Bachelor thesis: Regularization of Structures in Anisotropic X-ray Dark-field Tomography
Project by: Maximilian Hornung
Advisor:
Tobias Lasser
Supervision by:
Matthias Wieczorek
Abstract
Medical imaging modalities such as X-ray Computed Tomography (X-ray CT) have been the basis of accurate diagnosis in clinical practice for decades, one particular example being the detection of tumors. But medical imaging also plays a central role during the therapy, for example when planning complex surgeries or when planning and monitoring radiation therapy treatments. New X-ray contrast modalities, such as phase-contrast and dark-field contrast, are being developed in the last few years, based on a break-through in grating interferometry, with many promising clinical applications, ranging from breast cancer detection to diagnosis of osteoporosis.
Tasks
The aim of this project is the investigation of the directional X-ray dark-field contrast with an eye towards application in medical imaging of lung and brain. In particular, the influence of noise in the raw data on the extracted structural information is to be investigated for the recently introduced anisotropic X-ray dark-field tomography (AXDT) imaging modality. Optionally, the use of denoising techniques to compensate for the aforementioned noise is to be investigated. Based on the existing C++ framework “CampRecon”, which already incorporates the entire image processing pipeline for AXDT reconstruction, appropriate tools and methods for the investigation at hand are to be implemented and evaluated on experimental data.
Requirements
Scientific programming, strong mathematic background, C++
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