(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used ^99mTc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland–Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [−9.04; 9.00]%, and for the segment level, they were [−11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [−2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners.

La Tomografía por Emisión de Positrones (PET) permite cuantificar el metabolismo celular, siendo una de las técnicas más poderosas en el diagnóstico precoz de enfermedades cardíacas, oncológicas y neurológicas. Las imágenes se obtienen mediante la administración de un radiofármaco que marca el proceso metabólico a estudiar. Los rayos gamma que emite el radiotrazador, inciden en detectores ubicados alrededor del paciente generando eventos que se almacenan para la reconstrucción de la imagen. Para obtener imágenes cuantificables, se realizan diversas correcciones a los eventos, entre ellas por eventos dispersos. Con el objetivo de implementar la corrección por eventos dispersos, se realiza, en primera instancia, el modelado de los mismos por medio del algoritmo Single Scatter Simulation (SSS) obteniendo una estimación de la dispersión. Posteriormente se realiza la corrección en la imagen de emisión. Para la corrección se incorpora al algoritmo de reconstrucción MLEM (maximum-likelihood expectation–maximization) la estimación obtenida con el modelado. Los resultados preliminares, obtenidos en simulaciones de volúmenes de actividad constante, han demostrado que la implementación realizada mejora la homogeneidad de la imagen de emisión.