Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging

• URL: http://arxiv.org/abs/2207.10181v1
• Date: Wed, 20 Jul 2022 20:19:44 GMT
• Title: Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging
• Authors: Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun, Wolfgang Bogner, Georg Widhalm, Chenyu You, John A. Onofrey, Robin de Graaf, James S. Duncan
• Abstract summary: We propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI. Our enhancer network incorporates anatomical information from additional image modalities (MRI) and uses a learnable base distribution. Our method also allows visual quality adjustment and uncertainty estimation.
• Score: 13.408365072149795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for quantifying metabolites in the body, but the low spatial resolution limits its clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the experimentally-acquired high-resolution images. Attempts have been made with the generative adversarial networks to improve the image visual quality. In this work, we consider another type of generative model, the flow-based model, of which the training is more stable and interpretable compared to the adversarial networks. Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI. Different from previous flow-based models, our enhancer network incorporates anatomical information from additional image modalities (MRI) and uses a learnable base distribution. In addition, we impose a guide loss and a data-consistency loss to encourage the network to generate images with high visual quality while maintaining high fidelity. Experiments on a 1H-MRSI dataset acquired from 25 high-grade glioma patients indicate that our enhancer network outperforms the adversarial networks and the baseline flow-based methods. Our method also allows visual quality adjustment and uncertainty estimation.

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