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Accelerated DESPOT Imaging: DISCOPOT and LCAMP Techniques

Explore DISCOPOT method for k-space sharing in DESPOT collection, analyze errors due to offline recon, and discuss solutions using energy scaling. Investigate LCAMP technique for compressed sensing reconstruction and its potential application in DESPOT imaging.

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Accelerated DESPOT Imaging: DISCOPOT and LCAMP Techniques

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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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