Li Kaiyan, Kc Prabhat, Li Hua, Myers Kyle J, Anastasio Mark A, Zeng Rongping
Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
ArXiv. 2025 Jan 16:arXiv:2501.09224v1.
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.
医学成像系统通常通过使用图像质量（IQ）的客观测量方法来进行评估和优化。长期以来，一直提倡将作用于成像测量的理想观察者（IO）的性能作为指导成像系统优化的品质因数。对于计算机成像系统，作用于成像测量的IO的性能也为任务性能设定了一个上限，任何图像重建方法都无法超越。因此，估计IO性能在设计欠采样数据采集技术时可提供有价值的指导，通过识别那些无论重建方法多么先进或重建图像看起来多么合理，都不允许为指定任务重建诊断不适当图像的设计。由于基于深度学习的图像重建方法的医疗器械提交量大幅增加，以及当数据被积极欠采样时它们可能产生掩盖诊断信息潜在损失的清晰图像这一事实，对这种分析的需求变得紧迫。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）来估计数据空间IO的性能，以建立图像重建的基于任务的性能界限。在这项工作中，探索了这种数据空间CNN-IO分析在多线圈磁共振成像（MRI）系统中的应用。本研究利用风格化的多线圈灵敏度编码（SENSE）MRI系统和深度生成的随机脑模型来演示该方法。选择信号已知统计量和背景已知统计量（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。
