A monotonie image-space algorithm for joint PET image reconstruction and motion estimation

Motion compensation in PET imaging has become more and more important for obtaining high-resolution images. PET emission image and patient motion can be estimated simultaneously from gated data through a joint estimation framework. The resulting optimization problem, however, is challenging to solve. We propose an efficient algorithm for joint estimation by using the optimization transfer with the expectation maximization (EM) surrogate function. Each iteration of the algorithm consists of three separable steps: a gated image reconstruction by the EM update, motion estimation by image registration and image fusion with motion compensation. This algorithm resembles an empirical image-space approach, but guarantees to converge monotonically. Results from a computer simulation showed the proposed algorithm is faster than an existing monotonic gradient algorithm and is more stable than its nonmonotonic variant.