Klinisches Anwendungsprojekt: Evaluating modularization and efficiency of a C++ reconstruction framework for medical imaging modalities
Thesis by: Maximilian Hornung
Advisor:
Nassir Navab
Supervision by:
Tobias Lasser
Abstract
Medical imaging modalities such as X-ray Computed Tomography (X-ray CT) or Positron Emission Computed Tomography (PET) 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. The formation of three-dimensional images from the detector signals in commercial devices is still mostly done by analytical reconstruction methods, mainly due to their efficiency. However, iterative methods, such as statistical reconstruction, are becoming more popular, as they provide better image quality combined with reduced radiation dose. The higher computational complexity of the iterative methods is partly compensated by the increasingly available computational power.
Tasks
Aim of this project is the evaluation of modularization and efficiency of an existing C++ framework (the “elsa Recon Toolbox”) for the statistical reconstruction of high-resolution X-ray CT data. In particular, the base software components critical for the performance of the image reconstruction should be evaluated for efficiency, in addition to evaluating the modularity with an eye to both performance and maintainability / extensibility. The resulting code will be implemented in the “elsa Recon Toolbox” and will be applied to real clinical data from the radiology department of Klinikum rechts der Isar.
Requirements
Scientific programming, strong mathematic background, C++
Contact