Transformer Encoder-Decoder with depth-wise separable convolution for Low-Dose CT Denoising

Low-dose computed tomography (LDCT) images frequently suffer from noise and artifacts due to diminished radiation doses, challenging the diagnostic integrity of the images. We introduce an innovative approach, the Transformer Encoder-Decoder with Depth-wise Separable Convolution (ECformer), which incorporates depth-wise separable convolution into the Transformer architecture. This synergistic fusion of convolutional neural network (CNN) and Transformer capabilities notably enhances spatial information capture while concurrently mitigating computational demands. Specifically, depth-wise separable convolution supplants traditional linear projection within the Transformer, facilitating efficient learning of local contextual dynamics. Further, the adoption of an encoder-decoder architecture bolsters the hierarchical feature representation, with shortcut connections ensuring the preservation of detail. Empirical evaluations on the AAPM-Mayo Clinic Low-Dose CT Grand Challenge Dataset confirm that ECformer outperforms existing denoising methods.