Detection And Removal Methods Of Vascular Artifacts In Optical Imaging Based On Intrinsic Signals

With the development of the new imaging methods, different functional imaging methods like fMRI, PET have been maturely applied including brain optical imaging based on intrinsic signals. Because of its high spatial resolution, simple facility and long-time in-vivo recording, it can help study the brain functional architecture. But noises like breath, heart pulse, vasmotion always reduce the signal-noise ration and the quality of the image. So only after some image processing can we separate the signals, remove the artifact. Early brain optical imaging process methods based on intrinsic signals can effectively improve the image, but in some cases, the noise can hardly be removed. Engineers are interested in developing new imaging process methods.In this paper, we want to study some vascular artifact detection and removal methods because in our lab, the data we get from the experiment is quite veiled by the vascular artifact which strongly decreases the SNR. So we first introduce two applied vascular artifact detection methods, LSM and variance, which can predict the vascular artifact along with some unsatisfied disadvantage, and then we innovatively prove when we combine the LSM and variance, we can get more accurate vascular artifact. After that, we compare four vascular artifact removal methods and get the conclusion that only the median insert removal can improve the quality of the image. At last, we discuss the radius of the local area when we use the median insert removal method.Also we innovatively combine the vascular artifact detection and removal methods with the PCA and ICA, proving the combined method can effectively improve the quality of the image.So as a conclusion, in this paper, we study a new vascular artifact detection method which combines the LSM with variance. And we also suggest only the median insert method can get the highest quality of the image. Some factors like radius of the local region which can improve the quality are also discussed. At last, we do the pre-process by PCA or ICA, and do the median insert removal in the components whose quality are better than the original images.