标题： 基于学习的理想观察者估计加速磁共振成像图像重建方法的任务性能界限 显示英文标题

标题： Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers

摘要： 医学成像系统通常通过图像质量（IQ）的客观测量方法来进行评估和优化。长期以来，理想观察者（IO）在成像测量上的表现一直被提倡作为衡量指标，以指导成像系统的优化。对于计算成像系统而言，IO 在成像测量上的表现也设定了一个上限，任何图像重建方法都无法超越该上限。因此，在设计欠采样数据采集技术时，估计 IO 的性能可以提供有价值的指导，从而识别出不会允许生成对指定任务诊断不合适的图像的设计——无论重建方法多么先进或重建图像看起来多么可信。由于基于深度学习的图像重建方法的医疗设备提交量大幅增加，并且这些方法可能会产生干净的图像，掩盖了当数据被强烈欠采样时潜在的诊断信息损失，因此这种分析的需求非常迫切。 最近，在X射线计算机断层扫描（CT）背景下，研究了卷积神经网络（CNN）近似 IO（CNN-IO）来估算数据空间 IO 的性能，以建立图像重建的任务性能界限。在这项工作中，探索了将这种数据空间 CNN-IO 分析应用于多线圈磁共振成像（MRI）系统的方法。本研究利用风格化的多线圈敏感度编码（SENSE）MRI 系统和深度生成的随机脑模型来展示这种方法。选择了统计信号已知/背景已知（SKS/BKS）二元信号检测任务来研究不同加速因子对数据空间 IO 性能的影响。 显示英文摘要

摘要： Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing under-sampled data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically inappropriate images for a specified task - no matter how advanced the reconstruction method is or how plausible the reconstructed images appear. The need for such analysis is urgent because of the substantial increase of medical device submissions on deep learning-based image reconstruction methods and the fact that they may produce clean images disguising the potential loss of diagnostic information when data is aggressively under-sampled. Recently, convolutional neural network (CNN) approximated IOs (CNN-IOs) was investigated for estimating the performance of data space IOs to establish task-based performance bounds for image reconstruction, under an X-ray computed tomographic (CT) context. In this work, the application of such data space CNN-IO analysis to multi-coil magnetic resonance imaging (MRI) systems has been explored. This study utilized stylized multi-coil sensitivity encoding (SENSE) MRI systems and deep-generated stochastic brain models to demonstrate the approach. Signal-known-statistically and background-known-statistically (SKS/BKS) binary signal detection tasks were selected to study the impact of different acceleration factors on the data space IO performance.