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 TOFPET 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 real patient study, we demonstrate that the proposed method outperforms the conventional MLAA.
Bone cancer patient study: (a) (i) Dixon-based In-phase MR image, and attenuation images using (ii) the conventional MLAA, (iii) the Dixon-based attenuation mapping and (iiii) the proposed method. (b) Activity images (i) before and after attenuation correction using (ii) the conventional MLAA, (iii) Dixon-based synthetic CT (MRAC) and (iiii) the proposed method.
K. Kim, Y. D. Son, G. El Fakhri and Q. Li, Penalized direct estimation using joint similarity of kinetic images with partial dynamic data, Society of Nuclear Medicine and Molecular Imaging (SNMMI), June. 2017
K. Kim, G. El Fakhri and Q. Li, Direct Parametric Imaging using Partial Dynamic Data, IEEE Nuclear Science Symposium and Medical Imaging Conference (NSSMIC), Nov. 2016.