Noise reduction of diffusion tensor images by sparse representation and dictionary learning

Abstract

Background: The low quality of diffusion tensor image (DTI) could affect the accuracy of oncology diagnosis. Methods: We present a novel sparse representation based denoising method for three dimensional DTI by learning adaptive dictionary with the context redundancy between neighbor slices. In this study, the context redundancy among the adjacent slices of the diffusion weighted imaging volumes is utilized to train sparsifying dictionaries. Therefore, higher redundancy could be achieved for better description of image with lower computation complexity. The optimization problem is solved efficiently using an iterative block-coordinate relaxation method. Results: The effectiveness of our proposed method has been assessed on both simulated and real experimental DTI datasets. Qualitative and quantitative evaluations demonstrate the performance of the proposed method on the simulated data. The experiments on real datasets with different b-values also show the effectiveness of the proposed method for noise reduction of DTI. Conclusions: The proposed approach well removes the noise in the DTI, which has high potential to be applied for clinical oncology applications.