Abstract
The widespread use of computed tomograph (CT) technology in clinic has caused more and more patients to worry that they will receive too much radiation during the scanning. The low-dose CT (LDCT) scanning is more likely to be accepted by the patients. But LDCT images can adversely affect doctors’ diagnosis, owing to low quality of the images. Therefore, it is necessary to improve the diagnostic performance by denoising LDCT images. During the past few decades, the convolutional neural networks (CNNs) and Transformer models that achieve remarkable performance in natural image denoising provide new avenues for LDCT denoising. Although the existing methods have successfully achieved noise reduction, there is still large room for improvement in the denoising level. In this paper, we refer to the implementation of natural images denoising, and proposed a transformer-based U-shape network model to achieve denoising in LDCT images. In each transformer block, we used the depth-wise convolution, transposed self-attention mechanism, and SimpleGate to improve performance and speed up efficiency. Extensive experiments on the AAPM-Mayo clinic LDCT Grand Challenge dataset indicated that the proposed model yielded a competitive performance to the compared baseline denoising methods. In particular, good evaluation was achieved in noise suppression, structure preservation and lesion highlighting.
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Xiong, L., Qiu, W., Li, N., Li, Y., Zhang, Y. (2023). Low Dose CT Image Denoising Using Efficient Transformer with SimpleGate Mechanism. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_47
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