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Welcome to the microstruktur wiki!

Improvements to make:

• huge diameters gives NA signal attenuation without error message.
• 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.
• rename soderman to steyskaltanner?
• rewrite docs for DD1
• restricted zeppelin to temporal zeppelin
• Spherical harmonics should always check thst all non linear parameters are fixed.

Bugs:

• catch for if optimization flag is off but no initial condition is given.

Issues for later:

• let tournier07-csd be the first spherical mean FOD optimizer in FittedMultiCompartmentSphericalMeanModel.
• psi brute optimization should take into account that it's circular.
• peaks with corresponding peak intensities.
• 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).
• Implement Spherical harmonics as a non-parametric density for micro-environments.

Examples not to be included in first version

• AxCaliber with restricted extra-axonal diffusion [Burcaw et al. 2015]