论文标题
一种适用于膝盖MRI重建的自适应智能算法
An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
论文作者
论文摘要
自适应智能旨在通过进一步使用域知识来增强机器学习技术。在这项工作中，我们介绍了适应性情报在加速MR采购中的应用。从不足采样的K空间数据开始，一种受压缩传感理论启发的基于迭代学习的重建方案用于重建图像。我们采用深层神经网络来完善和纠正鉴于培训数据的先前重建假设。该网络接受了Facebook AI Research和Nyu Langone Health组织的2019 FastMRI挑战赛的膝盖MRI数据集的培训和测试。所有提交挑战的提交最初都是基于与已知地面图的相似性对挑战进行了排名，此后对前4个提交的提交进行了放射学评估。 FastMRI组织者在一个独立的挑战数据集上评估了我们的方法。它分别在8x加速的多线圈，4倍多线圈和4X单线圈轨道上分别排名＃1，共享＃1和＃3。这证明了该方法的出色性能和广泛的适用性。
Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We adopt deep neural networks to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil track. This demonstrates the superior performance and wide applicability of the method.
