Yu Zitong, Rahman Md Ashequr, Jha Abhinav K
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12035. doi: 10.1117/12.2613134. Epub 2022 Apr 4.
Multiple objective assessment of image-quality (OAIQ)-based studies have reported that several deep-learning (DL)-based denoising methods show limited performance on signal-detection tasks. Our goal was to investigate the reasons for this limited performance. To achieve this goal, we conducted a task-based characterization of a DL-based denoising approach for individual signal properties. We conducted this study in the context of evaluating a DL-based approach for denoising single photon-emission computed tomography (SPECT) images. The training data consisted of signals of different sizes and shapes within a clustered-lumpy background, imaged with a 2D parallel-hole-collimator SPECT system. The projections were generated at normal and 20% low-count level, both of which were reconstructed using an ordered-subset-expectation-maximization (OSEM) algorithm. A convolutional neural network (CNN)-based denoiser was trained to process the low-count images. The performance of this CNN was characterized for five different signal sizes and four different signal-to-background ratio (SBRs) by designing each evaluation as a signal-known-exactly/background-known-statistically (SKE/BKS) signal-detection task. Performance on this task was evaluated using an anthropomorphic channelized Hotelling observer (CHO). As in previous studies, we observed that the DL-based denoising method did not improve performance on signal-detection tasks. Evaluation using the idea of observer-study-based characterization demonstrated that the DL-based denoising approach did not improve performance on the signal-detection task for any of the signal types. Overall, these results provide new insights on the performance of the DL-based denoising approach as a function of signal size and contrast. More generally, the observer study-based characterization provides a mechanism to evaluate the sensitivity of the method to specific object properties, and may be explored as analogous to characterizations such as modulation transfer function for linear systems. Finally, this work underscores the need for objective task-based evaluation of DL-based denoising approaches.
基于多目标图像质量评估（OAIQ）的研究报告称，几种基于深度学习（DL）的去噪方法在信号检测任务上表现有限。我们的目标是探究这种有限性能的原因。为实现这一目标，我们针对单个信号特性对基于DL的去噪方法进行了基于任务的特征描述。我们在评估一种基于DL的单光子发射计算机断层扫描（SPECT）图像去噪方法的背景下开展了这项研究。训练数据由聚集块状背景内不同大小和形状的信号组成，使用二维平行孔准直器SPECT系统成像。投影在正常和低20%计数水平下生成，两者均使用有序子集期望最大化（OSEM）算法重建。训练了一个基于卷积神经网络（CNN）的去噪器来处理低计数图像。通过将每次评估设计为信号精确已知/背景统计已知（SKE/BKS）信号检测任务，对该CNN在五种不同信号大小和四种不同信背比（SBR）下的性能进行了特征描述。使用拟人化通道化霍特林观察者（CHO）评估该任务的性能。与之前的研究一样，我们观察到基于DL的去噪方法在信号检测任务上并未提高性能。使用基于观察者研究的特征描述理念进行的评估表明，基于DL的去噪方法在任何信号类型的信号检测任务上均未提高性能。总体而言，这些结果为基于DL的去噪方法作为信号大小和对比度函数的性能提供了新的见解。更一般地说，基于观察者研究的特征描述提供了一种评估方法对特定对象属性敏感性的机制，并且可以像对线性系统的调制传递函数等特征描述一样进行探索。最后，这项工作强调了对基于DL的去噪方法进行基于客观任务评估的必要性。
