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, 2012Jason Su

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

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,

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 :

117),1( nnn

Theory: DESPOT1

)1/exp(,)cos(1

)sin()1(1

1

10 TTREEEMSSPGR

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?

Conventional vs Random Spoiling

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?

“PSF” Improvement with Radial

Golden Angle Radial Sampling

Winkelmann (2007)

Methods1. Bloch simulation of conventional and random spoiling at

different angles and T1/TR2. 1.5T phantom scan to compare across many FA3. 1.5T phantom scan with many T1 for DESPOT14. 1.5T VFA and AFI in a single T1 5. 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

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

Results: Bloch Simulation

• A great result, T2 typically < 100ms

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

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

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?

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

Discussion

• Diffusion may improve the situation and allow lower TRYarnykh (2010)

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