Ultrafast Elastic Motion Correction via Motion Deblurring

Patient motion during PET studies degrades image quality. Some types of motion (e.g. brain) can be modeled as rigid-body transformations, whereas others (e.g. respiratory and cardiac), are more complex, involve deformations of the imaged organs, and require Elastic Motion Correction (EMC). The conventional way (cEMC) to handle the dense information needed for EMC is to divide the acquired data into multiple respiratory, cardiac, or dual “gates”, where motion is minimal within each gate. Motion fields can then be calculated between a reference gate and all other gates via optical flow. These motion fields can then be used in a cEMC iterative reconstruction process by warping the reference image to each gated image before forward projection and transposing the gated correction factors back to the reference image after backward projection. In this algorithm, the number of forward and backprojections, processing time, and memory requirements are proportional to the number of gates. In this paper, we introduce a faster algorithm, Elastic Motion Deblurring (EMDB), which does not depend on the number of gates. Instead, a Mass Preservation Optical Flow (MPOF) algorithm is used to calculate a blurring kernel from the reference gate to the static (motion blurred) image only. This novel approach reduces the processing time and hardware requirements for iterative EMC reconstruction.