论文标题
SRNR：使用嘈杂的高分辨率参考数据训练超分辨率MRI的神经网络
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data
论文作者
论文摘要
基于神经网络（NN）的超分辨率MRI方法通常需要在许多受试者中获得的高SNR高分辨率参考数据，这是耗时的，并且是可行且可访问的实现的障碍。我们建议使用嘈杂的参考数据（SRNR）训练NNS进行超分辨率，以利用经典的基于NN基于NN的DeNoising方法噪声2Noise的机制。我们系统地证明，使用嘈杂和高SNR参考的NNS的结果对于模拟和经验数据都相似。 SRNR建议可以使用少量的高分辨率参考数据重复来简化超分辨率MRI的训练数据准备。
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.
