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Abstract #1809

Quantifying Reconstruction Uncertainty with Image Quality Transfer

Ryutaro Tanno1,2, Aurobrata Ghosh1, Francesco Grussu3, Enrico Kaden1, Antonio Criminisi2, and Daniel C Alexander1

1Computer Science, University College London, London, United Kingdom, 2Microsoft Research Cambridge, 3Institute of Neurology, University College London

Image quality transfer employs machine learning techniques to enhance quality of images by transferring information from rare high-quality datasets. Despite its successful applications in super-resolution and parameter map estimation of diffusion MR images, it still remains unclear how to assess the veracity of the predicted image in practice, especially in the presence of pathology or features not observed in the training data. Here we show that one can derive a measure of uncertainty from the IQT framework and demonstrate its values as a surrogate measure of reconstruction accuracy (e.g. root mean square error).

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