Maximum posteriori reconstruction method of PET (positron emission tomography) image based on generalized entropy and MR (magnetic resonance) prior

Abstract

The invention discloses a maximum posteriori reconstruction method of a PET (positron emission tomography) image based on generalized entropy and MR (magnetic resonance) prior. The method comprises the following steps: (1) acquiring detection data before PET imaging by PET imaging equipment, meanwhile, obtaining various data correction parameter values in the imaging equipment and a system matrixof the imaging equipment; (2) constructing a mathematic statistical model for reconstructing the PET image; (3) obtaining a PET initial value image by a maximum likelihood method on the basis of mathematic statistical model solution; (4) registering the previously obtained MR image and the PET initial value image; (5) introducing anatomical prior by virtue of the generalized entropy and the registered MR image, carrying out reconstruction model transformation on the mathematic statistical model of the PET image by adopting a maximum posteriori method so as to obtain an optimization equation with a constraint objective function; and (6) carrying out iterative computation by a one-step-behind algorithm so as to finally obtain the final reconstructed medical image. By adopting the method, the visual effect and the quantitative index of the PET reconstructed image can be improved.

Description

technical field [0001] The invention relates to an image reconstruction method of medical images, in particular to a PET image maximum posterior reconstruction method based on generalized entropy and MR prior. Background technique [0002] Positron emission tomography (PET) is a non-invasive clinical tool for quantitative study of functional activity in vivo. PET reconstruction is an ill-conditioned problem due to low spatial resolution and inherent system noise. Statistical image reconstruction methods, such as maximum likelihood-expectation maximization (ML-EM), can better consider the physical effects of the system model and can establish mathematical models for the statistical characteristics of detection data and noise. The image quality of iterative reconstruction is better than the traditional analytical reconstruction algorithm represented by Filtered Back Projection (FBP) method. However, the ML-EM method is accompanied by the checkerboard effect caused by image q...

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Problems solved by technology

However, the ML-EM method is accompanied by the checkerboard effect caused by image quality degradation during the iterative process, resulting in a non-convergent iterative process

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