Motion-Compensated PET Image Reconstruction via Separable Parabolic Surrogates

The effective resolution of positron emission tomography (PET) can be significantly degraded by patient motion during data acquisition. This is especially true in the thorax due to respiratory motion. study concentrates on improvement correction algorithms both terms image quality and computational cost. In this paper, we present a novel motion-compensated reconstruction (MCIR) algorithm based parabolic surrogate likelihood function instead loglikelihood expectation maximization (EM) algorithm. theoretical advantage lies within fact that its upper bounded EM loglikelihood, thus it will converge faster than EM. particular importance PET correction, where reconstructions are very computationally demanding. Relaxation parameters were also introduced closer maximum (ML) solution achieve lower noise levels. Image with embedded relaxation actually converged better solutions corresponding ones without relaxation. Motion-compensated surrogates indeed shown accelerate convergence compared EM, reaching limit cycle. Nonetheless, incorporation ordered subsets setting, was less evident.