Abstract
The deep convolutional neural network has been extensively studied for medical images denoising, specifically for low dose CT (LDCT) denoising. However, most of them disregard that medical images have a large dynamic range. After normalizing the input image, the difference between two nearby HU levels becomes minimal; furthermore, after multiplying it with the floating-point weight vector, the feature response becomes insensitive to small changes in the input images. As a consequence, the denoised image becomes visually smooth. With this observation, we propose to use HU level slicing for improving the performance of the vanilla convolutional network. In our method, we first use different CT windows to slice the input image into a separate HU range. Then different CNN network is used to process each generated input slice separately. Finally, a feature fusion module combines the feature learned by each network and produces the denoised image. Extensive experiments with different state of the art methods in different training settings (both supervised and unsupervised) in three benchmark low dose CT databases validates HU level slicing can significantly improve the denoising performance of the existing methods.
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Bera, S., Biswas, P.K. (2022). Analyzing and Improving Low Dose CT Denoising Network via HU Level Slicing. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_56
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DOI: https://doi.org/10.1007/978-3-031-16446-0_56
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