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

项目摘要 磁共振成像（MRI）是一种多功能的成像方式，其具有缓慢的成像速度， 采集时间，这对于时间敏感型应用和患者来说都是一个挑战 吞吐量加速MRI将通过减少患者需要的时间来使患者受益 在扫描仪和降低医疗成本方面。这个项目是一个更大的 在保持诊断质量的同时加速MRI的科学努力。加速度，甚至 两倍的增长，将为公共卫生带来重大进步。目前的两个 加速MRI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 但是可能遭受难以表征的伪像，并且可能 有时类似于解剖学。具体而言，该项目将优化性能 包括欠采样模式和深度学习 重建，检测和定位细微病变。开展这项 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： 心理物理学研究，人类执行检测任务。第一个目标是 项目，我们将应用和开发检测任务和模型观测器。我们会考虑 包括一维和二维子采样的MRI中的欠采样采集策略 使用深度学习重建来增强数据一致性的方法。我们将 开发解剖背景中信号的检测任务，其中信号位置是 当观察者需要搜索信号时，人与机器 将对这些任务中的性能进行建模。第二个目标是优化数据 利用具有观测器模型的信号检测的捕获和神经网络重构 和心理物理实验我们还将引入一个基于可检测性的损失函数 到神经网络重建最近有兴趣探索低/中 场MRI，其具有与更高噪声的折衷。在第三个目标中，我们将评估效果 对信号检测的影响我们将使用来自高场采集的数据， 公开可用的数据库来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， 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.