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Quality control for structural and functional MRI

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Publicado el

Part of Stanford fMRI BootCamp.

Lecture given together with Oscar Esteban.

Publicado en: Educación
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Quality control for structural and functional MRI

  1. 1. How much noise is too much? Quality control for structural and functional MRI
  2. 2. Why bother?
  3. 3. Goals of quality control Deciding which data to include in your study and which to reject. Deciding on using a public dataset (is it appropriate for my design/study?) Diagnosing fixable problems with data acquisition process: Types of sequences Scanner malfunctions Head padding Participant instructions
  4. 4. When to perform quality control? Early! As soon as you get data: Helps fix problems with the scanner before the next subject Allows to recruit extra subjects if you know some data needs to be discarded QC (when done with the right tools) takes very little effort - but can save a lot of money and time in the long run!
  5. 5. Basics: data consistency Check if: Scans for a new subjects have the same (prescribed) parameters: Resolution Field of view Number of timepoints (fMRI) Each subject has all of the scans
  6. 6. Basics: data consistency MRIQC Bids-validator (
  7. 7. Motion in structural scans (T1 weighted) Picture courtesy of @le_feufollet
  8. 8. Motion in structural scans (T1 weighted) A lot of motion Some motion No motion
  9. 9. Gibbs ringing
  10. 10. Wrap around
  11. 11. Ghosting (Nyquist N/2 Ghosts) Mean image Stddev image
  12. 12. Ghosting (chemical shift)
  13. 13. Spikes t=0 t=1 t=2 Air mask
  14. 14. K-space (the final frontier)
  15. 15. K-space (the final frontier)
  16. 16. Spikes
  17. 17. Spin history effects
  18. 18. Motion and spin history effects
  19. 19. Motion and spin history effects
  20. 20. Motion and spin history effects
  21. 21. Motion and spin history effects
  22. 22. Motion and spin history effects
  23. 23. QC metrics Noise measurement Signal-to-noise ratio (SNR) - higher is better Contrast-to-noise ration (CNR) - higher is better Sharpness (full-width half maximum estimations) - smaller FWHM is better Goodness of fit of a noise model into the noise in the background (QI2) - lower is better Coefficient of Joint Variation (CJV) - lower is better Information theory Foreground-Background Energy Ratio (FBER) - higher is better Entropy Focus Criterion (EFC) - lower is better Artifacts Segmentation using mathematical morphology (QI1) - lower is better
  24. 24. QC metrics Noise measurement: SNR, tSNR, temporal standard deviation Information theory: EFC, FBER Confounds and artifacts: Framewise Displacement (FD) - lower is better (Standardized) DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels) - lower is better Ghost-to-Signal ratio (GSR) - lower is better Global correlation (GCOR) - lower is better Energy of spectrum (ES) - lower is better AFNI’s outlier detection and quality indexes
  25. 25. More thoughts about QC There are not strict rules which data to discard Some artefacts and distortions can be recovered by smart algorithms QC can help you decide results from which subjects you should interrogate more closely
  26. 26. Crowdsourcing artefacts What happens when you ask Twitter for help...
  27. 27. Example cases (with and without artifacts) along with QC reports All reports generated using MRIQC (