Machine Learning-Based Adaptation of Data Sampling and Reconstruction for Efficient Dynamic MRI

摘要 磁共振成像(MRI)对于疾病的检测和诊断是必不可少的。临床核磁共振扫描仪使用 fi摒弃了长采集时间的顺序数据采样模式，并采用非自适应重建算法 来生成图像。采集通常不是针对特定的fi临床任务和患者特征而定制的， 导致次优图像；它们通常是低分辨率、模糊或包含错误，这会降低其诊断能力 EFfiCacy.动态成像应用程序，其中必须快速捕获许多图像以描述器官的运动 例如心脏，往往受到这些不良影响的影响最大。我们建议取代传统的动态核磁共振成像 使用基于机器学习的采集系统进行采集，其中的数据采样被合理地优化在一起 结合重建方法和任务预测，优化图像质量和临床任务绩效。第一, 我们将探索和比较不同的学习MRI帧快速采样的方法来优化图像重建 使用大型公共数据集和当前复杂(迭代)重建算法的质量指标。我们会像- 确定在高欠采样情况下获得最佳图像重建质量的采样学习策略 各种因素。其次，我们将进一步将机器学习扩展到整个磁共振管道，并开发联合的方法 自适应的数据采集和图像重建以及最终的fi任务(例如，量化fi任务)作为预测器 井。一种关键方法将使用高度欠采样的(当前帧的)初始采集和/或过去(帧)数据作为输入 以快速预测患者和帧自适应的优化采样模式。然后是 来自扫描仪的样本将被用来快速产生机器学习的重建，然后是任务预测。 特别是，对于动态MRI，来自先前图像(帧)的时间信息将被有效地合并 并在建议的机器学习模型中加以利用，以推动有效的On-fi-fly自适应获取和重构- 特兹。我们提出了实现这些目标的数学公式和算法框架。已开发的 将在图像质量度量方面对基于学习的方法进行综合评估和交叉比较(例如， 均方根误差)和动态心脏MRI任务性能(射血分数估计)。 采样或加速速率，并使用现有数据集以及使用新收集的心脏MRI数据进行基准测试。 智能成像技术的发展将学习注入到成像管道中，可以实现快速和 有效的任务驱动的动态心脏核磁共振自适应成像及相关应用。这样的机器学习核磁共振 通过帮助实现成像系统和采集，该系统可以潜在地改进临床诊断和治疗 以实时适应，以高分辨率最佳地检测和成像各种特征。我们在这个项目中的目标是 进行初步的全面研究，以确定和分析以下各项的潜力、稳定性和算法行为 提出了机器学习动态磁共振成像的框架和技术。

ABSTRACT Magnetic resonance imaging (MRI) is essential for the detection and diagnosis of diseases. Clinical MRI scanners use fixed sequential data sampling patterns with long acquisition times, and employ nonadaptive reconstruction algorithms to generate images. The acquisitions are not usually tailored for the specific clinical task and patient characteristics, leading to sub-optimal images; they are often low-resolution, blurry, or contain errors that can reduce their diagnostic efficacy. Dynamic imaging applications, in which many images must be captured quickly to depict the motion of organs such as the heart, tend to suffer the most from these ill-effects. We propose to replace the conventional dynamic MRI acquisitions with a machine learning-based acquisition system, where the data sampling is efficiently optimized together with the reconstruction approach and task prediction, for optimized image quality and clinical task performance. First, we will explore and compare different ways of learning fast sampling of MRI frames to optimize image reconstruction quality metrics using large public data sets and current sophisticated (iterative) reconstruction algorithms. We will as- certain the sampling learning strategies that achieve the best image reconstruction quality at high data undersampling factors. Second, we will further extend machine learning throughout the MRI pipeline and develop approaches for joint adaptation of the data acquisition and image reconstruction and finally the task (e.g., quantification task) predictor as well. A key approach will use highly undersampled initial acquisitions (of current frame) and/or past (frame) data as input to the learned acquisition model to rapidly predict a patient- and frame-adaptive optimized sampling pattern. Then the samples from the scanner will be used to rapidly produce machine-learned reconstructions followed by task predictions. Particularly, for dynamic MRI, the temporal information from preceding images (frames) will be effectively incorporated and exploited in the proposed machine-learned models to drive efficient on-the-fly adaptive acquisitions and reconstruc- tions. We propose the mathematical formulations and algorithmic framework to accomplish these goals. The developed learning-based methods will be comprehensively evaluated and cross-compared in terms of image quality metrics (e.g., root mean squared error) and dynamic cardiac MRI task performance (ejection fraction estimation) at several undersam- pling or acceleration rates, and benchmarked using existing data sets as well as using newly collected cardiac MRI data. The development of smart imaging technologies that infuse learning across the imaging pipeline could enable rapid and effective task-driven adaptive imaging for dynamic cardiac MRI and related applications. Such a machine-learning MRI system could potentially improve clinical diagnosis and treatment, by helping enable the imaging system and acquisition to adapt in real-time to optimally detect and image various features at high resolution. Our goal in this project is to conduct the initial comprehensive studies to determine and analyze the potential, robustness, and algorithm behavior of the proposed machine learning dynamic MRI framework and techniques.