Modern medical imaging allows for a detailed insight into the human body. The wide range of imaging methods enables the acquisition of a large variety of information, but the individual modalities are usually limited to a small part of it. Therefore, often several acquisition types in different modalities are necessary to obtain sufficient information for the assessment. The evaluation of this extensive information poses great challenges for clinical users. In addition to the time expenditure, the identification of correlations across multiple data sets is a difficult task for human observers. This highlights the urgency of holistic processing of the accruing information. The simultaneous evaluation and processing of all available information thus not only has the potential to uncover previously unimagined correlations but is also an important step towards relieving the burden on clinical personnel. In this thesis, we investigate multiple approaches for the processing of multi-modal medical image data in different application areas. First, we will focus on hybrid X-ray and magnetic resonance (MR) imaging. The combination of these modalities has great potential especially in interventional imaging due to the combination of fast, high-resolution X-ray imaging and the high contrast diversity of magnetic resonance imaging. For further processing of this data, however, it is often advantageous to have the information from both modalities in one domain. Therefore, we investigate the possibility of a deep learning-based projection-to-projection translation of MR projection images to corresponding X-ray-like views. In the course of this work, we show that the characteristics of projection images pose special challenges to the methods of image synthesis. We tackle these by weighting the objective function with a focus on high-frequency structures and a corresponding adaptation of the network architecture. Both modifications show clear improvements compared to conventional approaches, quantitative as well as qualitative. ...


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    Titel :

    Multi-modal Medical Image Processing with Applications in Hybrid X-ray/Magnetic Resonance Imaging ; Multimodale medizinische Bildverarbeitung mit Anwendungen in der hybriden R¨ontgen-/Magnetresonanzbildgebung


    Beteiligte:

    Erscheinungsdatum :

    2021-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Schlagwörter :

    Klassifikation :

    DDC:    004 / 629




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