Penalized MLAA with spatially-encoded anatomic prior in TOF PET/MR

Abstract

PET/MR scanner has been developed for both molecular and morphological assessment with great potentials. In the PET/MR scan, the attenuation correction is still a problem. One method is the MR-based attenuation correction that generates the synthetic CT images from MR images. However, the lack of bone signal and the bias from the synthetic CT image can degrade the PET image quality. Another method is a maximum likelihood reconstruction of activity and attenuation (MLAA) using the time-of-flight (TOF) PET emission data, however, the noise component is considerably high from TOF PET data. To address this issue, we propose a penalized MLAA using a spatially-encoded anatomic MR prior, which jointly use a patch-based spatially-encoded similarity weight of MR image to improve the attenuation image quality. In addition, we propose a non-divergence criteria using a consistency condition in the iterative process. We exploit an alternating direction method of multipliers (ADMM) algorithm to optimize the cost function. In computer simulations, we demonstrate that the proposed method outperform the conventional MLAA.