Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image. However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT. To address this issue, we use concepts from the literature on model observers and our understanding of the human visual system to propose a DL-based denoising approach designed to preserve observer-related information for detection tasks. The proposed method was objectively evaluated on the task of detecting perfusion defect in myocardial perfusion SPECT images using a retrospective study with anonymized clinical data. Our results demonstrate that the proposed method yields improved performance on this detection task compared to using low-dose images. The results show that by preserving task-specific information, DL may provide a mechanism to improve observer performance in low-dose myocardial perfusion SPECT.

翻译：基于深度学习（DL）的方法在降低低剂量心肌灌注SPECT图像噪声方面展现出显著潜力。这类方法在临床应用前，必须通过临床任务评估其有效性。传统方法通常以某种保真度准则为目标，使预测的去噪图像与标准剂量参考图像之间的差异最小化。然而研究表明，这些方法虽具潜力，但在提升SPECT临床任务性能方面效果有限。针对这一问题，本文借鉴模型观察者领域的理论及对人类视觉系统的认知，提出了一种基于深度学习的去噪方法，旨在为检测任务保留与观察者相关的信息。通过回顾性临床匿名数据研究，该方法在心肌灌注SPECT图像灌注缺损检测任务中进行了客观评估。结果表明，与直接使用低剂量图像相比，本方法在该检测任务中取得了更优性能。研究证实，通过保留任务特异性信息，深度学习可有效提升低剂量心肌灌注SPECT中观察者的检测性能。