Generalizing Deep Learning Reconstruction for Free-Breathing and Quantitative MRI

项目总结/摘要 该项目的目标是提高定量磁共振成像的精度和分辨率 （MRI）。定量信息如组织松弛参数（例如，T1和T2）测量组织功能 并指示心脏、肝脏、大脑和其他器官中与疾病相关的变化。例如，T1变化可以 提供心肌弥漫性纤维化的证据，这可能是心脏病的信号。定量地图也是 可重复，纵向和跨受试者直接可比，受药物性质的影响较小。 与普通加权（非定量）临床成像相比，使用扫描仪。但是， 成像涉及更复杂和耗时的脉冲序列。为了实现这一目标，该项目 将开发新的机器学习算法，用于从自由呼吸数据中进行高质量的参数映射。 该项目的第一个目标将提高从高加速，噪声， 数据所提出的方法将现有的基于深度级联网络的图像重建与 用于超分辨率和参数图估计的基于卷积网络的块。初步研究 建议这些新的块提高清晰度并减轻重构参数图中的伪像。 下一个目标是提高这种人工神经网络的训练精度，以解释显着的 定量MRI中的每体素非线性拟合变异性。所提出的方法将重新加权使用的损失函数 用于通过所获得的参考图的拟合优度（确定系数）来校准这些网络 完全采样的训练数据。初步结果表明，质量意识的重新加权显着 提高了使用噪声训练数据时的重建图像质量。实验将评估 这两项创新与现有的基于深度学习的T1地图重建相比， 对比前后的心脏图像。 最终目标是通过估计和跟踪数据中的非刚性运动来解决采集期间的运动 深度级联人工神经网络架构的一致性阶段。提出了两种方法： - 已经在基于压缩模型的图像重建上证明的可变形运动估计，以及 一种新的“重新模糊”卷积神经网络，可以自动将伪影引入“干净”图像， 匹配运动损坏的数据这两种方法都可以在受运动影响的数据之间实现一致性， 在重建过程中的无运动图像。这两种方法将在心脏和腹部进行验证 图像的运动伪影和重建质量与屏气参数标测采集。

Project Summary/Abstract The goal of this project is to increase the precision and resolution of quantitative magnetic resonance imaging (MRI). Quantitative information such as tissue relaxation parameters (e.g., T1 and T2) measure tissue function and indicate disease-related changes in the heart, liver, brain, and other organs. For instance, T1 changes can provide evidence of diffuse fibrosis in the myocardium that can signal heart disease. Quantitative maps also are reproducible, directly comparable longitudinally and across subjects, and less affected by the properties of the scanner used, when compared versus common weighted (non-quantitative) clinical imaging. But, quantitative imaging involves more complicated and time-consuming pulse sequences. To accomplish this goal, this project will develop new machine learning algorithms for high-quality parameter mapping from free-breathing data. The first aim of this project will increase parameter map resolution achievable from highly accelerated, noisy data. The proposed method will integrate existing deep cascade network-based image reconstructions with convolutional network-based blocks for super-resolution and parameter map estimation. Preliminary studies suggest these new blocks improve sharpness and mitigate artifacts in the reconstructed parameter maps. The next aim will improve the training precision of such artificial neural networks to account for the significant per-voxel nonlinear fit variability in quantitative MRI. The proposed method will reweight the loss function used for calibrating these networks by the goodness-of-fit (coefficient of determination) of the reference maps obtained from fully sampled training data. Preliminary results demonstrate that quality-aware reweighting significantly improves reconstructed image quality when working with noisy training data. Experiments will evaluate the precision of both of these innovations against existing deep-learning-based reconstructions on T1 maps obtained from pre- and post-contrast cardiac images of volunteer patients. The final aim will address motion during the acquisition by estimating and tracking nonrigid motion in the data consistency stages of the deep cascade artificial neural network architecture. Two methods are proposed: deformable motion estimation already demonstrated on compressive model-based image reconstructions, and a new “re-blurring” convolutional neural network that automatically introduces artifacts into a “clean” image to match the motion-corrupted data. Both of these methods enforce consistency between motion-affected data and a motion-free image during the reconstruction. Both methods will be validated on both cardiac and abdominal images for motion artifacts and reconstruction quality against breath-held parameter mapping acquisitions.