Motion correction of respiratory-gated PET images using deep learning based image registration framework

PURPOSE Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning for motion correction. METHODS Our network uses a differentiable spatial transformer layer to warp the moving image to the fixed image and use a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and implemented an iterative image registration for comparison. Motion compensated reconstructions were compared with ungated images. RESULTS Our simulation study showed that the motion compensated methods can generate images with sharp boundaries and reveal more details in the heart region compared with the ungated image. The resulting normalized root mean square error (NRMS) was 24.3±1.7% for the deep learning based motion correction, 31.1±1.4% for the iterative registration based motion correction, and 41.9±2.0% for ungated reconstruction. The proposed deep learning based motion correction reduced the bias compared with the ungated image without increasing the noise level and outperformed the iterative registration based method. In the real data study, both motion compensated images provided higher lesion contrast and sharper liver boundary than the ungated image and had lower noise than the reference gate image. The contrast of the proposed method based on the deep neural network was higher than the ungated image and iterative registration method at any matched noise level. CONCLUSIONS In this work, we proposed a motion correction method for respiratory-gated PET images using deep learning based image registration framework. It does not require the knowledge of the true deformation field for training the network, which makes it very convenient to implement. We validated the proposed method using simulation and clinical data and showed its ability to reduce motion artifacts while utilizing all gated PET data.