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Abstract #2437

Training a tunable, spatially-adaptive denoiser without clean targets

Laura Pfaff1,2, Julian Hossbach1,2, Elisabeth Preuhs1, Tobias Wuerfl2, Silvia Arroyo Camejo2, Dominik Nickel2, and Andreas Maier1
1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 2MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany


Accelerating MRI is intrinsically limited by the thermal noise from the imaged object. In this work we aim to optimize MR image denoising using an unsupervised deep learning-based method. Stein's unbiased risk estimator and spatially resolved noise maps indicating the standard deviation of the noise for every pixel were incorporated into the training process. It was shown that this approach can achieve results that are equal or superior to those of state-of-the-art supervised and unsupervised methods. Furthermore, we show how to control the tradeoff between denoising and image sharpness by using a model conditioned on the noise map.

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