Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implemented to produce higher-quality images from stationary data. This is essentially a few-view imaging problem. In this work, we propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer. Then, given its reconstruction output and the original few-view reconstruction, we further refine the reconstruction using an image-domain reconstruction network. Validated by cardiac catheterization images, diagnostic interpretations from nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared clinical software, our method produced images with higher cardiac defect contrast on human studies compared with previous baseline methods, potentially enabling high-quality defect visualization using stationary few-view dedicated cardiac SPECT scanners.

翻译：心血管疾病（CVD）是全球主要死因，而采用SPECT的心肌灌注成像已广泛应用于CVD诊断。GE 530/570c专用心脏SPECT扫描仪采用固定几何结构同时采集19个投影，以提高灵敏度并实现动态成像。然而，有限的角采样数量会对图像质量产生负面影响。深度学习方法可从固定数据中生成更高质量的图像，这本质上属于少视角成像问题。本文提出一种基于3D Transformer的新型双域网络TIP-Net，用于高质量三维心脏SPECT图像重建。该方法首先通过定制化的投影-图像域Transformer，直接从投影数据重建三维心脏SPECT图像，无需迭代重建过程。随后，结合该重建输出与原始少视角重建结果，通过图像域重建网络进一步优化重建质量。经心导管造影图像验证、核医学心脏专科医师的诊断解读以及FDA 510(k)认证临床软件对缺损大小的量化评估，本方法在人体研究中生成的图像相比现有基线方法具有更高的心脏缺损对比度，有望利用固定式少视角专用心脏SPECT扫描仪实现高质量缺损可视化。