What’s the difference between parametric deconvolution (BedpostX) and non-parametric deconvolution (CSD)?

Hey all,
I’m new to diffusion imaging so maybe my questions are a bit unclear. Sorry for that.

For my master-thesis I want to use your CSD approach and also the FSL BedpostX approach to analyse the direction information/voxel. I read all the paper about your method and the Fsl method. But I still do not understand the difference between parametric and non-parametric deconvolution.

  1. In that case what exactly means parametric and non-parametric? I also read that bedpostX uses different models (ball-and-stick and a multi shell model)
    I guess I do understand the SD approach (getting the ODF by deconvolute the signal with an response function) more than the parametric appraoch (bedpostX).

  2. If somebody could tell me in in general the differences of this two appraoches it would be so helpful to me.

Thank you

Hi @Max_Wichmann,

Parametric means that you have some parameters that control the distributions of the sticks. For example if a distribution was Gaussian then you would have the mean and standard deviation as parameters. The parametric approach is using bayesian ideas were you try to infer the parameters of the distributions of the sticks etc… But you still need to set some priors about how those distribution will be. So you need to estimate that somehow from the signal or set it in a specific way. For example you could start with a uniform distribution.

In the SD approach you deconvolve using a response function estimating the signal from highly anisotropic areas but you are not using any specific distribution. I think both methods are comparable at the end. And I would love to see a paper that compares the two extensively.

I hope this was helpful information. For more details about the parametric techniques I think you should ask directly in the FSL list. In DIPY we currently do not support bayesian techniques but it would be nice to have that in the future if we see that there are some clear advantages.

Best regards,


Dear Eleftherios,
thank you so far for your good explanations!

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