PET Image Reconstruction Using Kernel Method

PET Image Reconstruction

Positron emission tomography (PET) scanners collect measurements of a patient’s in vivo radiotracer distribution. These measurements are reconstructed into cross-sectional images. Tomographic image reconstruction forms images of functional information in nuclear medicine applications and the same principles can be applied to modalities such as X-ray computed tomography. This chapter provides a ...

Bayesian PET image reconstruction

We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realist...

Reconstruction of Pet Image Based on Kernelized Expectation-maximization Method

Positron emission tomography (PET) image reconstruction is challenging for low count frame. The reconstruction is important method to retrieve information that has been lost in the images. To improve image quality, prior information are used. Based on kernel method, PET image intensity in each pixel is obtained from prior information and the coefficients can be estimated by the maximum likeliho...

Image Reconstruction Techniques for PET

Advances in PET Image Reconstruction.

Until recently, the most widely used methods for image reconstruction were direct analytic techniques. Iterative techniques, although computationally much more intensive, produce improved images (principally arising from more accurate modeling of the acquired projection data), enabling these techniques to replace analytic techniques not only in research settings but also in the clinic. This art...