrophy in relapsing multiple sclerosis: the Multiple Sclerosis Collaborative Research Group (MSCRG). Neurology 1999;53:139–48

   Abstract/FREE Full Text
3. 3.↵
   1. Horsfield MA,
   2. Rovaris M,
   3. Rocca MA,
   4. et al

   . Whole-brain atrophy in multiple sclerosis measured by two segmentation processes from various MRI sequences. J Neurol Sci 2003;216:169–77

   CrossRefPubMedWeb of Science
4. 4.↵
   1. Sharma J,
   2. Sanfilipo MP,
   3. Benedict RH,
   4. et al

   . Whole-brain atrophy in multiple sclerosis measured by automated versus semiautomated MR imaging segmentation. AJNR Am J Neuroradiol 2004;25:985–96

   Abstract/FREE Full Text
5. 5.↵
   1. Collins DL,
   2. Montagnat J,
   3. Zijdenbos AP,
   4. et al

   . Automated estimation of brain volume in multiple sclerosis with BICCR. Lect Notes Comput Sci 2001;2082:141–47

   CrossRef
6. 6.↵
   1. Fischl B,
   2. Salat DH,
   3. Busa E,
   4. et al

   . Whole brain segmentation: automated labeling of neuroanatomical structures in human brain. Neuron 2002;33:341–55

   CrossRefPubMedWeb of Science
7. 7.↵
   1. Freeborough PA,
   2. Fox NC.

   . The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans Med Imaging 1997;16:623–29

   CrossRefPubMedWeb of Science
8. 8.↵
   1. Freeborough PA,
   2. Fox NC,
   3. Kitney RI.

   . Interactive algorithms for the segmentation and quantitation of 3-D MRI brain scans. Comput Methods Programs Biomed 1997;53:15–25

   CrossRefPubMedWeb of Science
9. 9.↵
   1. Smith SM,
   2. De Stefano N,
   3. Jenkinson M,
   4. et al

   . Normalized accurate measurement of longitudinal brain change. J Comput Assist Tomogr 2001;25:466–75

   CrossRefPubMedWeb of Science
10. 10.↵
    1. Smith SM.

    . Fast robust automated brain extraction. Hum Brain Mapp 2002;17:143–55

    CrossRefPubMedWeb of Science
11. 11.↵
    1. Ashburner J,
    2. Friston KJ.

    . Voxel-based morphometry—the methods. Neuroimage 2000;11:805–21

    CrossRefPubMedWeb of Science
12. 12.↵
    1. Camara O,
    2. Schnabel JA,
    3. Ridgway GR,
    4. et al

    . Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer's disease images. Neuroimage 2008;42:696–709

    CrossRefPubMed
13. 13.↵
    1. Boyes RG,
    2. Rueckert D,
    3. Aljabar P,
    4. et al

    . Cerebral atrophy measurements using Jacobian integration: comparison with the boundary shift integral. Neuroimage 2006;32:159–69

    CrossRefPubMed
14. 14.↵
    1. Camara O,
    2. Schweiger M,
    3. Scahill R,
    4. et al

    . Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans Med Imaging 2006;25:1417–30

    CrossRefPubMed
15. 15.↵
    1. Cotton F,
    2. Weiner HL,
    3. Jolesz FA,
    4. et al

    . MRI contrast uptake in new lesions in relapsing–remitting multiple sclerosis followed at weekly intervals. Neurology 2003;30:640–46
16. 16.↵
    1. Duning T,
    2. Kloska S,
    3. Streinstrater O,
    4. et al

    . Dehydration confounds the assessment of brain atrophy. Neurology 2005;64:548–50

    Abstract/FREE Full Text
17. 17.↵
    1. Sampat MP,
    2. Healy BC,
    3. Meier DS,
    4. et al

    . Disease modelling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements. Neuroimage 2010;52:1367–73

    CrossRefPubMed
18. 18.↵
    1. Dalton CM,
    2. Chard DT,
    3. Davies GR,
    4. et al

    . Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain 2004;127:1101–07

    Abstract/FREE Full Text
19. 19.↵
    1. Gordon N.

    . Apparent cerebral atrophy in patients on treatment with steroids. Dev Med Child Neurol 1980;22:502–06

    PubMedWeb of Science
20. 20.↵
    1. Hoogervorst EL,
    2. Polman CH,
    3. Barkhof F.

    . Cerebral volume changes in multiple sclerosis patients treated with high-dose intravenous methylprednisolone. Mult Scler 2002;8:415–19

    Abstract/FREE Full Text
21. 21.↵
    1. Rao AB,
    2. Richert N,
    3. Howard T,
    4. et al

    . Methylprednisolone effect on brain volume and enhancing lesions in MS before and during IFNbeta-1b. Neurology 2002;59:688–94

    Abstract/FREE Full Text
22. 22.↵
    1. Fox RJ,
    2. Fisher E,
    3. Tkach J,
    4. et al

    . Brain atrophy and magnetization transfer ratio following methylprednisolone in multiple sclerosis: short-term changes and long-term implications. Mult Scler 2005;11:140–45

    Abstract/FREE Full Text
23. 23.↵
    1. Meier D,
    2. Weiner H,
    3. Guttmann C.

    . MR imaging intensity modelling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability. AJNR Am J Neuroradiol 2007;28:1956–63

    Abstract/FREE Full Text
24. 24.↵
    1. Pelletier D,
    2. Garrison K,
    3. Henry R.

    . Measurement of whole-brain atrophy in multiple sclerosis. J Neuroimaging 2004;14:11S–19S

    CrossRefPubMedWeb of Science
25. 25.↵
    1. Bellon EM,
    2. Haacke EM,
    3. Coleman PE,
    4. et al

    . MR artifacts: a review. AJR Am J Roentgenol 1986;147:1271–81

    CrossRefPubMedWeb of Science
26. 26.↵
    1. Caramanos Z,
    2. Fonov VS,
    3. Francis SJ,
    4. et al

    . Gradient distortion in MRI: characterizing and correcting for their effects on SIENA-generated measures of brain volume change. Neuroimage 2010;49:1601–11

    CrossRefPubMed
27. 27.↵
    1. Jovicich J,
    2. Czanner S,
    3. Han X,
    4. et al

    . MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 2009;46:177–92

    CrossRefPubMedWeb of Science
28. 28.↵
    1. Han X,
    2. Jovicich J,
    3. Salat D,
    4. et al

    . Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 2006;32:180–94

    CrossRefPubMedWeb of Science
29. 29.↵
    1. Jovicich J,
    2. Czanner S,
    3. Greve D,
    4. et al

    . Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 2006;30:436–43

    CrossRefPubMedWeb of Science
30. 30.↵
    1. Karaçali B,
    2. Davatzikos C.

    . Simulation of tissue atrophy using a topology preserving transformation model. IEEE Trans Med Imaging 2006;25:649–52

    CrossRefPubMedWeb of Science
31. 31.↵
    1. Pieperhoff P,
    2. Sudmeyer M,
    3. Homke L,
    4. et al

    . Detection of structural changes of the human brain in longitudinally acquired MR images by deformation field morphometry: methodological analysis, validation and application. Neuroimage 2008;43:269–87

    CrossRefPubMed
32. 32.↵
    1. Sharma S,
    2. Noblet V,
    3. Rousseau F,
    4. et al

    . Evaluation of brain atrophy estimation algorithms using simulated ground-truth data. Med Image Anal 2010;14:373–89

    CrossRefPubMed
33. 33.↵
    1. McDonald WI,
    2. Comtston DAS,
    3. G Edan G,
    4. et al

    . Diagnostic criteria for MS: guidelines from the international panel on the diagnosis of MS. Ann Neurol 2001;50:121–27

    CrossRefPubMedWeb of Science
34. 34.↵
    1. Sled JG,
    2. Zijdenbos AP,
    3. Evans ACA.

    . Nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;1:87–97
35. 35.↵
    1. Boyes RG,
    2. Gunter JL,
    3. Frost C,
    4. et al

    . Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage 2008;39:1752–62

    CrossRefPubMed
36. 36.↵
    1. Pachai C,
    2. Zhu YM,
    3. Guttmann C,
    4. et al

    . Unsupervised and adaptive segmentation of multispectral 3D magnetic resonance images of human brain: a generic approach. Int Conf Med Image Comput Comput Assist Interv 2001;4:1067–74
37. 37.↵
    1. Fox NC,
    2. Freeborough PA.

    . Brain atrophy progression measured from registered serial MRI: validation and application to Alzheimer's disease. J Magn Reson Imaging 1997;7:1069–75

    PubMedWeb of Science
38. 38.↵
    1. Leung KK,
    2. Clarkson MJ,
    3. Bartlett JW,
    4. et al

    . Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. Neuroimage 2010;50:516–23

    CrossRefPubMed
39. 39.↵
    1. Jenkinson M,
    2. Smith S.

    . A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;36:143–56
40. 40.↵
    1. Jenkinson M,
    2. Bannister P,
    3. Brady M,
    4. et al

    . Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:824–41
41. 41.↵
    1. Zhang Y,
    2. Brady M,
    3. 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

    CrossRefPubMedWeb of Science
42. 42.↵
    1. Christensen GE,
    2. Rabbitt RD,
    3. Miller MI.

    . Deformable templates using large deformation kinematics. IEEE Trans Med Imaging 1996;5:1435–47
43. 43.↵
    1. Davatzikos C,
    2. Vaillant M,
    3. Resnick SM,
    4. et al

    . A computerized approach for morphological analysis of the corpus callosum. J Comput Assist Tomogr 1996;20:88–97

    CrossRefPubMedWeb of Science
44. 44.↵
    1. Thompson PM,
    2. Giedd JN,
    3. Woods RP,
    4. et al

    . Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature 2000;404:190–93

    CrossRefPubMedWeb of Science
45. 45.↵
    1. Calmon G,
    2. Roberts N.

    . Automatic measurement of changes in brain volume on consecutive 3D MR images by segmentation propagation. Magn Reson Imaging 2000;18:439–53

    CrossRefPubMedWeb of Science
46. 46.↵
    1. Hua X,
    2. Leow AD,
    3. Parikshak N,
    4. et al

    . Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 2008;43:458–69

    CrossRefPubMedWeb of Science
47. 47.↵
    1. Klein A,
    2. Andersson J,
    3. Ardekani BA,
    4. et al

    . Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 2009;46:786–802

    CrossRefPubMedWeb of Science
48. 48.↵
    1. Vemuri BC,
    2. Ye J,
    3. Chen Y,
    4. et al

    . Image registration via level-set motion: applications to atlas-based segmentation. Med Image Anal 2003;7:1–20

    CrossRefPubMedWeb of Science
49. 49.↵
    1. Wei X,
    2. Guttman CR,
    3. Warfeild SK,
    4. et al

    . Has your patient's multiple sclerosis lesion burden and brain atrophy actually changed? Mult Scler 2004;10:402–06

    Abstract/FREE Full Text
50. 50.↵
    1. Jasperse B,
    2. Valsasina P,
    3. Neacsu V,
    4. et al

    . Intercenter agreement of brain atrophy measurement in multiple sclerosis patients using manually-edited SIENA and SIENAX. J Magn Reson Imaging 2007;26:881–85

    CrossRefPubMedWeb of Science
51. 51.↵
    1. Dickerson BC,
    2. Fenstermacher E,
    3. Salat DH,
    4. et al

    . Detection of cortical thickness correlates of cognitive performance: reliability across MRI scan sessions, scanners, and field strengths. Neuroimage 2008;39:10–18

    CrossRefPubMedWeb of Science
52. 52.↵
    1. Miller DH.

    . Biomarkers and surrogate outcomes in neurodegenerative disease: lessons from multiple sclerosis. Neuroradiology 2004;1:284–94
53. 53.↵
    1. Bermel RA,
    2. Sharma J,
    3. Tjoa CW,
    4. et al

    . A semiautomated mesure of whole-brain atrophy in multiple sclerosis. J Neurol Sci 2003;208:57–65

    CrossRefPubMedWeb of Science