A Modified Higher-Order Singular Value Decomposition Framework With Adaptive Multilinear Tensor Rank Approximation for Three-Dimensional Magnetic Resonance Rician Noise Removal - Wang, Li; Xiao, Di; Hou, Wen S.; Wu, Xiao Y.; Chen, Lin (Frontiers in Oncology, 2020)
The Magnetic Resonance (MR) images are acknowledged to be inevitably corrupted by Rician distributed noise which adversely affected the image quality for diagnosis purpose. However, the traditional denoising methods may recover the images from corruptions with severe loss of detailed structure and edge information, which would affected the lesion detections and diagnostic decision making. In this study we challenged improving the Rician noise removal from 3D MR volumetric data through a Modified High-Order Singular Value Decomposition (MHOSVD) method. The proposed framework of MHOSVD involved a parameterized logarithmic nonconvex penalty function for Low-Rank Tensor Approximation (LRTA) algorithm optimization to suppress the image noise in MR dataset. Reference cubes were extracted from the noisy image volume and block matching was performed according to non-local similarity for a fourth-order tensor construction. Then the LRTA problem was implemented by tensor factorization approaches, and the ranks of unfolding matrices along different modes of the tensor were estimated utilizing an adaptive non-convex low-rank method. The denoised MR images were finally restored through aggregating all recovered cubes. We investigated the proposed algorithm MHOSVD on both the synthetic and real clinic 3D MR images for Rician noise removal, and relative results demonstrated the MHOSVD can recover images with fine structures and detailed edge preservation with heavy noise even as high as 15% of the maximum intensity. The experiments results were also compared along with several classical denoising methods, the MHOSVD exhibited a sufficient improvement in noise removal performance at various noise conditions in terms of different measurement index such as peak-signal-to-noise ratio and structure-similarity-index metrics. Based upon the comparison, the proposed MHOSVD has proved a relative stated-of-the-art performance with excellent detailed structure reservation.