In this work, we present a novel convolutional neural network based method for perfusion map generation in dynamic susceptibility contrast-enhanced perfusion imaging. The proposed architecture is trained end-to-end and solely relies on raw perfusion data for inference. We used a dataset of 151 acute ischemic stroke cases for evaluation. Our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy.
CITATION STYLE
Hess, A., Meier, R., Kaesmacher, J., Jung, S., Scalzo, F., Liebeskind, D., … McKinley, R. (2019). Synthetic perfusion maps: Imaging perfusion deficits in DSC-MRI with deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11383 LNCS, pp. 447–455). Springer Verlag. https://doi.org/10.1007/978-3-030-11723-8_45
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