Recent medical image reconstruction techniques focus on generating high-quality medical images suitable for clinical use at the lowest possible cost and with the fewest possible adverse effects on patients. Recent works have shown significant promise for reconstructing MR images from sparsely sampled k-space data using deep learning. In this work, we propose a technique that rapidly estimates deep neural networks directly at reconstruction time by fitting them on small adaptively estimated neighborhoods of a training set. In brief, our algorithm alternates between searching for neighbors in a data set that are similar to the test reconstruction, and training a local network on these neighbors followed by updating the test reconstruction. Because our reconstruction model is learned on a dataset that is in some sense similar to the image being reconstructed rather than being fit on a large, diverse training set, it is more adaptive to new scans. It can also handle changes in training sets and flexible scan settings, while being relatively fast. Our approach, dubbed LONDN-MRI, was validated on multiple data sets using deep unrolled reconstruction networks. Reconstructions were performed at four fold and eight fold undersampling of k-space with 1D variable-density random phase-encode undersampling masks. Our results demonstrate that our proposed locally-trained method produces higher-quality reconstructions compared to models trained globally on larger datasets as well as other scan-adaptive methods.

翻译：最近的医学图像重建技术专注于以尽可能低的成本和对患者尽可能少的不良影响，生成适用于临床的高质量医学图像。近期研究显示，利用深度学习从稀疏采样的k空间数据中重建磁共振图像具有显著前景。本研究提出一种技术，在重建过程中通过在小规模自适应估计的邻域上拟合神经网络，直接快速估计深度神经网络。简述而言，我们的算法交替执行以下步骤：在数据集中搜索与测试重建相似的邻域数据，在这些邻域上训练局部网络，随后更新测试重建。由于我们的重建模型是在与待重建图像具有某种相似性的数据集上学习的，而非在大规模多样化训练集上拟合，因此对新扫描数据具有更强的适应性。同时，该方法能适应训练集的变化和灵活的扫描设置，且保持相对较快的速度。我们提出的方法名为 LONDN-MRI，在多个数据集上采用深度展开重建网络进行了验证。使用一维变密度随机相位编码欠采样掩模，在四倍和八倍k空间欠采样条件下进行重建。结果表明，相较于在大数据集上全局训练的模型及其他扫描自适应方法，我们提出的局部训练方法可生成更高质量的重建结果。