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daducci / COMMIT

Linear framework to combine tractography and tissue micro-structure estimation with diffusion MRI
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A matrix in voxels containing crossing fibers #43

Closed behnam12 closed 5 years ago

behnam12 commented 6 years ago

Hi COMMIT group,

I am going to understand the idea of A matrix in voxels containing crossing fibers. My question is that: for a given fiber, in a voxel containing crossing fibers, how COMMIT framework selects between different peaks? As I show in the attached image, for the 1st fiber in 1st voxel, how COMMIT choose the 1st column of left A matrix instead of the 1st column of the right A matrix.(I think, this is the example which explained by Daducci during his presentation in ESMRMB conference).

in another word, I am wondering what the criteria is for selecting between 2 peaks in a voxel for a given fiber.

Best Behnam commit

daducci commented 6 years ago

Hi @behnam12 ,

COMMIT actually does not select between different peaks, but there will be an element in the A matrix for that specific fiber, in that specific voxel, for that specific orientation of the fiber in that voxel. In practice, the polyline representing a fiber is split into segments, each with position and orientation; in particular, the orientation is the actual orientation of the fiber in that location. This information is known a priori, because you know exactly the geometry of the polyline.

Is that clear?

behnam12 commented 6 years ago

Hi @daducci,

Thank you for your explanation. Now, AIC matrix is clear for me. Actually, I am doing my PhD in the field of Using multi-contrast microstructural tractography and need to understand COMMIT and then combine it with forward model for g-ratio or myelin. I have two other questions regarding COMMIT.
According to my understanding, response function of extracellular component AEC is reoriented to match the one of principal diffusion directions. Could you explain that what the criteria could be for selecting one of principal diffusion directions for a given voxel. I have also another question regarding predicted signal, if I understand correctly, the predicted signal S(q)=AX, at q space location q ϵ R3 ,is calculated for each diffusion gradient directions, nd, and then it is compared with y vector (measured data). So , is S ϵ R nx ny nz nd ?

Thanks for your time and explanation

Best Behnam