Deep Learning for PET Image Reconstruction

Image Reconstruction Techniques for PET

200x Low-dose PET Reconstruction using Deep Learning

Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation exposure. To minimize this potential risk in PET imaging, efforts have been made to reduce the amount of radiotracer usage. However, lowing dose results in low ...

Deep reconstruction model for dynamic PET images

Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. I...

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...