# Title:
## Presenter: Aurelien Coussat
## Date: 30.09 2025
## Participants:
Aurelien Coussat (AC)
Wojciech Krzemień (WK)
Konrad Klimaszewski (KK)
Roman Shopa (RS)
Lech Raczyński (LR)
Kamil Dulski (KD)
Oleksandr Fedoruk (OF)
## Questions/Remarks:
WK: Are all your building blocks functions differentiable?
AC: Yes, all are differentiable.
WK: Does the autograd know how to differentiate all functions? Do you provide both the function and its derivative?
It knows in my case, cause the "differentiation" is trivial?
AC: It is exactly how I finally implemented it. I provided both function and derivative as building blocks.
KK: How is the Most Likely Path estimated? Is it purely geometrical?
AC: Yes. It was investigated a lot, and it seems, in some sense, sufficient.
WK: Did you think about some regularisation?
AC: Yes, we considered it, especially for a specific phantom where we have issues with the conversion.
LR: How do you initialise your algorithm?
AC: For a moment, we start with the empty image.