Polyethylene glycol terephthalate (PET) reconstruction method based on sparsification and Poisson model

Abstract

The invention discloses a polyethylene glycol terephthalate (PET) reconstruction method based on a sparsification and Poisson model. The PET reconstruction method includes: firstly, acquiring projection data, determining image size range and pixel range, and calculating a system probability matrix; obtaining an initial image through a filtered back projection (FBP) traditional algorithm, and using an obtained log-likelihood function as a reconstruction recovery item; using a wavelet transformation and discrete cosine transformation (DCT) mixed base and weighting as sparse regularization constraint, decomposing an objective function by using a split Bregman method to obtain two sub-problems, using a first sub-problem as a sparse regularization problem under a Gaussian model, and using linear Bregman interation for solving; using a second sub-problem as a Poisson denoising problem, and using a close operator method for solving; operating the last one iterative formula, completing an integrated iteration, obtaining a reconstructed image, and serving as an initial value of the next iteration.

Description

technical field [0001] The invention relates to the field of positron emission computed tomography, in particular to a PET reconstruction method based on sparse and Poisson model. Background technique [0002] Positron Emission Tomography (PET) is a relatively advanced imaging technology for clinical examination in the field of nuclear medicine. It has been widely used in the diagnosis and differential diagnosis of various diseases, condition judgment, curative effect evaluation, organ function research and new drug development, etc. [0003] The process of PET imaging is to collect the number of photons generated by the annihilation of positrons to obtain projection data by injecting or taking radiopharmaceuticals. However, the data collection lasts for a long time, and the massive storage data recorded by the detector ring also brings difficulties for subsequent data storage and image reconstruction. Data processing is often performed by means of computer clusters, which ...

AI Technical Summary

Problems solved by technology

The maximum likelihood estimation can adapt to the statistical characteristics of Poisson noise and has been widely used. However, as the number of iterations increases, the noise will be amplified. For this, regularization terms are introduced to constrain the sparseness of the image. Regularization provides a new regularization idea for image reconstruction. At present, most of the functions of a single basis are used, such as discrete cosine transform, but a single basis cannot be the most effective representation for different component images, and the sparse representation method of the image also affects the image quality. Key Factors of Quality

In addition, most of the objective functions are established in the case of Gaussian least squares. The least square method can well fit the solution of Gaussian noise, but it is often polluted by quantum noise in medical imaging systems, and quantum noise obeys Poisson distribution statistics. principle rather than additive noise

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