Xie Huidong, Gan Weijie, Ji Wei, Chen Xiongchao, Alashi Alaa, Thorn Stephanie L, Zhou Bo, Liu Qiong, Xia Menghua, Guo Xueqi, Liu Yi-Hwa, An Hongyu, Kamilov Ulugbek S, Wang Ge, Sinusas Albert J, Liu Chi
Department of Biomedical Engineering, Yale University, United States of America.
Department of Computer Science & Engineering, Washington University in St. Louis, United States of America.
Med Image Anal. 2025 Jul 30;106:103729. doi: 10.1016/j.media.2025.103729.
Myocardial perfusion imaging using SPECT is widely utilized to diagnose coronary artery diseases, but image quality can be negatively affected in low-dose and few-view acquisition settings. Although various deep learning methods have been introduced to improve image quality from low-dose or few-view SPECT data, previous approaches often fail to generalize across different acquisition settings, limiting realistic applicability. This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning. Using both image and projection data, a consistency strategy is proposed to ensure that diffusion sampling at each step aligns with the low-dose/few-view projection measurements, the image data, and the scanner geometry, thus enabling generalization to different low-dose/few-view settings. Incorporating anatomical spatial information from CT and total variation constraint, we proposed a 2.5D conditional strategy to allow DiffSPECT-3D to observe 3D contextual information from the entire image volume, addressing the 3D memory/computational issues in diffusion model. We extensively evaluated the proposed method on 1,325 clinical Tc tetrofosmin stress/rest studies from 795 patients. Each study was reconstructed into 5 different low-count levels and 5 different projection few-view levels for model evaluations, ranging from 1% to 50% and from 1 view to 9 view, respectively. Validated against cardiac catheterization results and diagnostic review from nuclear cardiologists, the presented results show the potential to achieve low-dose and few-view SPECT imaging without compromising clinical performance. Additionally, DiffSPECT-3D could be directly applied to full-dose SPECT images to further improve image quality, especially in a low-dose stress-first cardiac SPECT imaging protocol.
使用单光子发射计算机断层扫描（SPECT）的心肌灌注成像被广泛用于诊断冠状动脉疾病，但在低剂量和少视图采集设置下，图像质量可能会受到负面影响。尽管已经引入了各种深度学习方法来从低剂量或少视图SPECT数据中提高图像质量，但以前的方法往往无法在不同的采集设置中通用，限制了实际适用性。这项工作引入了DiffSPECT-3D，一种用于3D心脏SPECT成像的扩散框架，它可以有效适应不同的采集设置，而无需进一步的网络重新训练或微调。利用图像和投影数据，提出了一种一致性策略，以确保在每个步骤的扩散采样与低剂量/少视图投影测量、图像数据和扫描仪几何形状对齐，从而能够推广到不同的低剂量/少视图设置。结合来自CT的解剖空间信息和总变差约束，我们提出了一种2.5D条件策略，使DiffSPECT-3D能够从整个图像体积中观察3D上下文信息，解决扩散模型中的3D内存/计算问题。我们在来自795名患者的1325例临床锝替曲膦酸盐负荷/静息研究中广泛评估了所提出的方法。每项研究被重建为5种不同的低计数水平和5种不同的投影少视图水平用于模型评估，分别从1%到50%以及从1视图到9视图。根据心脏导管检查结果和核心脏病专家的诊断审查进行验证，所呈现的结果表明在不影响临床性能的情况下实现低剂量和少视图SPECT成像的潜力。此外，DiffSPECT-3D可以直接应用于全剂量SPECT图像以进一步提高图像质量，特别是在低剂量负荷优先的心脏SPECT成像协议中。
