MaDetIndexAxdt

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

Master's thesis: Detectability Indices for Anisotropic X-ray Dark-field Tomography

Thesis by: Theodor Cheslerean Boghiu
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 are detectability indices for use in Anisotropic X-ray Dark-field Tomography (AXDT). The aim is to derive and implement a detectability index for AXDT within our C++ reconstruction framework, in order to use them for optimization of acquisition geometries. The results are to be tested and evaluated on experimental data from our AXDT scanners.

Requirements

Interest in maths, scientific programming, C++

Contact


ProjectForm
Title: Detectability Indices for 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: Theodor Cheslerean Boghiu
Director: Tobias Lasser
Supervisor:  
Type: Master Thesis
Area: Medical Imaging
Status: finished
Start: 2018/06/15
Finish: 2018/12/15
Thesis (optional):  
Picture:  


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