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Welcome to the microstruktur wiki!

Improvements to make:

• remove spherical harmonics dependency on dipy. only depend on visualization ondipy.
• spherical mean model explanation should include astro-models.
• fitting does not give error if there is no b0.
• ODI and beta_fraction are optimization parameters. The flags to do this should be included in the call for the Watson/BinghamDistributedModel. i.e. if these are turned off they use the regular kappa/beta parameters.
• huge diameters gives NA signal attenuation without error message.
• volume fractions cannot be fixed while using MIX.
• add print_model_summary function. Should output model composition and parameter optimization settings.
• set custom parameter link and set custom replaced parameter. make parameter link a list of 4, and an optimized parameter a list of 5, with the last item being the name appendix for the optimized parameter.
• fod optimizer cannot replay custom parameters now.
• still fix brute2fine fixed parameters.
• callaghan sphere
• van gelderen plane and capped cylinder
• separate parameters links in separate python file in utils.
• rewrite docs for DD1

Issues for later:

• psi brute optimization should take into account that it's circular.
• mu parameter ranges should not be constrained somehow.
• Brute2Fine should estimate proper grid for mu instead of theta-phi grid in equal steps.
• function "print_relevant_references" that can model-dependently print the references that are related with the current model composition.
• l0.5-norm for mix as in (Zhu, Xinghua, et al. "Model selection and estimation of multi-compartment models in diffusion MRI with a Rician noise model." IPMI. 2013.).
• find optimal parameters for Ns and NSpherePoints for arbitrary model setup.
• make bingham and watson both sh-order and sphere dependent (so less points are sampled at lower sh-orders)
• visualize model using graph nodes from optimized parameters -> linked / preset parameters -> input for models -> combined signal. dask uses http://www.graphviz.org/.
• Implement sparse dictionary fitting using http://spams-devel.gforge.inria.fr/ as in AMICO.
• deep q-space learning Golkov et al. https://sci-hub.tw/10.1109/TMI.2016.2551324
• Analytic gradients in optimization.
• Bingham is currently normalized using spherical mean instead of analytically. The implementation of the generalized hyperconfluent function of Matrix argument is required, see http://www-math.mit.edu/~plamen/files/hyper.pdf. This is also of consequence for closed form watson/bingham dispersed stick implementations, see Appendix A in https://sci-hub.io/10.1016/j.neuroimage.2012.01.056.
• directly estimate bingham / watson in spherical harmonics https://arxiv.org/pdf/1501.04395.pdf
• Implement Matrix-Variate Distribution models and DIAMOND (Scherrer).