Accurate Attenuation Correction of TOF-PET/MR Scanner using Both MLAA and Anatomic Prior

205 Objectives The attenuation correction of time-of-flight (TOF) PET/MR scanner can be computed either by maximum likelihood activity and attenuation reconstruction (MLAA) using TOF information, or by MR-based attenuation correction. Although the MLAA algorithm has been theoretically presented and validated, the quality of estimated transmission image is not always acceptable depending on the amount of emission counts in a region. On the other hand, MR-based attenuation correction is known for the large bias in certain tissue and bone. To address above challenging issues, we propose a MLAA algorithm with MR-based anatomical prior to accurately estimation both emission and attenuation image using the TOF information as well as the high resolution MR image. We also compared the performance with the conventional MLAA using only TOF PET data, attenuation image derived from Dixon-based MR images, and the MLAA method using MR-based anatomical prior. Methods The MLAA with MR-prior iterates two steps: one step is the estimation of activity image using TOF-based separable quadratic surrogate (SQS) with a quadratic regularization; and another step is the estimation of attenuation image using the maximum likelihood transmission (MLTR) and the MR-prior based regularization with a patch-based non-local weight. Specifically, the non-local weight calculates the similarity between patches in the MR image, and the corresponding similarity weight is used to estimate the attenuation image. In this framework, we can also use multiple MR images in the prior to facilitate the estimation of attenuation image. For example, we used Dixon-based fat, in-phase, water MR images in our patient study. We exploit an alternating direction method of multipliers (ADMM) algorithm to optimize the cost function with multiple regularization terms. To evaluate the performance of the proposed method, both simulation data with a XCAT phantom and a patient data (male, 56 years old) with lung cancer and bone metastases were used. The proposed method demonstrate superior image quality in simulation, compared with traditional methods. In patient study, the GE SIGNA TOF-PET/MR scanner with 420 ns time resolution is used. We compared the qualities of attenuation images using the conventional MLAA, Dixon-based mapping and the proposed method as well as the emission images after attenuation correction. We also compared the contrast between tumor region and background region in the final emission images of these methods. Results In comparison of attenuation images in figure 1(a), the attenuation image of conventional MLAA is very noisy. It also shows artifacts, such as additional intensity in the air and missing intensity in the chest wall. Dixon-based attenuation image shows a clear result, however, the attenuation values are piecewise constant. In the MLAA with a MR-prior, the image quality is significantly better than the conventional MLAA, and we can see details clearly. In figure 1(b), we compared activity images before and after attenuation correction using the conventional MLAA, Dixon-based mapping and the proposed method. The proposed method shows superior image quality, particularly in the bone region that is important for this patient with bone metastases. More specifically, we confirmed that the tumor-to-muscle ratio of the proposed method is higher than other methods as shown in figure 1(c). Conclusions The MR-prior based MLAA is successfully developed and validated using both simulation and patient study. The results indicated that it can achieve superior quality in both emission and attenuation images because of the utilization of the TOF information as well as the MR information in the estimation.