Multimedia Tools and Applications

, Volume 75, Issue 4, pp 2067–2090 | Cite as

A novel dementia diagnosis strategy on arterial spin labeling magnetic resonance images via pixel-wise partial volume correction and ranking

Article

Abstract

Arterial Spin Labeling (ASL) is an emerging magnetic resonance imaging technique attracting increasing attention in dementia diagnosis only beginning from recent years. ASL is capable to provide direct and quantitative measurement of cerebral blood flow (CBF) of scanned patients, so that brain atrophy of demented patients could be revealed by measured low CBF within certain brain regions through ASL. However, partial volume effects (PVE) mainly caused by signal cross-contamination due to pixel heterogeneity and limited spatial resolution of ASL, often prevents CBF from being precisely measured. Inaccurate CBF is prone to mislead and even deteriorate dementia disease diagnosis results, thereafter. In this paper, a novel dementia disease diagnosis strategy based on ASL is proposed for the first time. The diagnosis strategy is composed of two steps: 1) to conduct pixel-wise PVE correction on original ASL images and 2) to predict dementia disease severities based on corrected ASL images via ranking. Extensive experiments and comprehensive statistical analysis are carried out to demonstrate the superiority of the new strategy with comparison to several existing ones. Promising results are reported from the statistical point of view.

Keywords

Magnetic Resonance Image Alzheimer’s Disease Ranking 

Notes

Acknowledgments

The authors would like to acknowledge national grants 61403182, 61363046, 61301194 and 61302121 approved by the National Natural Science Foundation of China, grants 20142BBE50023 and 20142BAB217033 approved by the Jiangxi Provincial Department of Science and Technology, as well as the NWPU grant 3102014JSJ0014 for supporting this study.

References

  1. 1.
    Asllani I, Borogovac A, Brown T (2008) Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. Magn Reson Med 60:1362–1371CrossRefGoogle Scholar
  2. 2.
    Brant-Zawadzki M, Gillan G, Nitz W (1992) MPRAGE: a three-dimensional, t1-weighted, qradient-echo sequence–initial experience in the brain. Radiology 182(3):769–775CrossRefGoogle Scholar
  3. 3.
    Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi M (2007) Forecasting the global burden of alzheimer’s disease. Alzheimers Dement 3(3):186–191CrossRefGoogle Scholar
  4. 4.
    Chen Y, Wolk D, Reddin J, Korczykowski M, Martinez P, Musiek E, Newberg A, Julin P, Arnold S, Greenberg J, Detre J (2011) Voxel-level comparison of arterial spin-labeling perfusion mri and fdg-pet in alzheimer disease. Neurology 77(2):1977–1985CrossRefGoogle Scholar
  5. 5.
    Du Y, Tsui B, Frey E (2005) Partial volume effect compensation for quantitative brain spect imaging. IEEE Trans Med Imaging 24(8):969–976CrossRefGoogle Scholar
  6. 6.
    Erlandsson K, Buvat I, Pretorius H, Thomas B, Hutton B (2012) A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol 57(21):119–159CrossRefGoogle Scholar
  7. 7.
    FMRIB Software Library (FSL) toolbox. http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/
  8. 8.
    Folstein M, Folstein S, McHung P (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198CrossRefGoogle Scholar
  9. 9.
    Galton C, Patterson K, Graham K, Lambon-Ralph M, Williams G, Antoun N, Sahakian B, Hodges J (2001) Differing patterns of temporal atrophy in alzheimer’s disease and semantic dementia. Neurology 57(2):216–225CrossRefGoogle Scholar
  10. 10.
    Golay X, Hendrikse J, Lim T (2004) Perfusion imaging using arterial spin labeling. Top Magn Reson Imaging: TMRI 15(1):10–27CrossRefGoogle Scholar
  11. 11.
    Gold G, Eniko K, Herrmann F, Canuto A, Hof P, Jean-Pierre M, Constantin B, Giannakopoulos P (2005) Cognitive consequences of thalamic, basal ganglia, and deep white matter lacunes in brain aging and dementia. Stroke 36(6):1184–1188CrossRefGoogle Scholar
  12. 12.
    Goldstein T, Osher S (2008) The Split Bregman Method for L1 Regularized Problems. UCLA CAM report 08–29:1–21Google Scholar
  13. 13.
    Gunn R, Gunn S, Turkheimer F, Aston J, Cunningham V (2002) Positron emission tomography compartmental models: a basis pursuit strategy for kinetic modeling. J Cereb Blood Flow Metab 22:1425–1439CrossRefGoogle Scholar
  14. 14.
    Individual Brain Atlases using Statistical Parametric Mapping (IBA-SPM) Software. http://www.thomaskoenig.ch/Lester/ibaspm.htm
  15. 15.
    Jarvelin K, Kekalainen J (2000) IR Evaluation Methods for Retrieving Highly Relevant Documents. In: Proceedings ACM Special Interest Group Inf Retrieval (SIGIR), pp. 41–48Google Scholar
  16. 16.
    Joachims T SVM light - An Implementation of Support Vector Machine in C. URL http://svmlight.joachims.org
  17. 17.
    Johnson N, Jahng G, Weiner M, Miller B, Chui H, Jagust W, Gorno-Tempini M, Schuff N (2005) Pattern of cerebral hypoperfusion in alzheimer disease and mild cognitive impairment measured with arterial spin-labeling mr imaging: initial experience. Radiology 234:851–859CrossRefGoogle Scholar
  18. 18.
    Keerthi S (2002) Efficient Tuning of SVM Hyperparameters using Radius/Margin Bound and Iterative Algorithms. IEEE Trans Neural Netw 13(5):1225–1229CrossRefGoogle Scholar
  19. 19.
    Laakso M, Partanen K, Riekkinen P, Lehtovirta M, Helkala E, Hallikainen M, Hanninen T, Vainio P, Soininen H (1996) Hippocampal volumes in alzheimer’s disease, parkinson’s disease with and without dementia, and in vascular dementia an mri study. Neurology 46(3):678–681CrossRefGoogle Scholar
  20. 20.
    Liu M, Zhang D, Shen D (2012) Ensemble sparse classification of alzheimers disease. NeuroImage 60(2):1106–1116CrossRefMathSciNetGoogle Scholar
  21. 21.
    Mahendra B (1987) A pathography of dementia. Dementia 1:189–202CrossRefGoogle Scholar
  22. 22.
    Malpass K, Disease Alzheimer (2012) Arterial Spin-Labeled MRI for Diagnosis and Monitoring of AD. Nat Rev Neurol 8(3):847–849Google Scholar
  23. 23.
    Mioshi E, Dawson K, Mitchell J, Arnold R, Hodges J (2006) The addenbrooke’s cognitive examination revised (ace-r): a brief cognitive test battery for dementia screening. Int J Geriatr Psychiatr 21(11):1078–1085CrossRefGoogle Scholar
  24. 24.
    Murphy K, Brunberg J, Cohan R (1996) Adverse reactions to gadolinium contrast media: a review of 36 cases. Am J Roentgenol 167(4):847–849CrossRefGoogle Scholar
  25. 25.
    Musiek E, Chen Y, Korczykowski M, Saboury B, Martinez P, Reddin J, Alavi A, Kimberg D, Wolk D, Julin P, Newberg A, Arnold S, Detre J (2012) Direct comparison of fluorodeoxyglucose positron emission tomography and arterial spin labeling magnetic resonance imaging in alzheimer’s disease. Alzheimers Dement 8(1):51–59CrossRefGoogle Scholar
  26. 26.
    Parkes L, Rashid W, Chard D, Tofts P (2004) Normal cerebral perfusion measurements using arterial spin labeling: reproducibility, stability, and age and gender effects. Magn Reson Med 51:736–743CrossRefGoogle Scholar
  27. 27.
  28. 28.
    Rice J (2007) Mathematical Statistics and Data Dnalysis, second edition. Duxbury PressGoogle Scholar
  29. 29.
    Statistical Parametric Mapping (SPM) toolbox. http://www.fil.ion.ucl.ac.uk/spm/
  30. 30.
  31. 31.
    Wang Z, Das S, Xie S, Arnold S, Detre J (2013) Arterial Spin Labeled MRI in Prodromal Alzheimer’s Disease: A Multi-site Study. NeuroImage: Clin 2:630–636CrossRefGoogle Scholar
  32. 32.
    Wee C, Yap P, Shen D (2013) Prediction of alzheimers disease and mild cognitive impairment using cortical morphological patterns. Hum Brain Mapp 34(12):3411–3425CrossRefGoogle Scholar
  33. 33.
    World Health Organization, The Top 10 Causes of Death. http://www.who.int/mediacentre/factsheets/fs310/en/index2.html
  34. 34.
    Wright M (2004) The interior-point revolution in optimization: history, recent developments, and lasting consequences. Bull Am Math Soc 42(39):1–16Google Scholar
  35. 35.
    Zhou L, Wang Y, Li Y, Yap P, Shen D (2011) Hierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures. PLoS Comput Biol 6(7):e21935Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Information EngineeringNanchang UniversityNanchangChina
  2. 2.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  3. 3.INCIDE CenterUniversity of KonstanzKonstanzGermany
  4. 4.School of Software EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations