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Dual-energy computed tomography (DECT) has attracted the attention of clinical researchers because of its outstanding capabilities to identify and decompose materials. Considering that material decomposition is unstable, reconstructed material images experience severe noise magnification resulting from the measurement data. To alleviate this problem, we propose a direct material reconstruction method by establishing a novel constrained reconstruction model based on total variation (TV) and block matching 3D (BM3D) frame regularization. TV regularization preserves the sparsity in the gradient domain of material maps and helps to preserve the image edges. BM3D frame is applied to depict the similarities among various patches in reconstructed images by grouping similar 2D image blocks into 3D data arrays combined with sparse transforms. To solve the program efficiently, a practical algorithm based on alternating direction method is developed. A modified strategy of block matching denoising is designed by incorporating the polychromatic reconstructed image into the problem solution. Digital and real data phantom studies are performed to validate the performance of the proposed method. The proposed method reduces the standard deviation on the selected region of interests by an average of 95.02% and 89.03% for the digital phantom and 95.21% and 84.19% for the real data compared with the extended simultaneous algebraic reconstruction technique and TV-based method, respectively. The reconstruction results demonstrate that the proposed method has promising capabilities in direct material reconstruction and superiority over its counterparts.

A Direct Material Reconstruction Method for DECT Based on Total Variation and BM3D Frame