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Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI

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Abstract

Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ.

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References

  1. Detre JA et al (1992) Perfusion imaging. Magn Reson Med 23(1):37–45

    Article  PubMed  CAS  Google Scholar 

  2. Williams DS et al (1992) Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci 89(1):212–216

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Dolui S et al (2016) Structural correlation-based outlier rejection (SCORE) algorithm for arterial spin labeling time series. J Magn Reson Imaging 45(6):1786–1797

  4. Chen JJ, Jann K, Wang DJ (2015) Characterizing resting-state brain function using arterial spin labeling. Brain connectivity 5(9):527–542

    Article  PubMed  PubMed Central  Google Scholar 

  5. Liu X et al (2016) Three-dimensional hemodynamics analysis of the circle of Willis in the patient-specific nonintegral arterial structures. Biomech Model Mechanobiol 15(6):1439–1456

    Article  PubMed  Google Scholar 

  6. Mohb Adib MA et al (2017) Minimizing the blood velocity differences between phase-contrast magnetic resonance imaging and computational fluid dynamics simulation in cerebral arteries and aneurysms. Med Biol Eng Comput, p. 1–15

  7. Alsop DC et al (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 73(1):102–116

    Article  PubMed  Google Scholar 

  8. Wong EC (1999) Potential and pitfalls of arterial spin labeling based perfusion imaging techniques for MRI, in Functional MRI. C.T.W.M.a.P.A. Bandettini (Ed) New York. p. 63–69

  9. Wang Z et al (2008) Empirical optimization of ASL data analysis using an ASL data processing toolbox: ASLtbx. Magn Reson Imaging 26(2):261–269

    Article  PubMed  Google Scholar 

  10. Wang Z (2012) Improving cerebral blood flow quantification for arterial spin labeled perfusion MRI by removing residual motion artifacts and global signal fluctuations. Magn Reson Imaging 30(10):1409–1415

    Article  PubMed  PubMed Central  Google Scholar 

  11. Restom K, Behzadi Y, Liu TT (2006) Physiological noise reduction for arterial spin labeling functional MRI. NeuroImage 31(3):1104–1115

    Article  PubMed  Google Scholar 

  12. Behzadi Y et al (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37(1):90–101

    Article  PubMed  PubMed Central  Google Scholar 

  13. Power JD et al (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59(3):2142–2154

    Article  PubMed  Google Scholar 

  14. Fang R, Huang J, Luh W-M (2015) A spatio-temporal low-rank total variation approach for denoising arterial spin labeling MRI data. IEEE 12th International Symposium on Biomedical Imaging (ISBI) 2015:498–502

    Article  Google Scholar 

  15. Glover GH, Li TQ, Ress D (2000) Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44(1):162–167

    Article  PubMed  CAS  Google Scholar 

  16. Bibic A et al (2010) Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing. MAGMA 23(3):125–137

    Article  PubMed  Google Scholar 

  17. Wells JA et al (2010) Reduction of errors in ASL cerebral perfusion and arterial transit time maps using image de-noising. Magn Reson Med 64(3):715–724

    Article  PubMed  Google Scholar 

  18. Wang Z (2014) Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI. Hum Brain Mapp 35(7):2869–2875

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cox DD, Savoy RL (2003) Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19(2):261–270

    Article  PubMed  Google Scholar 

  20. LaConte S et al (2005) Support vector machines for temporal classification of block design fMRI data. NeuroImage 26(2):317–329

    Article  PubMed  Google Scholar 

  21. Mourão-Miranda J et al (2005) Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28(4):980–995

    Article  PubMed  Google Scholar 

  22. Mourão-Miranda J, Fristonb KJ, Brammer M (2007) Dynamic discrimination analysis: a spatial-temporal SVM. NeuroImage 36(1):88–99

    Article  PubMed  Google Scholar 

  23. Mourão-Miranda J et al (2006) The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data. NeuroImage 33:1055–1065

    Article  PubMed  Google Scholar 

  24. Fan Y et al (2007) Multivariate examination of brain abnormality using both structural and functional MRI. NeuroImage 36:1189–1199

    Article  PubMed  Google Scholar 

  25. Wang Z et al (2007) Support vector machine learning-based fMRI data group analysis. NeuroImage 36(4):1139–1151

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wang Z (2009) A hybrid SVM-GLM approach for fMRI data analysis. NeuroImage 46(3):608–615

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang Z et al (2008) Assessment of functional development in normal infant brain using arterial spin labeled perfusion MRI. NeuroImage 39(3):973–978

    Article  PubMed  Google Scholar 

  28. Mirman D et al (2015) Neural organization of spoken language revealed by lesion-symptom mapping. Nat Commun 6:6762

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Mirman D et al (2015) The ins and outs of meaning: behavioral and neuroanatomical dissociation of semantically-driven word retrieval and multimodal semantic recognition in aphasia. Neuropsychologia 76:208–219

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhang Y et al (2014) Multivariate lesion-symptom mapping using support vector regression. Hum Brain Mapp 35(12):5861–5876

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kim KH, Bang SW, Kim SR (2004) Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 42(3):419–427

    Article  PubMed  CAS  Google Scholar 

  32. Kumar S et al (2015) Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism. Med Biol Eng Comput 53(7):609–622

    Article  PubMed  Google Scholar 

  33. Dai W et al (2008) Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magn Reson Med 60(6):1488–1497

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3):27

    Google Scholar 

  35. Wang J et al (2003) Arterial spin labeling perfusion fMRI with very low task frequency. Magn Reson Med 49(5):796–802

    Article  PubMed  Google Scholar 

  36. Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. In 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE

  37. Vorontsov E et al (2017) Metastatic liver tumour segmentation with a neural network-guided 3D deformable model. Med Biol Eng Comput 55(1):127–139

    Article  PubMed  Google Scholar 

  38. Zhu H et al (2017) Metric learning for multi-atlas based segmentation of hippocampus. Neuroinformatics 15(1):41–50

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yang J et al (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  PubMed  Google Scholar 

  40. Rueda A, Malpica N, Romero E (2013) Single-image super-resolution of brain MR images using overcomplete dictionaries. Med Image Anal 17(1):113–132

    Article  PubMed  Google Scholar 

  41. Aguirre G et al (2002) Experimental design and the relative sensitivity of BOLD and perfusion fMRI. NeuroImage 15(3):488–500

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 61602307, 61671198), Natural Science Foundation of Zhejiang Province Grant LZ15H180001, the Youth 1000 Talent Program of China, Hangzhou Qianjiang Endowed Professor Program, and Hangzhou Innovation Seed Fund.

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Correspondence to Ze Wang.

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Zhu, H., He, G. & Wang, Z. Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI. Med Biol Eng Comput 56, 951–956 (2018). https://doi.org/10.1007/s11517-017-1735-6

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  • DOI: https://doi.org/10.1007/s11517-017-1735-6

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