Journal club mcdespot with b0 b1 inhomogeneity
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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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  • 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)


  • 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

M cdespot theory2
mcDESPOT Theory

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%)


  • 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?