WE‐AB‐204‐09: Respiratory Motion Correction in 4D‐PET by Simultaneous Motion Estimation and Image Reconstruction (SMEIR)

Purpose: In conventional 4D‐PET, images from different frames are reconstructed individually and aligned by registration methods. Two issues with these approaches are: 1) Reconstruction algorithms do not make full use of all projections statistics; and 2) Image registration between noisy images can Result in poor alignment. In this study we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) method for cone beam CT for motion estimation/correction in 4D‐PET. Methods: Modified ordered‐subset expectation maximization algorithm coupled with total variation minimization (OSEM‐ TV) is used to obtain a primary motion‐compensated PET (pmc‐PET) from all projection data using Demons derived deformation vector fields (DVFs) as initial. Motion model update is done to obtain an optimal set of DVFs between the pmc‐PET and other phases by matching the forward projection of the deformed pmc‐PET and measured projections of other phases. Using updated DVFs, OSEM‐ TV image reconstruction is repeated and new DVFs are estimated based on updated images. 4D XCAT phantom with typical FDG biodistribution and a 10mm diameter tumor was used to evaluate the performance of the SMEIR algorithm. Results: Image quality of 4D‐PET is greatly improved by the SMEIR algorithm. When all projections are used to reconstruct a 3D‐PET, motion blurring artifacts are present, leading to a more than 5 times overestimation of the tumor size and 54% tumor to lung contrast ratio underestimation. This error reduced to 37% and 20% for post reconstruction registration methods and SMEIR respectively. Conclusion: SMEIR method can be used for motion estimation/correction in 4D‐PET. The statistics is greatly improved since all projection data are combined together to update the image. The performance of the SMEIR algorithm for 4D‐PET is sensitive to smoothness control parameters in the DVF estimation step.