Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction

• URL: http://arxiv.org/abs/2304.06955v1
• Date: Fri, 14 Apr 2023 06:58:44 GMT
• Title: Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
• Authors: Christoph Angermann, Simon G\"oppel and Markus Haltmeier
• Abstract summary: State-of-the-art reconstruction methods have been developed based on recent advances in deep learning. For such approaches to be used in safety-critical domains such as medical imaging, the network reconstruction should not only provide the user with a reconstructed image, but also with some level of confidence in the reconstruction. This work is the first approach to solving inverse problems that additionally models data-dependent uncertainty by estimating an input-dependent scale map.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods have been developed based on recent advances in deep learning. Especially for highly underdetermined problems, maintaining data consistency is a key goal. This can be achieved either by iterative network architectures or by a subsequent projection of the network reconstruction. However, for such approaches to be used in safety-critical domains such as medical imaging, the network reconstruction should not only provide the user with a reconstructed image, but also with some level of confidence in the reconstruction. In order to meet these two key requirements, this paper combines deep null-space networks with uncertainty quantification. Evaluation of the proposed method includes image reconstruction from undersampled Radon measurements on a toy CT dataset and accelerated MRI reconstruction on the fastMRI dataset. This work is the first approach to solving inverse problems that additionally models data-dependent uncertainty by estimating an input-dependent scale map, providing a robust assessment of reconstruction quality.

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