标题： 通过数据过滤提高加速MRI的深度学习 显示英文标题

标题： Improving Deep Learning for Accelerated MRI With Data Filtering

摘要： 深度神经网络在加速MRI重建中实现了最先进的结果。 大多数基于深度学习的成像研究集中在改进在固定和同质训练和评估数据上训练和评估的神经网络架构。 在本工作中，我们研究了数据整理策略以改善MRI重建。 我们从18个公共来源汇编了一个包含110万张图像的大型原始k空间数据集，并构建了一个包含48个测试集的多样化评估集，涵盖了解剖结构、对比度、线圈数量和其他关键因素的变化。 我们提出了不同的数据过滤策略，以提高当前最先进的神经网络在加速MRI重建中的性能。 我们的实验表明，过滤训练数据会导致一致但适度的性能提升。 这些性能提升在不同的训练集大小和加速条件下都是稳健的，我们发现当未过滤训练集中的分布内数据比例较低时，过滤特别有益。 显示英文摘要

摘要： Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data curation strategies for improving MRI reconstruction. We assemble a large dataset of raw k-space data from 18 public sources consisting of 1.1M images and construct a diverse evaluation set comprising 48 test sets, capturing variations in anatomy, contrast, number of coils, and other key factors. We propose and study different data filtering strategies to enhance performance of current state-of-the-art neural networks for accelerated MRI reconstruction. Our experiments show that filtering the training data leads to consistent, albeit modest, performance gains. These performance gains are robust across different training set sizes and accelerations, and we find that filtering is particularly beneficial when the proportion of in-distribution data in the unfiltered training set is low.