Résumé
Objective To propose an idea executing myocardial perfusion-positron emission tomography (MP-PET) to solve its problems in vulnerability to noise,degraded image quality etc.Methods Low-rank plus sparse decomposition theory was applied to decomposing MP-PET images,and sparsity constraint of MP-PET images was executed with its tight frame coefficient to restore the image quality.Results The validity of the proposed method was verified by computer simulation and multi quantitative index.Conclusion The results demonstrate better performance of the restored images from the proposed recovery model compared with other methods.
Résumé
<p><b>OBJECTIVE</b>To propose a new method for dynamic positron emission tomographic (PET) image reconstruction using low rank and sparse penalty (L&S).</p><p><b>METHODS</b>The L&S reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82Rb myocardial perfusion image. The L&S reconstruction method was compared with maximum likelihood expectation maximization (MLEM) method, low-rank penalty method and sparse penalty method.</p><p><b>RESULTS</b>The L&S reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the most similar with the reference actual polar map.</p><p><b>CONCLUSION</b>L&S reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.</p>