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Oct 8, 2012 Jason Su

Journal Club: Lin and Song. (Philips and UPenn ) Improved Signal Spoiling in Fast Radial Gradient-Echo Imaging: Applied to Accurate T1 Mapping and Flip Angle Correction. (MRM 2009). Oct 8, 2012 Jason Su. Motivation. Mapping methods like DESPOT1 and AFI depend on “perfect” SPGR signal models

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Oct 8, 2012 Jason Su

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  1. Journal Club:Lin and Song. (Philips and UPenn)Improved Signal Spoiling in Fast Radial Gradient-Echo Imaging: Applied to Accurate T1 Mapping and Flip Angle Correction. (MRM 2009) Oct 8, 2012 Jason Su

  2. Motivation • Mapping methods like DESPOT1 and AFI depend on “perfect” SPGR signal models • Assumes that there is no coherent transverse magnetization left after a TR • The ability to achieve this in experiment directly affects the performance of these methods • At higher flip angles (>40 deg) and T1/TR ratios (>50), typical spoiling schemes breakdown • Ives and I are working to improve DESPOT1 at 3T and 7T with B1 mapping and pulse design, this is also needed

  3. Other Papers • Many other papers address this topic with a focus on better understanding the spoiling model like including diffusion • Yarnykh. Optimal RF and Gradient Spoiling… MRM 2010. • Preibisch and Deichmann. Influence of RF Spoiling on the Stability and Accuracty of T1 Mapping… MRM 2009,

  4. Theory: Spoiling • The goal of spoiling is to dephase the signal at any given voxel so that there is no net coherent transverse magnetization • This is typically achieved via RF and gradient spoiling • Gradient spoiling: a gradient is played at the end of the TR that results in a net N*2π phase twist across the length of a voxel -> signal integrates to zero across the voxel • RF spoiling: the phase of the RF pulse is changed between each TR, determined experimentally for representative T1s • Typical quadratic spoiling :

  5. Theory: DESPOT1

  6. Idea: Random Spoiling • The key innovation of this paper is to randomize the spoiling for both the RF phase and spoiler gradient area • Uniform random sampling • Tested different maximum gradient area schemes (up to 2, 4, 10, 20, 50, 100 cycles per voxel) • Does this lead to better spoiling at the end of a TR?

  7. Conventional vs Random Spoiling

  8. Problem: Random Signal • Generates non-steady state signal that oscillates randomly about the theoretical perfectly spoiled signal • The image has small random changes from TR to TR • Results in ghosting/corrupted images with Cartesian sampling scheme • Solution: use a radial sampling scheme to spread out the inter-TR errors • Golden angle method may have been chosen for easy of implementation?

  9. “PSF” Improvement with Radial

  10. Golden Angle Radial Sampling Winkelmann(2007)

  11. Methods • Bloch simulation of conventional and random spoiling at different angles and T1/TR • 1.5T phantom scan to compare across many FA • 1.5T phantom scan with many T1 for DESPOT1 • 1.5T VFA and AFI in a single T1 • 3T VFA and AFI in vivo, no reference T1 • In general slice thickness was large (>1cm) • Was this to reduce TR by allowing the spoiler gradient to be shorter? • I thought this was a little messy, a lot of experiments that could have been combined

  12. Results: Bloch Simulation • Surprising that high gradient spoiling at low flips does worse • Why not a higher T1/TR in (c)? Realistic and more problematic

  13. Results: Bloch Simulation • A great result, T2 typically < 100ms

  14. Results: Simulation and Experiment • At high flip angles, there’s a large deviation between experiment and simulation that’s not discussed, high T2/TR regime

  15. Results: DESPOT1 • Random spoiling shows better T1 accuracy in phantom tubes • At lower T1s, the Ernst angle is higher, which is where the spoiling condition is worse for conventional

  16. Results: AFI • Didn’t verify AFI independently with another method • 2nd hand approach, if the T1 is uniform then the B1 map is good, which Ives and I have used in the past but not preferable?

  17. Results: In Vivo • T1 maps corrected with AFI B1 map at 3T • Need reference T1 for a convincing result • (c) and (e) are conventional • (d) and (f) are random spoiling

  18. Discussion Yarnykh (2010) • Diffusion may improve the situation and allow lower TR

  19. Discussion • Need both random RF and gradient spoiling • How to adapt to Cartesian sampling? • They propose larger random gradient which will reduce errors at higher flips, but still fundamentally random variation between lines • What if they played out the same random sequence each TR for either RF or gradient? • Ives and I bump up the spoiler gradient at 3T, but this paper suggests that this will have little effect on the resulting signal if it’s a multiple of 2π • But Yarnykh shows improvement with increased spoiler

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