BaRegularizationStrategiesAxdt

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Bachelor's thesis: Manifold-based regularization of spherical functions in Anisotropic X-ray Dark-field Tomography

Thesis by: Nikola Dinev
Advisor: Tobias Lasser

Abstract

Anisotropic X-ray Dark-field Tomography (AXDT) enables the visualization of microstructure orientations without having to explicitly resolve them. Based on the directionally dependent X-ray dark-field signal as measured by an X-ray grating interferometer and our spherical harmonics forward model, AXDT reconstructs spherical scattering functions for every volume position, which in turn allows the extraction of the microstructure orientations. Potential applications range from materials testing to medical diagnostics.

Tasks

Subject of this thesis is the regularization of the spherical scattering functions used in AXDT. The aim is to investigate manifold-based regularization approaches of spherical functions, to implement them in our C++ reconstruction framework, and to evaluate them against existing, non-regularized methods. In addition, the mode of application of the regularization may be investigated, for example as denoising after reconstruction or as alternating optimization during reconstruction.

Requirements

Scientific programming, strong mathematic background, C++

Contact


ProjectForm
Title: Manifold-based regularization of spherical functions in Anisotropic X-ray Dark-field Tomography
Abstract: Anisotropic X-ray Dark-field Tomography (AXDT) enables the visualization of microstructure orientations without having to explicitly resolve them. Based on the directionally dependent X-ray dark-field signal as measured by an X-ray grating interferometer and our spherical harmonics forward model, AXDT reconstructs spherical scattering functions for every volume position, which in turn allows the extraction of the microstructure orientations. Potential applications range from materials testing to medical diagnostics.
Student: Nikola Dinev
Director: Tobias Lasser
Supervisor:  
Type: Bachelor Thesis
Area: Medical Imaging
Status: running
Start: 2018/04/15
Finish: 2018/08/15
Thesis (optional):  
Picture:  


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