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Journal Club: mcDESPOT with B0 & B1 Inhomogeneity. Papers. Deoni et al. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn . Reson . Med. (2008) vol. 60 (6) pp. 1372-1387

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papers
Papers
  • Deoni et al. Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magn. Reson. Med. (2008) vol. 60 (6) pp. 1372-1387
  • Deoni. Correction of main and transmit magnetic field (B0 and B1) inhomogeneity effects in multicomponent-driven equilibrium single-pulse observation of T1 and T2. Magn. Reson. Med. (2010)
mcdespot
mcDESPOT
  • A whole-brain quantitative mapping technique
  • Idea: collect SPGR and SSFP scans at several flip angles
    • These have a known theoretical steady-state signal equation
    • Fit the equation as it varies with flip angle to the collected data at each voxel
    • Gives us: T1, T2, and more
  • Uses a two-compartment model for the signal equation: a fast and slow relaxing species in exchange
scdespot theory
scDESPOT Theory
  • DESPOT1: SPGR equation
    • Find M0 and T1, minimize (SSPGR-ŜSPGR)2
  • DESPOT2: SSFP equation
    • Given T1, find M0 and T2, minimize (SSSFP-ŜSSFP)2
m cdespot theory
mcDESPOT Theory
  • 2 component SPGR equation
    • Find fF,fS, T1,F, T1,S, kFS=1/τF, kSF=1/τS
m cdespot theory1
mcDESPOT Theory
  • 2 component SSFP equation
    • Find M0 and T1, minimize (SSPGR-ŜSPGR)2
simplifying assumptions
Simplifying Assumptions
  • 2 component model
    • Only need to find fF (the fast volume fraction)
  • Chemical equilibrium
    • Allows us to eliminate finding kSF=1/τS
  • Both components are on the same resonance
fitting method
Fitting Method
  • Genetic Algorithm
    • Previous method, proved to be too slow
  • Stochastic Region of Contraction
    • Current method, processing time is still substantial (about 24 hrs. for a 2mm isotropic brain)
    • Supposedly good for avoiding local minima
    • Not much literature on it (Berger and Silverman. Microphone Array Optimization byStochastic Region Contraction.)
stochastic region of contraction
Stochastic Region of Contraction
  • Has been offered as an alternative to simulated annealing, which can be slow but is very general
  • SRC is good for objective functions with these characteristics:
    • Few large valleys, many small local minima is fine
    • The neighborhood around the global minimum is still lower than any other local minima
    • Depends on <100 variables
stochastic region of contraction1
Stochastic Region of Contraction
  • Given an initial N-dimensional, rectangular, search volume containing the global optimum
    • Explore the objective function with random points in the space
    • Systematically contract the volume until it reaches a satisfactorily small region that traps the global optimum
stochastic region of contraction2
Stochastic Region of Contraction
  • Algorithm
    • Define initial search space for: T1,F&S, T2,F&S, fF, τF, Δωs
    • Treat this rectangular box as a uniform distribution and sample N times
    • Compute the objective function for each sample
    • Keep M of the best samples and define the new box based on the ranges of the variables in these samples
    • Rinse and repeat until convergence
when things go wrong
When Things Go Wrong
  • Simulation of artifacts
    • B0 effects
    • B1 and slab profile effects
      • The key assumption of mcDESPOT is that the prescribed flip angle is achieved everywhere in the volume
      • Need to account for this if not the case
  • In vivo at 1.5T (and some proof of concept 3T)
    • 4 normal volunteers
b0 solutions
B0 Solutions
  • B0 mapping
  • DESPOT2-FM with phase-cycled SSFP
    • Requires collection of another set of 9 SSFP images
    • Modified signal equation and objective function as presented earlier
b1 solutions
B1 Solutions
  • B1 mapping
    • AFI, niDALL, Bloch-Siegert
  • DESPOT1-HIFI with IR-SPGR (1 component)
    • Requires addition of 1 IR-prepped scan
    • Details of signal equation to derive B1 map not covered here
    • Gives κ(r):
in vivo2
In Vivo
  • B0 and B1 effects are not enough to account for the difference in MWF between mcDESPOT and T2-MCRI (usu. in range of 8-9%)
conclusions
Conclusions
  • DESPOT1-HIFI does well even thought slab profile changes with angle and assumes single component
    • Alternatives should be considered though since anatomical structures are visible on the maps: not the best B1 map
    • Bloch-Siegert seems compelling but need a way to incorporate slab profile as well
  • DESPOT2-FM and phase-cycled SSFP has been a part of the protocol at 1.5T and should also stay when we move to 3T+
    • Alternative B0 mapping methods should be considered if they offer a significant benefit in acquisition time
other avenues to explore
Other Avenues to Explore
  • 3 component model, is another pool skewing the MWF?
  • Are the 2 components actually on the same resonance?
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