Glioblastoma, the most common primary malignant brain tumor, still carries a bleak prognosis. To guide more efficient treatment options, a lot of scientific effort is put into investigating the peritumoral edema for microscopic tumor infiltration. Here, Diffusion Tensor Imaging (DTI) has emerged as a promising imaging tool. However, its application is impeded by the diffusion effects of free-water. In this thesis, a novel Deep Learning-based approach for correcting the free-water contamination of DTI data is evaluated for its utility in recurrence prediction of glioblastoma.
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Glioblastoma, the most common primary malignant brain tumor, still carries a bleak prognosis. To guide more efficient treatment options, a lot of scientific effort is put into investigating the peritumoral edema for microscopic tumor infiltration. Here, Diffusion Tensor Imaging (DTI) has emerged as a promising imaging tool. However, its application is impeded by the diffusion effects of free-water. In this thesis, a novel Deep Learning-based approach for correcting the free-water contamination o...
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