Investigation of a deep learning-based reconstruction approach utilizing dual-view projection for myocardial perfusion SPECT imaging.

Abstract

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 ^99mTc-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.