ObjectiveTo analyze the influencing factors for image quality of 18F-deoxyglucose (FDG) positron emission tomography (PET)/CT systemic tumor imaging and explore the method of control in order to improve the PET/CT image quality. MethodsRetrospective analysis of image data from March to June 2011 collected from 1 000 18F-FDG whole body tumor imaging patients was carried out. We separated standard films from non-standard films according to PET/CT image quality criteria. Related factors for non-standard films were analyzed to explore the entire process quality control. ResultsThere were 158 cases of standard films (15.80%), and 842 of non-standard films (84.20%). Artifact was a major factor for non-standard films (93.00%, 783/842) followed by patients’ injection information recording error (2.49%, 21/842), the instrument factor (1.90%, 16/842), incomplete scanning (0.95%, 8/842), muscle and soft tissue uptake (0.83%, 7/842), radionuclide contamination (0.59%, 5/842), and drug injection (0.24%, 2/842). The waste film rate was 5.80% (58/1 000), and the redoing rate was 2.20% (22/1 000). ConclusionComplex and diverse factors affect PET/CT image quality throughout the entire process, but most of them can be controlled if doctors, nurses and technicians coordinate and cooperate with each other. The rigorous routine quality control of equipment and maintenance, patients’ full preparation, appropriate position and scan field, proper parameter settings, and post-processing technology are important factors affecting the image quality.
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.