Multi-contrast unbiased MRI atlas of a Parkinson’s disease population

  • Yiming Xiao
  • Vladimir Fonov
  • Silvain Bériault
  • Fahd Al Subaie
  • M. Mallar Chakravarty
  • Abbas F. Sadikot
  • G. Bruce Pike
  • D. Louis Collins
Original Article

Abstract

Purpose

Parkinson’s disease (PD) is the second leading neurodegenerative disease after Alzheimer’s disease. In PD research and its surgical treatment, such as deep brain stimulation (DBS), anatomical structural identification and references for spatial normalization are essential, and human brain atlases/templates are proven highly instrumental. However, two shortcomings affect current templates used for PD. First, many templates are derived from a single healthy subject that is not sufficiently representative of the PD-population anatomy. This may result in suboptimal surgical plans for DBS surgery and biased analysis for morphological studies. Second, commonly used mono-contrast templates lack sufficient image contrast for some subcortical structures (i.e., subthalamic nucleus) and biochemical information (i.e., iron content), a valuable feature in current PD research.

Methods

We employed a novel T1–T2* fusion MRI that visualizes both cortical and subcortical structures to drive groupwise registration to create co-registered multi-contrast (T1w, T2*w, T1–T2* fusion, phase, and an R2* map) unbiased templates from 15 PD patients, and a high-resolution histology-derived 3D atlas is co-registered. For validation, these templates are compared against the Colin27 template for landmark registration and midbrain nuclei segmentation.

Results

While the T1w, T2*w, and T1–T2* fusion templates provide excellent anatomical details for both cortical and subcortical structures, the phase and R2* map contain the biochemical features. By one-way ANOVA tests, our templates significantly (\(p<0.05\)) outperform the Colin27 template in the registration-based tasks.

Conclusion

The proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.

Keywords

Parkinson’s disease Multi-echo FLASH Unbiased brain atlas Multi-contrast  Susceptibility-weighted imaging 

References

  1. 1.
    Rajput AH, Offord KP, Beard CM, Kurland LT (1984) Epidemiology of parkinsonism: incidence, classification, and mortality. Ann Neurol 16(3):278–282CrossRefPubMedGoogle Scholar
  2. 2.
    Kleiner-Fisman G, Fisman DN, Sime E, Saint-Cyr JA, Lozano AM, Lang AE (2003) Long-term follow up of bilateral deep brain stimulation of the subthalamic nucleus in patients with advanced Parkinson disease. J Neurosurg 99(3):489–495CrossRefPubMedGoogle Scholar
  3. 3.
    Vingerhoets FJ, Villemure JG, Temperli P, Pollo C, Pralong E, Ghika J (2002) Subthalamic DBS replaces levodopa in Parkinson’s disease: two-year follow-up. Neurology 58(3):396–401CrossRefPubMedGoogle Scholar
  4. 4.
    Schaltenbrand G, Wahren W (1977) Atlas for stereotaxy of the human brain. 2nd, rev. and enl. edn. Year Book Medical Publishers, ChicagoGoogle Scholar
  5. 5.
    Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging. G. Thieme; New YorkGoogle Scholar
  6. 6.
    Lalys F, Haegelen C, Ferre JC, El-Ganaoui O, Jannin P (2010) Construction and assessment of a 3-T MRI brain template. Neuroimage 49(1):345–354CrossRefPubMedGoogle Scholar
  7. 7.
    Bardinet E, Bhattacharjee M, Dormont D, Pidoux B, Malandain G, Schupbach M, Ayache N, Cornu P, Agid Y, Yelnik J (2009) A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease. J Neurosurg 110(2):208–219CrossRefPubMedGoogle Scholar
  8. 8.
    Chakravarty MM, Sadikot AF, Germann J, Bertrand G, Collins DL (2008) Towards a validation of atlas warping techniques. Med Image Anal 12(6):713–726CrossRefPubMedGoogle Scholar
  9. 9.
    Haegelen C, Coupe P, Fonov V, Guizard N, Jannin P, Morandi X, Collins DL (2013) Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease. Int J Comput Assist Radiol Surg 8(1):99–110Google Scholar
  10. 10.
    Murphy DGM, Decarli C, Schapiro MB, Rapoport SI, Horwitz B (1992) Age-related differences in volumes of subcortical nuclei, brain matter, and cerebrospinal-fluid in healthy-men as measured with magnetic-resonance-imaging. Arch Neurol Chicago 49(8):839–845CrossRefPubMedGoogle Scholar
  11. 11.
    Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, Fox NC (2003) A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol Chicago 60(7):989–994CrossRefPubMedGoogle Scholar
  12. 12.
    Hutchinson M, Raff U (2000) Structural changes of the substantia nigra in Parkinson’s disease as revealed by MR imaging. Am J Neuroradiol 21(4):697–701PubMedGoogle Scholar
  13. 13.
    Nagano-Saito A, Washimi Y, Arahata Y, Kachi T, Lerch JP, Evans AC, Dagher A, Ito K (2005) Cerebral atrophy and its relation to cognitive impairment in Parkinson disease. Neurology 64(2):224–229CrossRefPubMedGoogle Scholar
  14. 14.
    Montgomery EB (2010) Deep brain stimulation programming: principles and practice. Oxford University Press, OxfordGoogle Scholar
  15. 15.
    Chakravarty MM, Sadikot AF, Germann J, Hellier P, Bertrand G, Collins DL (2009) Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications. Hum Brain Mapp 30(11):3574–3595CrossRefPubMedGoogle Scholar
  16. 16.
    Richter EO, Hoque T, Halliday W, Lozano AM, Saint-Cyr JA (2004) Determining the position and size of the subthalamic nucleus based on magnetic resonance imaging results in patients with advanced Parkinson disease. J Neurosurg 100(3):541–546CrossRefPubMedGoogle Scholar
  17. 17.
    Duval C, Panisset M, Strafella AP, Sadikot AF (2006) The impact of ventrolateral thalamotomy on tremor and voluntary motor behavior in patients with Parkinson’s disease. Exp Brain Res 170(2):160–171CrossRefPubMedGoogle Scholar
  18. 18.
    Lehericy S, Bardinet E, Tremblay L, Van de Moortele PF, Pochon JB, Dormont D, Kim DS, Yelnik J, Ugurbil K (2006) Motor control in basal ganglia circuits using fMRI and brain atlas approaches. Cereb Cortex 16(2):149–161CrossRefPubMedGoogle Scholar
  19. 19.
    Burton EJ, McKeith IG, Burn DJ, Williams ED, O’Brien JT (2004) Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 127(Pt 4):791–800CrossRefPubMedGoogle Scholar
  20. 20.
    Summerfield C, Junque C, Tolosa E, Salgado-Pineda P, Gomez-Anson B, Marti MJ, Pastor P, Ramirez-Ruiz B, Mercader J (2005) Structural brain changes in Parkinson disease with dementia: a voxel-based morphometry study. Arch Neurol Chicago 62(2):281–285CrossRefPubMedGoogle Scholar
  21. 21.
    Sudhyadhom A, Haq IU, Foote KD, Okun MS, Bova FJ (2009) A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: the fast gray matter acquisition T1 inversion recovery (FGATIR). Neuroimage 47(Suppl 2):T44–T52Google Scholar
  22. 22.
    Xiao Y, Bailey L, Chakravarty MM, Beriault S, Sadikot AF, Pike GB, Collins DL (2012) Atlas-based segmentation of the subthalamic nucleus, red nucleus, and substantia nigra for deep brain stimulation by incorporating multiple MRI contrasts. In: Information processing in computer-assisted interventions (IPCAI), pp 135–145Google Scholar
  23. 23.
    Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tande D, Karachi C, Ayache N, Cornu P, Agid Y (2007) A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 34(2):618–638CrossRefPubMedGoogle Scholar
  24. 24.
    Pallavaram S, Dawant BM, Koyama T, Yu H, Neimat J, Konrad PE, D’Haese PF (2009) Validation of a fully automatic method for the routine selection of the anterior and posterior commissures in magnetic resonance images. Stereot Funct Neuros 87(3):148–154CrossRefGoogle Scholar
  25. 25.
    Chakravarty MM, Bertrand G, Hodge CP, Sadikot AF, Collins DL (2006) The creation of a brain atlas for image guided neurosurgery using serial histological data. Neuroimage 30(2):359–376CrossRefPubMedGoogle Scholar
  26. 26.
    Guimond A, Meunier J, Thirion JP (1998) Automatic computation of average brain models. Lect Notes Comput Sc 1496:631–640CrossRefGoogle Scholar
  27. 27.
    Guimond A, Meunier J, Thirion JP (2000) Average brain models: a convergence study. Comput Vis Image Und 77(2):192–210CrossRefGoogle Scholar
  28. 28.
    Bhatia KK, Aljabar P, Boardman JP, Srinivasan L, Murgasova M, Counsell SJ, Rutherford MA, Hajnal J, Edwards AD, Rueckert D (2007) Groupwise combined segmentation and registration for atlas construction. Med Image Comput Comput Assist Interv 10(Pt 1):532–540PubMedGoogle Scholar
  29. 29.
    Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1):313–327CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Joshi S, Davis B, Jomier M, Gerig G (2004) Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23(Suppl 1):S151–S160CrossRefPubMedGoogle Scholar
  31. 31.
    Lorenzen P, Prastawa M, Davis B, Gerig G, Bullitt E, Joshi S (2006) Multi-modal image set registration and atlas formation. Med Image Anal 10(3):440–451CrossRefPubMedCentralPubMedGoogle Scholar
  32. 32.
    Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008) Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3):1064–1080CrossRefPubMedCentralPubMedGoogle Scholar
  33. 33.
    Wang Q, Seghers D, D’Agostino E, Maes F, Vandermeulen D, Suetens P, Hammers A (2005) Construction and validation of mean shape atlas templates for atlas-based brain image segmentation. Inf Process Med Imaging 19:689–700CrossRefPubMedGoogle Scholar
  34. 34.
    O’Gorman RL, Shmueli K, Ashkan K, Samuel M, Lythgoe DJ, Shahidiani A, Wastling SJ, Footman M, Selway RP, Jarosz J (2011) Optimal MRI methods for direct stereotactic targeting of the subthalamic nucleus and globus pallidus. Eur Radiol 21(1):130–136Google Scholar
  35. 35.
    Schenck JF, Zimmerman EA (2004) High-field magnetic resonance imaging of brain iron: birth of a biomarker? NMR Biomed 17(7):433–445CrossRefPubMedGoogle Scholar
  36. 36.
    Boelmans K, Holst B, Hackius M, Finsterbusch J, Gerloff C, Fiehler J, Munchau A (2012) Brain iron deposition fingerprints in Parkinson’s disease and progressive supranuclear palsy. Mov Disord 27(3):421–427CrossRefPubMedGoogle Scholar
  37. 37.
    Gorell JM, Ordidge RJ, Brown GG, Deniau JC, Buderer NM, Helpern JA (1995) Increased iron-related MRI contrast in the substantia nigra in Parkinson’s disease. Neurology 45(6):1138–1143CrossRefPubMedGoogle Scholar
  38. 38.
    Ogg RJ, Langston JW, Haacke EM, Steen RG, Taylor JS (1999) The correlation between phase shifts in gradient-echo MR images and regional brain iron concentration. Magn Reson Imaging 17(8):1141–1148CrossRefPubMedGoogle Scholar
  39. 39.
    Peran P, Hagberg G, Luccichenti G, Cherubini A, Brainovich V, Celsis P, Caltagirone C, Sabatini U (2007) Voxel-based analysis of R2* maps in the healthy human brain. J Magn Reson Imaging 26(6):1413–1420 Google Scholar
  40. 40.
    Forkert ND, Schmidt-Richberg A, Treszl A, Hilgetag C, Fiehler J, Munchau A, Handels H, Boelmans K (2013) Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2’ MR imaging. Methods Inf Med 52(2):128–136CrossRefPubMedGoogle Scholar
  41. 41.
    Xiao Y, Beriault S, Pike GB, Collins DL (2012b) Multicontrast multiecho FLASH MRI for targeting the subthalamic nucleus. Magn Reson Imaging 30(5):627–640CrossRefPubMedGoogle Scholar
  42. 42.
    Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C (2008) An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27(4):425–441CrossRefPubMedCentralPubMedGoogle Scholar
  43. 43.
    Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97CrossRefPubMedGoogle Scholar
  44. 44.
    Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155CrossRefPubMedGoogle Scholar
  45. 45.
    Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18(2):192–205CrossRefPubMedGoogle Scholar
  46. 46.
    Miller M, Banerjee A, Christensen G, Joshi S, Khaneja N, Grenander U, Matejic L (1997) Statistical methods in computational anatomy. Stat Methods Med Res 6(3):267–299CrossRefPubMedGoogle Scholar
  47. 47.
    Nyul LG, Udupa JK (1999) On standardizing the MR image intensity scale. Magn Reson Med 42(6):1072–1081CrossRefPubMedGoogle Scholar
  48. 48.
    Beriault S, Subaie FA, Collins DL, Sadikot AF, Pike GB (2012) A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7(5):687–704CrossRefPubMedGoogle Scholar
  49. 49.
    Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC (1998) Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 22(2):324–333CrossRefPubMedGoogle Scholar
  50. 50.
    Ardekani BA, Guckemus S, Bachman A, Hoptman MJ, Wojtaszek M, Nierenberg J (2005) Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans. J Neurosci Methods 142(1):67–76CrossRefPubMedGoogle Scholar
  51. 51.
    Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41CrossRefPubMedCentralPubMedGoogle Scholar
  52. 52.
    Guizzard N, Fonov V, Collins DL (2009) Symmetric optimization scheme versus constrained symmetrization for non-linear registrations. In: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis (validation and methodological issues)”Google Scholar
  53. 53.
    Volz S, Hattingen E, Preibisch C, Gasser T, Deichmann R (2009) Reduction of susceptibility-induced signal losses in multi-gradient-echo images: application to improved visualization of the subthalamic nucleus. Neuroimage 45(4):1135–1143CrossRefPubMedGoogle Scholar
  54. 54.
    Guo T, Finnis KW, Parrent AG, Peters TM (2005) Development and application of functional databases for planning deep-brain neurosurgical procedures. Med Image Comput Comput Assist Interv 8(Pt 1):835–842PubMedGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  • Yiming Xiao
    • 1
  • Vladimir Fonov
    • 1
  • Silvain Bériault
    • 1
  • Fahd Al Subaie
    • 2
  • M. Mallar Chakravarty
    • 3
    • 4
  • Abbas F. Sadikot
    • 2
  • G. Bruce Pike
    • 5
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging CenterMontreal Neurological Institute and HospitalMontrealCanada
  2. 2.Division of neurosurgeryMcGill UniversityMontrealCanada
  3. 3.Kimel Family Translational Imaging Genetics LaboratoryThe Centre for Addiction and Mental HealthTorontoCanada
  4. 4.Department of Psychiatry, Institute of Biomaterials and Biomedical EngineeringUniversity of TorontoTorontoCanada
  5. 5.Department of Radiology, Hotchkiss Brain InstituteUniversity of CalgaryCalgaryCanada

Personalised recommendations