Keywords: Quantitative Imaging, Machine Learning/Artificial IntelligenceTypically, in quantitative MRI, an inverse problem of finding parameter maps from magnitude images has to be solved. Neural networks can be applied to replace non-linear regression models and implicitly learn a suitable spatial regularization. However, labeled training data is often limited. Thus, we propose a combination of training on synthetic data and on unlabeled in-vivo data utilizing pseudo-labels and a Noise2Self-inspired technique. We present a convolutional neural network trained to predict T1, B0, and B1 maps and their estimated aleatoric uncertainties from a single WASABITI scan.
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