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Longitudinal image analysis has become a hot research topic due to the progress of medical image processing and analysis. However, noises generally exist in most of medical images, which will negatively affect statistical characteristics of the image intensities and therefore weaken the contrast between different organs to further result in difficulties to image processing methods. In this paper, traditional local de-noising methods are first reviewed with their disadvantages in introducing new artifacts being revealed. On the contrary, non-local de-noising eliminates image noises while intact image structure information is preserved. Therefore, an improved non-local method is then proposed by incorporating temporal information of longitudinal images into the formula of non-local de-noising. Finally, the proposed method is applied to eliminate noises from cardiac longitudinal MR images. The proposed method searches similar pairs in the whole image space rather than in local areas, which is particularly different from traditional methods where only neighborhood intensities are used. The similarities are considered as weightings which are used to estimate the intensity by weighted average of all similar pairs. Experimental results show that the proposed method can effectively eliminate noises from cardiac longitudinal images without weakening boundaries of the images. To improve time performance, the proposed method is accelerated using CUDA and 150× improvement is obtained.

Temporally Regularized Non-local De-noising and Its Application to Cardiac Longitudinal MR Images