Liu Hui, Zhang Yajing, Lyu Zhenlei, Cheng Li, Gao Lilei, Wu Jing, Liu Yaqiang
Department of Engineering Physics, Tsinghua University Beijing, China.
Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education Beijing, China.
Am J Nucl Med Mol Imaging. 2025 Feb 25;15(1):15-27. doi: 10.62347/MLFB9278. eCollection 2025.
Single-photon emission computed tomography (SPECT) is widely used in myocardial perfusion imaging (MPI) in clinic. However, conventional dual-head SPECT scanners require lengthy scanning times and gantry rotation, which limits the application of SPECT MPI. In this work, we proposed a deep learning-based approach to reconstruct dual-view projections, aiming to reduce acquisition time and enable non-rotational imaging for MPI based on conventional dual-head SPECT scanners. U-Net was adopted for the dual-view projection reconstruction. Initially, 2D U-Nets were used to evaluate various data organization schemes for dual-view projection as input, including paved projection, interleaved projection, and stacked projection, with and without an attenuation map. Subsequently, we developed 3D U-Nets using the optimal data organization scheme as input to further enhance reconstruction performance. The dataset consisted of a total of 116 SPECT/CT scans with Tc-tetrofosmin tracer acquired on a GE NM/CT 640 scanner. Reconstruction performance was assessed using quantitative metrices and absolute percentage errors, while the reconstruction images from the full-view projection were used as reference images. The 2D U-Nets provided reasonable transverse view images but exhibited slight axial discontinuity compared to the reference images, regardless of the data organization schemes. Incorporating the attenuation map reduced this axial discontinuity. Quantitatively, the 2D U-Net trained using both stacked projection and attenuation map achieved the best performance, with a normalized mean absolute error of 0.6%±0.3% and a structural similarity index measure (SSIM) of 0.93±0.04. The 3D U-Net further improved the performance with less axial discontinuity and a higher SSIM of 0.94±0.03. The localized absolute percentage errors were 1.8±16.8% and -2.0±6.3% in the left ventricular (LV) cavity and myocardium, respectively. We developed a deep learning-based image reconstruction approach for dual-view projection from a conventional SPECT scanner. The 3D U-Net, trained with the stacked projection with an attenuation map is effective for non-rotational imaging and could benefit dynamic myocardium perfusion imaging.
单光子发射计算机断层扫描（SPECT）在临床心肌灌注成像（MPI）中被广泛应用。然而，传统的双头SPECT扫描仪需要较长的扫描时间和机架旋转，这限制了SPECT MPI的应用。在这项工作中，我们提出了一种基于深度学习的方法来重建双视图投影，旨在减少采集时间，并基于传统双头SPECT扫描仪实现MPI的非旋转成像。采用U-Net进行双视图投影重建。最初，使用二维U-Net来评估作为输入的双视图投影的各种数据组织方案，包括平铺投影、交错投影和堆叠投影，有无衰减图。随后，我们使用最优数据组织方案作为输入开发了三维U-Net，以进一步提高重建性能。数据集总共包括在GE NM/CT 640扫描仪上采集的116次使用锝-替曲膦示踪剂的SPECT/CT扫描。使用定量指标和绝对百分比误差评估重建性能，同时将全视图投影的重建图像用作参考图像。无论数据组织方案如何，二维U-Net都能提供合理的横断面图像，但与参考图像相比，轴向存在轻微的不连续性。加入衰减图减少了这种轴向不连续性。在定量方面，使用堆叠投影和衰减图训练的二维U-Net性能最佳，归一化平均绝对误差为0.6%±0.3%，结构相似性指数测量（SSIM）为0.93±0.04。三维U-Net进一步提高了性能，轴向不连续性更小，SSIM更高，为0.94±0.03。左心室（LV）腔和心肌的局部绝对百分比误差分别为1.8±16.8%和-2.0±6.3%。我们开发了一种基于深度学习的方法，用于从传统SPECT扫描仪进行双视图投影图像重建。用带有衰减图的堆叠投影训练的三维U-Net对于非旋转成像有效，并且有利于动态心肌灌注成像。
