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Fig 5. The q-space approach illustrated using simulated data. First, diffusion weighted MR imaging data are acquired changing the diffusion gradient strength (g) along each direction considered (A). Then, using the relationship q = 
x 
x g, where is the gyromagnetic ratio and is the pulse duration, the measured signal intensity, S(g), is expressed as function of q (B). The probability P(r, 
) that a molecule ends up at position r after a time is then calculated as the inverse Fourier transform of S(q), being the separation between the leading edges of the pulses (C). Finally, in one approach to characterizing P, the fast and slow diffusion components are extracted after Gaussian fitting to P(r, ) and the peak height of the slow component used as probability for zero displacement. The slow component is thought to reflect the integrity of the myelin sheath and cell membranes.
