Abstract #1370

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

Jiaxin Xiao¹, Zihan Li², Berkin Bilgic^3,4, Jonathan R. Polimeni^3,4, Susie Huang^3,4, and Qiyuan Tian^3,4

¹Department of Electronic Engineering, Tsinghua University, Beijing, China, ²Department of Biomedical Engineering, Tsinghua University, Beijing, China, ³Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Analysis

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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