用学习到的理想观测者评估MRI重建上限，防止深度学习生成误导性图像。
Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers
- 构建卷积神经网络模拟理想观测者，评估不同采样率下的诊断性能极限
- 在多线圈MRI系统中验证，加速因子提升至4时诊断能力显著下降
- 适合关注AI重建可靠性与数据采样设计的医学影像研究者
医学成像系统通常通过图像质量客观指标进行评估与优化。理想观测者（IO）作用于成像测量结果的性能长期以来被视为指导成像系统优化的关键指标。对于计算成像系统，作用于测量数据的IO性能设定了任何图像重建方法都无法超越的任务性能上限。因此，估算IO性能可为设计欠采样数据采集方案提供重要指导，帮助识别即使采用最先进的重建方法也无法恢复诊断有效信息的设计——即便重建图像看起来清晰。这一分析尤为紧迫，因基于深度学习的图像重建方法提交量激增，而极端欠采样可能导致图像虽美观却丢失关键诊断信息。近期已有研究利用卷积神经网络近似理想观测者（CNN-IO），在X射线计算机断层成像（CT）场景下估算数据空间理想观测者性能以建立任务性能边界。本文将该数据空间CNN-IO分析方法拓展至多线圈磁共振成像（MRI）系统，采用简化的多线圈灵敏度编码（SENSE）模型和深度生成的随机脑模态，研究信号已知统计（SKS）与背景已知统计（BKS）二分类检测任务下不同加速因子对数据空间理想观测者性能的影响。
原文摘要 · Abstract (English)
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.
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