Iterative methods in regularized tomographic reconstruction

PhD Defense
Start Date
13-11-2017 09:00
End Date
13-11-2017 11:30
Auditorium, Central Building
Speaker's name
Pierre PALEO
Speaker's institute
Contact name
Fabienne Mengoni
Host name
Alessandro Mirone
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In the last years, there have been a diversification of the tomography imaging technique
for many applications. However, experimental constraints often lead to limited data - for
example fast scans, or medical imaging where the radiation dose is a primary concern.
The data limitation may come as a low signal to noise ratio, scarce views or a missing
angle wedge. On the other hand, artefacts are detrimental to reconstruction quality. In
these contexts, the standard techniques show their limitations.
In this work, we explore how regularized tomographic reconstruction methods can
handle these challenges. These methods treat the problem as an inverse problem, and
the solution is generally found by the means of an optimization procedure. Implementing
regularized reconstruction methods entails to both designing an appropriate regularization,
and choosing the best optimization algorithm for the resulting problem.
On the modelling part, we focus on three types of regularizers in an unified mathemat-
ical framework, along with their efficient implementation : Total Variation, Wavelets and
dictionary-based reconstruction. On the algorithmic part, we study which state-of-the-art
convex optimization algorithms are best fitted for the problem and parallel architectures
(GPU), and propose a new algorithm for an increased convergence speed.
We then show how the standard regularization models can be extended to take the
usual artefacts into account, namely rings and local tomography artefacts. Notably, a
novel quasi-exact local tomography reconstruction method is proposed.

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