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arXiv · 2211.05360

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data

Jiaxin Xiao·Zihan Li·Berkin Bilgic·Jonathan R. Polimeni·Susie Huang·Qiyuan Tian
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Abstract

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation. We propose to train NNs for Super-Resolution using Noisy Reference data (SRNR), leveraging the mechanism of the classic NN-based denoising method Noise2Noise. We systematically demonstrate that results from NNs trained using noisy and high-SNR references are similar for both simulated and empirical data. SRNR suggests a smaller number of repetitions of high-resolution reference data can be used to simplify the training data preparation for super-resolution MRI.

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Jiaxin Xiao, Zihan Li, Berkin Bilgic, Jonathan R. Polimeni, Susie Huang, Qiyuan Tian. 2022-11-10. SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data. https://arxiv.org/abs/2211.05360

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