Electrical Engineering and Systems Science > Image and Video Processing
[Submitted on 28 Feb 2021 (v1), last revised 5 Jul 2021 (this version, v4)]
Title:TransCT: Dual-path Transformer for Low Dose Computed Tomography
View PDFAbstract:Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (X_{L_c}) and latent texture features (X_{L_t}) from the LF part, as well as HF embeddings (X_{H_f}) from the HF part. Further, we feed X_{L_t} and X_{H_f} into a modified transformer with three encoders and decoders to obtain well-refined HF texture features. After that, we combine these well-refined HF texture features with the pre-extracted X_{L_c} to encourage the restoration of high-quality LDCT images with the assistance of piecewise reconstruction. Extensive experiments on Mayo LDCT dataset show that our method produces superior results and outperforms other methods.
Submission history
From: Zhicheng Zhang [view email][v1] Sun, 28 Feb 2021 21:46:54 UTC (3,276 KB)
[v2] Wed, 3 Mar 2021 02:10:12 UTC (3,277 KB)
[v3] Mon, 24 May 2021 19:12:28 UTC (3,290 KB)
[v4] Mon, 5 Jul 2021 06:24:14 UTC (1,718 KB)
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