There is an important need for methods to reduce radiation dose and imaging time in myocardial perfusion imaging (MPI) SPECT. Deep learning (DL) methods have demonstrated promise in predicting normal-count images from low-count images for MPI SPECT, but the methods that have been objectively evaluated on the clinical task of detecting perfusion defects have not shown improved performance compared with low-count images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific DL-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that are known to impact observer performance on detection tasks. We objectively evaluated the proposed method on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies (N = 338). Performance on the task of detecting perfusion defects was quantified with an anthropomorphic channelized Hotelling observer. Images denoised with DEMIST yielded significantly improved detection performance compared to the corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, the proposed method significantly improved performance compared to the low-dose images in terms of the task-agnostic metrics of root mean squared error and structural similarity index metric. A mathematical analysis reveals that DEMIST preserves detection-task-specific features while improving the noise properties, thus resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

翻译：在心肌灌注成像（MPI）SPECT中，迫切需要降低辐射剂量和缩短成像时间的方法。深度学习（DL）方法在从低计数图像预测正常计数图像方面已显示出潜力，但经过客观评估的、针对检测灌注缺损临床任务的方法，其性能并未优于低计数图像。针对这一需求，我们基于模型观察者理论及对人视觉系统的理解，提出了一种检测任务特异性深度学习去噪方法（DEMIST）用于MPI SPECT图像处理。该方法在执行去噪的同时，旨在保留已知影响观察者检测任务性能的特征。我们基于338例接受MPI检查患者的匿名临床数据，通过回顾性研究对提出的方法在灌注缺损检测任务上进行了客观评估。使用拟人化信道化Hotelling观察者量化了检测灌注缺损任务的性能。与相应低剂量图像及常用任务无关深度学习去噪方法处理的图像相比，DEMIST去噪后的图像在检测性能上显著提升。按患者性别和缺损类型进行分层分析后观察到类似结果。此外，在任务无关指标——均方根误差和结构相似性指数方面，该方法相较于低剂量图像显著提升了性能。数学分析表明，DEMIST在改善噪声特性的同时保留了检测任务特异性特征，从而提升了观察者性能。该结果为在MPI SPECT低计数图像去噪中进一步临床评估DEMIST提供了有力依据。