Attenuation Correction for Myocardial Perfusion SPECT Imaging in the Image Domain

Attenuation correction (AC), as an essential component of quantitative SPECT imaging, is highly challenging in the absence of CT or transmission scanning in dedicated cardiac SPECT devices. This study investigated the potential of direct attenuation correction (AC) of
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Tc-sestamibi SPECT myocardial perfusion imaging (MPI) in the image domain using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT
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Tc-ses-tamibi images of 99 subjects were retrospectively used, considering non-attenuation corrected SPECT images as input and CT-based attenuation corrected (CT-AC) SPECT images as reference. Chang’s AC technique was also performed as a baseline for evaluating the performance of deep learning-based models. The quantitative accuracy of the attenuation corrected SPECT images was validated using the proposed methods considering the corresponding CT-AC SPECT images of 19 external datasets as reference. Image-derived metrics, including the voxel-wise mean absolute error (MAE), relative error (RE), structural similarity index measure (SSIM), as well as clinically relevant indices, such as total perfusion deficit (TPD), were calculated. The quantitative results obtained from the external validation dataset of the deep learning models exhibited good agreement with the reference CT-based attenuation corrected MPI-SPECT images. The deep learning models showed superior performance over Chang’s AC method. The ResNet and UNet models resulted in MAE of 20.24±17.63 and 13.65±11.23 and SSIM of 0.99±0.04 and 0.98±0.05, respectively. In comparison, Chang’s approach led to MAE and SSIM of 80.27±47.56 and 0.93±0.09, respectively. Similarly, the clinical evaluation showed a mean TPD of 12.78±9.22% and 12.57±8.93% for ResNet and UNet models, respectively, compared to TPD of 12.84±8.63% for reference SPECT CT-AC images. In contrast, Chang’s approach led to a mean TPD of 16.68±11.24%. The evaluation of deep learning approaches implemented in this work showed the promising potential of deep learning-based techniques in producing attenuation corrected MPI-SPECT images in the image domain without anatomical imaging.