DeTransUnet: attenuation correction of gated cardiac images without structural information

Abstract

Abstract Objective. Myocardial perfusion imaging (MPI) with positron emission tomography (PET) is a non-invasive imaging method, and it is of great significance to the diagnosis and prognosis of coronary heart disease. Attenuation correction (AC) for PET images is a necessary step for further quantitative analysis. In order not to use magnetic resonance (MR) or computed tomography (CT) images for AC, this work proposes DeTransUnet to obtain AC PET images directly from no-attenuation corrected (NAC) PET images. Approach. The proposed DeTransUnet is a 3D structure which combines the multi-scale deformable transformer layers and the 3D convolutional neural network (CNN). And it integrates the advantages of transformer with long-range dependence and CNN suitable for image calculation. The AC images using CT images for AC and scatter correction (SC) and are considered as training labels, while the NAC images are reconstructed without AC and SC. Standard uptake value (SUV) values are calculated for both NAC and AC images to exclude the influence of weight and injection dose. With NAC SUV images as the inputs of the DeTransUnet, the outputs of DeTransUnet are AC SUV images. Main results. The proposed DeTransUnet was performed on an MPI gated-PET dataset, and the results were compared with Unet2D and Unet2.5D. The metrics of the whole image and the left ventricular myocardium show that the proposed method has advantages over other deep learning methods. Significance. The proposed DeTransUnet is a novel AC framework that does not require CT or MR images. It can be used as an independent AC method on PET/MR instrument. In addition, when CT images contain defects or cannot be registered with PET images on PET/CT instrument, DeTransUnet is able to repair the defects and keep consistent with the NAC images.