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Textural Characterisation on Small Vessel Disease Regions of Interest

Explore MRI texture analysis for studying normal tissues in stroke patients to understand small vessel disease markers and associations with age and tissue texture variations.

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Textural Characterisation on Small Vessel Disease Regions of Interest

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  1. Textural Characterisation on Regions of Interest: A useful Tool for the Study of Small Vessel Disease Maria del C. Valdés Hernández & Linda Viksne Co-authors: Katie Hoban, Anna K. Heye, Victor-González-Castro & Joanna M. Wardlaw

  2. Background • Stroke is the second largest cause of death worldwide and the commonest cause of disability in adulthood • R.L. Sacco et al. Stroke, 44:2064 –2089, 2013 • MRI studies have focused on assessing pathological characteristics whilst the analysis of apparently normal tissues has been limited to study atrophy • Textural features contain information about the spatial distribution of intensity variations on an image, are independent of tone and invariant under monotonic grey-tone transformations • We developed a template and a framework that uses texture analysis to study normal tissues in brain MRI scans of stroke patients

  3. Methods – Arterial Territories template Probabilistic map of the Brain Arterial Territories (standard space) Stroke lesions from 24 patients with cortical stroke delineated on FLAIR images Brain Arterial Territories delineated considering the extent of the damage https: //ww4.aievolution.com/hbm1401/index.cfm?do=abs. pubSearchAbstracts&hasDocuments=1

  4. Methods – R.O.I. template • Three slices: • Near base of the brain, basal ganglia present • Middle brain, body of lateral ventricles • Near top, centrum semiovale Template: set of seven Analyze object maps (i.e. *.obj files): one for each slice and tissue type

  5. Methods – R.O.I. template usage • Semi-automatic ROI placement: ROIs from the 7 templates (in Analyze object map format) are moved considering the specific anatomy and disease degree • Each ROI: • Area: 12 mm2 approx. • Volume of 45 to 50 mm3 • Placement criteria: • Avoid white matter hyperintensities, lesions, haemorrhages, perivascular spaces, artefacts, inhomogeneities

  6. Methods – Textural features (1) Four second order statistical textural features out of the 14 defined by Haralick and colleagues calculated from a final Grey Level Co-occurrence Matrix determined for each ROI R.M. Haralick et al. IEEE Trans Syst Man Cyb 3(6):610-621, 1973

  7. Methods – The Grey Level Co-occurrence Matrix (GLCM) GLCM 1350 0 1 2 3 0 1 2 3 0 0 1 1 2 2 3 3 Final GLCM d=1; θ=00 d=1; θ=1350

  8. Methods – Textural features (2) Relative contrast between grey and white matter:

  9. Methods – Hypotheses framework (1)

  10. Methods – Hypotheses framework (2)

  11. Experiments - Datasets FLAIR 384×224 acquisition matrix, 28 x 5 mm slices, 1 mm slice gap fspgr 12o 256x192 acquisition matrix, 42 x 4 mm slices, • 42 patients with non disabling ischaemic stroke • 64.9±10.0 years old • 19/42 (45.2%) with stroke subtype lacunar

  12. Experiments – Statistical tests • Mann Whitney U test: 2 Independent groups (non-parametric) (e.g. comparing textural features on the same arterial territory as the stroke with others on a different slice and vascular territory) • Kruskal-Wallis test: k Independent groups (non-parametric) (e.g. textural features measured on different patients or across different slices) • Wilcoxon test: 2 Related groups (non parametric) (e.g. comparing textural features from ROIs on opposites hemispheres of the same slice of a patient) • Friedman’s test: k Related groups (non-parametric) (e.g. textural features measured in ROIs from the same slice of a patient) • Univariate linear regression: to test for associations

  13. Results – Inter-hemispheric textural balance in normal tissues

  14. Results – Spatial dependence of texture on normal tissues from infarcted region

  15. Results – Association between texture on normal tissues and age Entropy values were balanced between hemispheres and equal in some regions Entropy values not significantly associated with age due to prevalence of equal entropy values

  16. Results – Association between texture on normal tissues and SVD markers (1)

  17. Results – Association between texture on normal tissues and SVD markers (2) GMD entropy was not found to be associated with PVS, But WM and GMC entropies could be associated with PVS (were associated in FLAIR) .

  18. Concluding remarks • Integrated template and framework demonstrates heterogeneity of normal tissues in stroke patients in agreement with histopathology • A.A. Gouwet al.  J NeurolNeurosurg Psych. 2010, 82 (2), pp.126 • Local signal intensity variations could be associated with SVD markers • Overall the location of the infarct (i.e. hemisphere and, more precisely, arterial territory) was not found to be associated with normal tissue textural data (intensity, entropy). However, in some cases sample sizes were very small (e.g., only 3 stroke lesions in left PCA territory)

  19. Thanks! Study participants College of Medicine and Veterinary Medicine at the University of Edinburgh, UK and primary study funder:

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