[Neuroimaging] [Dipy] RE: Interpretation of beta in the Sparse Fascicle Model
Reid, Robert I. (Rob)
Reid.Robert at mayo.edu
Tue Nov 22 17:32:51 EST 2016
Hi again,
Does anybody have any suggestions on quantitatively estimating the fraction of each fiber bundle in a voxel?
Thanks,
Rob
--
Robert I. Reid, Ph.D. | Sr. Analyst/Programmer, Information Technology
Aging and Dementia Imaging Research | Opus Center for Advanced Imaging Research
Mayo Clinic | 200 First Street SW | Rochester, MN 55905 | mayoclinic.org<http://www.mayoclinic.org/>
From: Neuroimaging [mailto:neuroimaging-bounces+reid.robert=mayo.edu at python.org] On Behalf Of Reid, Robert I. (Rob)
Sent: Monday, November 14, 2016 11:42 AM
To: 'neuroimaging at python.org'
Subject: [Neuroimaging] Interpretation of beta in the Sparse Fascicle Model
Hi,
I am trying to use a set of simulations to optimize the b values in a multishell acquisition for general use. My current choice for the objective (cost) function is the difference between the true input and apparent recovered "total fiber vector"s, which I define as
(f0 * d0, f1 * d1, f2 * d2),
where fi and di are the voxel fraction and direction of fiber I, so it is a 9 dimensional vector, and the error in each fiber's direction is weighted by its voxel fraction. My problem is getting the fiber fractions. I have mostly followed the sparse fascicle model tutorial in http://nipy.org/dipy/examples_built/sfm_reconst.html#example-sfm-reconst , and the beta values seem to be what I should use. I set the apparent fiber fraction to sum(beta_j), for j in the part of the sphere closest to the true direction of fiber i. (That can misassign outliers, I know, but that's a different problem.)
It *almost* works, but sum(beta) is often a bit larger than 1, especially as b of the outer shell is raised from 2000 to 3000.
For example, with (f0, f1, f2) = (0.500, 0.250, 0.125),
with b_hi = 2000 I get [ 0.50418062, 0.21846355, 0.15918703]
with b_hi = 3000 I get [ 0.63809217, 0.36634215, 0.30759466]
When averaged over a large number of simulations and scenarios the trend is that there is less angular error at b_hi = 3000, but the overall error function favors b_hi = 2000, because the fiber fraction estimates are so bad at b_hi = 3000. I am using the ExponentialIsotropicModel for the isotropic part.
Am I abusing beta in some way, or is it just overestimating the fiber fractions "naturally" and I should accept the indication that the fiber fraction estimation degrades when going from 2000 to 3000?
Note that beta should not (in my understanding) be normalized so that sum(beta) = 1. In the above example the sum of the fiber fractions is 0.875, and in general this is a quantity that I would like to estimate.
Thanks,
Rob
--
Robert I. Reid, Ph.D. | Sr. Analyst/Programmer, Information Technology
Aging and Dementia Imaging Research | Opus Center for Advanced Imaging Research
Mayo Clinic | 200 First Street SW | Rochester, MN 55905 | mayoclinic.org<http://www.mayoclinic.org/>
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