Image reconstruction for positron emission tomography based on patch-based regularization and dictionary learning.

Abstract

PurposePositron emission tomography (PET) is an important tool for nuclear medical imaging. It has been widely used in clinical diagnosis, scientific research, and drug testing. PET is a kind of emission computed tomography. Its basic imaging principle is to use the positron annihilation radiation generated by radionuclide decay to generate gamma photon images. However, in practical applications, due to the low gamma photon counting rate, limited acquisition time, inconsistent detector characteristics, and electronic noise, measured PET projection data often contain considerable noise, which results in ill‐conditioned PET images. Therefore, determining how to obtain high‐quality reconstructed PET images suitable for clinical applications is a valuable research topic. In this context, this paper presents an image reconstruction algorithm based on patch‐based regularization and dictionary learning (DL) called the patch‐DL algorithm. Compared to other algorithms, the proposed algorithm can retain more image details while suppressing noise.MethodsExpectation‐maximization (EM)‐like image updating, image smoothing, pixel‐by‐pixel image fusion, and DL are the four steps of the proposed reconstruction algorithm. We used a two‐dimensional (2D) brain phantom to evaluate the proposed algorithm by simulating sinograms that contained random Poisson noise. We also quantitatively compared the patch‐DL algorithm with a pixel‐based algorithm, a patch‐based algorithm, and an adaptive dictionary learning (AD) algorithm.ResultsThrough computer simulations, we demonstrated the advantages of the patch‐DL method over the pixel‐, patch‐, and AD‐based methods in terms of the tradeoff between noise suppression and detail retention in reconstructed images. Quantitative analysis shows that the proposed method results in a better performance statistically [according to the mean absolute error (MAE), correlation coefficient (CORR), and root mean square error (RMSE)] in considered region of interests (ROI) with two simulated count levels. Additionally, to analyze whether the results among these methods have significant differences, we used one‐way analysis of variance (ANOVA) to calculate the corresponding P values. The results show that most of the P < 0.01; some P> 0.01 < 0.05. Therefore, our method can achieve a better quantitative performance than those of traditional methods.ConclusionsThe results show that the proposed algorithm has the potential to improve the quality of PET image reconstruction. Since the proposed algorithm was validated only with simulated 2D data, it still needs to be further validated with real three‐dimensional data. In the future, we intend to explore GPU parallelization technology to further improve the computational efficiency and shorten the computation time.