Cone-Beam CT X-Ray Image Simulation for the Generation of Training Data

Type: BA thesis

Status: running

Date: June 1, 2022 - November 2, 2022

Supervisors: Maximilian Rohleder, Bjoern Kreher (Siemens Healthineers), Andreas Maier

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Description

Deep Learning methods can be used to reduce the severity of Metal Artefacts in Cone-Beam CT images. This thesis aims to design and validate a simulation pipeline, which creates realistic X-Ray projection images from available CT volumes and metal object meshes. Additionally, 2D and 3D ground truth binary masks should provide a segmentation of metal to be used as ground truth during training. The explicit focus of the data generation will be placed on the accuracy of the Metal Artefacts.

Your qualifications

  • Fluent in Python and/or C++
  • Knowledge of Homogenous Coordinates and Projective Mapping
  • Interest in Quality Software Development / Project Organisation
  • Experience with CUDA and interface to C++ / Python (optional, big plus)

You will learn

  • to organize a short-term project (report status and structured sub-goals)
  • to scientifically evaluate the developed methods
  • to report scientific findings in a thesis / a publication

 

The thesis is funded by Siemens Healthineers and can be combined with a working student position prior to or after the thesis (up to 12 h/week). If interested, please write a short motivational email to Maxi.Rohleder@fau.de highlighting your qualifications and describe one related code project you are proud of. Please also attach your CV and transcript of records from your current and previous studies.