Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection

项目总结 磁共振成像(MRI)是一种多用途的成像方式，但速度慢 获取时间，这对于时间敏感型应用和患者来说都是一项挑战 吞吐量。加速核磁共振成像将使患者受益，因为它可以减少患者需要的时间 在扫描仪和降低医疗保健成本方面。这个项目是一个更大的 在保持诊断质量的同时加快磁共振成像的科学努力。加速，甚至 两个因素，将导致公共卫生的重大进步。当前的两个 加速磁共振成像的方法依赖于收集更少的数据(欠采样)和深度学习 重建。这些方法可以使用显著的图像质量 采样不足，但可能会出现难以表征的伪影，并且可能 有时类似于解剖学。具体地说，该项目将优化性能 加速磁共振成像，包括欠采样模式和深度学习 重建，关于检测和定位细微的病变。要实现这一点 优化，我们将首先开发机器检测病变所需的方法 和人类观察者模型。人类观察者模型将通过以下方式进行验证 人类执行探测任务的心理物理学研究。在这个项目的第一个目标中 项目，我们将应用和开发探测任务和模型观察员。我们会考虑 磁共振成像中的欠采样捕获策略包括一维亚采样和二维亚采样 方法使用深度学习重建来加强数据一致性。我们会 开发解剖背景中信号的检测任务 已知以及观察者何时需要搜索信号。人和机器 将对这些任务的绩效进行建模。在第二个目标中，我们将优化数据 基于观测器模型的信号检测捕获和神经网络重构 和心理物理实验。我们还将引入基于可检测性的损失函数 神经网络重建。最近有兴趣探索低/中的好处 现场核磁共振，它有一个权衡与较高的噪音。在第三个目标中，我们将评估效果 信号检测的场强。我们将使用来自高场采集的数据 可公开使用的数据库，用于模拟来自较低磁场的图像。使用检测 对于细微病变，我们将评估不同场强下的检测性能。这 研究项目将有助于加强研究环境和扩大参与 在曼哈顿学院，让学生参与生物医学研究，将应用 数学、统计学和数据科学。

PROJECT SUMMARY Magnetic resonance imaging (MRI) is a versatile imaging modality that suffers from slow acquisition times, which is a challenge, for both time sensitive applications and for patient throughput. Accelerating MRI would benefit patients both by reducing the time they need to be in the scanner and in reducing the cost of healthcare. This project is part of a larger scientific effort to accelerate MRI while maintaining the diagnostic quality. Acceleration, even by a factor of two, would result in a major advance for public health. Two of the current approaches to accelerate MRI rely on collecting less data (under-sampling) and deep learning reconstruction. These approaches can lead to images with diagnostic quality using significant under-sampling but may suffer from artifacts which are hard to characterize and may sometimes resemble anatomy. Specifically, this project will optimize the performance of accelerated MRI, including undersampling patterns and deep learning reconstructions, on detecting and localizing subtle lesions. To carry out this optimization, we will first develop the methods required for detection of lesions by machine and human observer models. The human observer models will be validated by psychophysical studies where humans perform the detection task. In the first aim of this project, we will apply and develop detection tasks and model observers. We will consider under-sampled acquisition strategies in MRI including one and two-dimensional subsampling methods using deep learning reconstructions which enforce data consistency. We will develop detection tasks for signals in anatomical backgrounds were the signal location is known and when the observer needs to search for the signal. The human and machine performance in these tasks will be modeled. In the second aim, we will optimize data acquisition and neural network reconstruction using signal detection with observer models and psychophysical experiments. We will also introduce a detectability-based loss function to neural network reconstructions. There is recent interest in exploring the benefits of low/mid field MRI which has a trade-off with higher noise. In the third aim, we will evaluate the effect of field strength on signal detection. We will use data from high field acquisitions from a publicly available database to model images from lower magnetic fields. Using the detection of subtle lesions, we will evaluate detection performance with varying field strength. This research project will help to strengthen the research environment and broaden participation at Manhattan College by involving students in biomedical research incorporating applied mathematics, statistics and data science.