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Accelerated DESPOT1

Accelerated DESPOT1. Jason Su Oct. 10, 2011. DISCOPOT. View sharing of k-space between a sequence of angles Fully sampled center of k-space, under sampled outer Outer k-space pattern is pseudo-random but complementary with shared angles Mixing scheme:

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Accelerated DESPOT1

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  1. Accelerated DESPOT1 Jason Su Oct. 10, 2011

  2. DISCOPOT • View sharing of k-space between a sequence of angles • Fully sampled center of k-space, under sampled outer • Outer k-space pattern is pseudo-random but complementary with shared angles • Mixing scheme: • AB1.*fa_{i} + B2.*fa_{i-1} + B3.*fa_{i+1} • Edge cases are slightly different • Tested on raw SPGR P-file data with fa1-13 • Many angles collected with the goal of mcDESPOT in mind

  3. DISCOPOT Sampling

  4. DISCOPOT – fa1

  5. Errors Due to offline.recon

  6. DISCOPOT – fa8

  7. DISCOPOT – T1

  8. Why are fa1&2 the worst?

  9. Solutions • Use the signal equation to scale the mixed k-space data • Can calculate scale factors a priori assuming a uniform T1 • Can scale by the ratio of energy in the centers of k-space between images • What errors do we expect we use a constant scale factor? • At higher flips, the SPGR curves are nearly parallel regardless of T1, this means that a constant scale factor should work very well • At lower flips, performance will be worse • Consider the SPGR signal as a time signal, the lower flips is where things diverge and we get different behavior with T1. After the Ernst angle, the signal decays predictably

  10. DISCOPOT, use full fa1 – T1

  11. DISCOPOT, a priori scaling – fa1

  12. DISCOPOT, energy scaling – fa1

  13. DISCOPOT, energy scaling – fa8

  14. DISCOPOT, energy scaling – T1

  15. Comments • fa1 makes a greater max error than fa8 but its distribution is tighter overall, standard deviation is lower • Perhaps central k-space energy is not a good measure at higher flips due to higher contrast • Errors are worst around CSF: periphery and ventricles

  16. LCAMP • Compressed sensing reconstruction • Same undersampling pattern as DISCO, but do not mix data • Uses the constraint of known non-zero wavelet coefficient locations based on a prior • We use the view shared volume as this prior • Remaining questions: • How sensitive is the solution to the initial guess? • How sensitive is the solution to the location constraint?

  17. DESPOT – fa1

  18. DISCOPOT – fa1

  19. LCAMP+DISCO – fa1

  20. LCAMP+DISCO, energy scaling – fa1

  21. DESPOT – fa8

  22. DISCOPOT – fa1

  23. LCAMP+DISCO – fa1

  24. LCAMP+DISCO, energy scaling – fa8

  25. LCAMP+DISCO, energy scaling – T1

  26. Comments • Something is going wrong with the LCAMP reconstruction • LCAMP output seems to closely match the initial guess for fa1, is it helping much?

  27. Conclusions and Future Work • DISCOPOT with energy scaling provides a compelling way to accelerate a DESPOT collection • Future work to apply this to a SSFP set and mcDESPOT

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