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P025 MPRAGE Pre-Contrast

This document details innovative methods of MRI segmentation applied to a patient with Secondary Progressive Multiple Sclerosis (SPMS). Utilizing an inverse nonlinear warp from MNI to SPGR FA and MPRAGE spaces, significant white matter demyelination and lesion identification was achieved, despite some limitations with z-score thresholding. Various segmentation techniques (SPGR, FLAIR, T2-PD) were assessed for their effectiveness in capturing cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) lesions. Comprehensive analysis highlights best practices and areas for improvement.

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P025 MPRAGE Pre-Contrast

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  1. P025 MPRAGE Pre-Contrast

  2. P025 MPRAGE w/ Z-Score < -4

  3. Notes • This was accomplished by doing an inverse nonlinear warp from MNI to the SPGR FA 18, then to the MPRAGE space with a linear transform (ideally these transforms should be combined) • Images are larger than shown, zoom in for more detail • Not all lesions are found by the z-score thresholding but a significant amount are • Large volumes of white matter are found to be significantly demyelinated: perhaps DAWM or actually revealing the “invisible disease”? • This was one the patients with a larger amount of low MWF volume, EDSS 5.0 SPMS

  4. Another Look at Multi-Channel Segmentation with FSL FAST

  5. MPRAGE • Same slices as shown in the z-score slides • Lesions of interest circled

  6. SPGR 3 class-CSF • Generally good CSF segmentation • Does not capture most lesions

  7. SPGR 3 class-GM • Includes some or all of lesions

  8. SPGR 3 class-WM • Matches nicely with the MPRAGE scan • Partially includes lesions

  9. SPGR 4 class-CSF • Generally worse than 3 class CSF

  10. SPGR 4 class-GM • More conservative estimate of GM, much fewer lesions included

  11. SPGR 4 class-More GM • Deep GM • Includes many of the lesions

  12. SPGR 4 class-WM • Does well at excluding most focal lesions but appears to be some partially including some • More conservative

  13. SPGR-FLAIR 3 class-CSF • Grabs most lesions • Unfortunately mis-classifies GM too

  14. SPGR-FLAIR 3 class-GM • Includes many regions previously seen as WM

  15. SPGR-FLAIR 3 class-WM • Good exclusion of lesions identified by z-score • Does poorly at WM and GM segmentation, misses some WM

  16. SPGR-FLAIR 4 class-CSF • Generally worse than 3 class CSF also • Chokes back mask too far

  17. SPGR-FLAIR 4 class-GM • Still misidentifies a lot of WM as GM • Catches edges of our lesions of interest

  18. SPGR-FLAIR 4 class-More GM • Again mis-includes a lot of WM

  19. SPGR-FLAIR 4 class-WM • Avoids lesions but also misses a lot of regular WM since those are misidentified as GM

  20. SPGR-T2-PD 3 class-CSF • Decent at outside brain CSF, though catches some WM • Does not get any CSF inside brain

  21. SPGR-T2-PD 3 class-GM • Captures our lesions of interest • Gets ventricle CSF • Not as good as SPGR 3 class

  22. SPGR-T2-PD 3 class-WM • Overly greedy WM • Misses focal lesions but gets GM

  23. SPGR-T2-PD 4 class-CSF • Same problems as with SPGR-T2-PD CSF segmentation

  24. SPGR-T2-PD 4 class-GM • GM + inner CSF • Catches darker lesions • Pretty poor, also gets WM

  25. SPGR-T2-PD 4 class-More GM • Catches some lesions but overall pretty garbagey • Doesn’t really correspond to a distinct tissue class

  26. SPGR-T2-PD 4 class-WM • A conservative estimate • Circled region shows possible inclusion of GM and missing of brain stem

  27. Best CSF • SPGR 3 class (includes some lesions) • SPGR-FLAIR 3 class (includes all lesions) • SPGR-T2-PD 3 class (useful for out of brain CSF, does not include lesions)

  28. Best WM • SPGR 3 class (best anatomically) • SPGR 4 class (conservative) • SPGR-FLAIR 4 class (more lesion exclusion)

  29. Best GM • SPGR 3 class (GM+lesions) • SPGR 4 class (deep GM+lesions)

  30. Best Lesion • SPGR 3 class (GM+lesions) • SPGR-FLAIR 3 class (CSF missing some lesions)

  31. Coming Soon • Segmentation with a priori maps • Ideas about how to combine maps to produce NAWM, NAGM, and lesion only masks

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