通过数据筛选提升深度学习在加速MRI重建中的表现。
Improving Deep Learning for Accelerated MRI With Data Filtering
- 从18个公开源筛选k空间数据，构建多样化训练集。
- 过滤训练数据可带来稳定但小幅的重建性能提升。
- 在分布内数据占比低时，筛选效果尤为显著。
深度神经网络在加速MRI重建中取得领先成果。现有研究多聚焦于改进神经网络架构，且在固定、同质的数据上训练与评估。本文研究了数据整理策略对MRI重建的影响。我们整合了来自18个公开数据源的110万张原始k空间图像，构建包含48个测试集的多样化评估集，涵盖解剖结构、对比度、线圈数量等关键差异。提出并评估多种数据筛选策略，以提升当前先进神经网络在加速MRI重建中的表现。实验表明，筛选训练数据可带来一致且适度的性能提升，且在不同训练集规模和加速度条件下均具鲁棒性；当未筛选训练集中分布内数据比例较低时，筛选效果尤为突出。
原文摘要 · Abstract (English)
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.
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