Direct respiratory motion correction of whole-body PET images using a deep learning framework incorporating spatial information

Abstract

Breathing can cause blurring and artifacts in PET images, especially in the body trunk region. The blurring and artifacts can negatively impact cancer detection and response to therapy assessment. Many motion detection techniques, such as using external motion sensors or data driving methods, have been used to facilitate respiratory motion correction for PET. These methods require sophisticated gating or motion compensated image reconstruction, which are time consuming. In our work, we propose a deep learning framework based on U-Net to directly perform respiratory motion correction for whole-body PET in the image domain. Our framework was trained with the patches of the PET images without motion correction as input and the motion-corrected images using the data-driving gating (DDG) method as label. The framework also incorporated spatial information of voxels by adding additional layer of voxels’ vertical locations to the input. To evaluate our framework, we conducted 5-fold cross validations to generate the motion-corrected images for 30 subjects and compared them with the ground truth images corrected by the DDG methods. Our framework could correct the PET images in regions affected by respiratory motion. The incorporation of voxels’ spatial information could further improve the performance of motion correction. There is a great potential of our framework to perform direct respiratory motion correction in the image domain in a convenient manner.