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至关重要。临床MRI扫描仪使用 固定的连续数据采样模式，采集时间长，并采用非自适应重建算法 来生成图像。收购通常不是针对特定的临床任务和患者特征， 导致次优图像;它们通常分辨率低、模糊或包含可能降低其诊断准确性的错误 效率。动态成像应用，其中必须快速捕获许多图像以描绘器官的运动 例如心脏，往往遭受这些不良影响最多。我们建议取代传统的动态MRI 使用基于机器学习的采集系统进行采集，其中数据采样得到有效优化 通过重建方法和任务预测，优化图像质量和临床任务性能。第一、 我们将探索和比较学习MRI帧的快速采样以优化图像重建的不同方法 使用大型公共数据集和当前复杂（迭代）重建算法的质量度量。我们会- 确定在高数据欠采样下实现最佳图像重建质量的采样学习策略 因素其次，我们将进一步扩展机器学习在整个MRI管道中的应用，并开发联合应用的方法。 数据采集和图像重建的适配以及最终任务（例如，量化任务）预测器作为 好.一种关键方法将使用（当前帧的）高度欠采样的初始采集和/或过去（帧）数据作为输入 以快速预测患者和帧自适应优化的采样模式。则 来自扫描仪的样本将用于快速生成机器学习重建，然后进行任务预测。 特别地，对于动态MRI，将有效地合并来自先前图像（帧）的时间信息 并在提出的机器学习模型中加以利用，以推动有效的实时自适应采集和检索， 选择。我们提出的数学公式和算法框架来实现这些目标。发达 基于学习的方法将在图像质量度量（例如， 均方根误差）和动态心脏MRI任务性能（射血分数估计）， 脉冲或加速率，并使用现有的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，注入学习整个成像管道可以实现快速， 用于动态心脏MRI和相关应用的有效任务驱动自适应成像。这种机器学习的核磁共振成像 该系统可以通过帮助实现成像系统和采集， 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，以确定和分析的潜力，鲁棒性，和算法的行为 提出的机器学习动态MRI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.