Optical coherence tomography angiography (OCTA), as a non-invasive three-dimensional fundus vascular imaging technique, has significant advantages in the diagnosis and follow-up of eye diseases such as diabetic retinopathy and age-related macular degeneration. However, the existence of OCTA image artifacts has seriously affected its clinical application. These artifacts are caused by various factors such as image acquisition, internal characteristics of the eyeball, eye movement and image processing, such as weak signals, blinking, defocusing, bands, tilting, occlusion, exposure, projection, movement and layering, leading to vascular quantization deviation, lesion blurring and image distortion, thereby reducing the accuracy of clinical diagnosis. To address this issue, researchers have proposed a variety of correction strategies, including enhancing signal strength, optimizing equipment, developing algorithms to identify and eliminate shadow artifacts, using hardware or software methods for motion correction, and employing deep learning algorithms for image quality assessment and artifact removal. Constructing a unified and systematic framework for artifact cognition and processing is crucial for enhancing the reliability of OCTA diagnostic results and will drive the level of ophthalmic diagnosis and treatment to a new height.
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.