MRI-guided brain PET image filtering and partial volume correction

作者：Jianhua Yan, Jason Chu-Shern Lim, David William Townsend · 发表于：Physics in Medicine and Biology · 年份：2015 · DOI：10.1088/0031-9155/60/3/961 · 被引用次数：69 · 研究领域：Medical Imaging Techniques and Applications、Medical Image Segmentation Techniques、Advanced MRI Techniques and Applications

Positron emission tomography (PET) image quantification is a challenging problem due to limited spatial resolution of acquired data and the resulting partial volume effects (PVE), which depend on the size of the structure studied in relation to the spatial resolution and which may lead to over or underestimation of the true tissue tracer concentration. In addition, it is usually necessary to perform image smoothing either during image reconstruction or afterwards to achieve a reasonable signal-to-noise ratio. Typically, an isotropic Gaussian filtering (GF) is used for this purpose. However, the noise suppression is at the cost of deteriorating spatial resolution. As hybrid imaging devices such as PET/MRI have become available, the complementary information derived from high definition morphologic images could be used to improve the quality of PET images. In this study, first of all, we propose an MRI-guided PET filtering method by adapting a recently proposed local linear model and then incorporate PVE into the model to get a new partial volume correction (PVC) method without parcellation of MRI. In addition, both the new filtering and PVC are voxel-wise non-iterative methods. The performance of the proposed methods were investigated with simulated dynamic FDG brain dataset and (18)F-FDG brain data of a cervical cancer patient acquired with a simultaneous hybrid PET/MR scanner. The initial simulation results demonstrated that MRI-guided PET image filtering can produce less no...