Based on the Convolutional Neural Network(CNN),image denoising methods can effectively remove the artifacts and noise associated with low-dose Computed Tomography(CT),thereby ensuring high-quality output while minimizing radiation exposure.This information is of great significance for patient health and medical diagnosis.This study proposes a novel denoising network called MDTNet to enhance the quality of low-dose CT images.In this approach,multilevel encoder-decoder denoising networks are constructed using a Dual-Tree Complex Wavelet Transform(DTCWT),which enables the preservation of high-frequency details.Moreover,a pixel shuffle was employed to facilitate multi-level input and feature fusion,resulting in significantly reduced computation and memory complexity.In addition,by training a set of residual mappings in the wavelet domain,optimal denoising performance was achieved using a two-level MDTNet with LeGall and Qshift_b filters.The effectiveness of the MDTNet was evaluated on the 2016 NIH-AAPM-Mayo Clinic low-dose CT grand challenge dataset.The experimental results demonstrate that MDTNet outperforms state-of-the-art denoising methods in quantitative and qualitative evaluations.Specifically,compared with the FWDNet,on 1 mm slices,MDTNet improved the average Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index Measure(SSIM)by 0.088 7 dB and 0.002 4,respectively;on a 3 mm slice,the increase was 0.144 3 dB and 0.003,respectively.Moreover,MDTNet processed 512X512 low-dose CT images on a Graphical Processing Unit(GPU)in 0.193 s.Preserving high-frequency details while maintaining efficiency,MDTNet presents an innovative framework for denoising low-dose CT images.
维普
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