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A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention

• Original Paper
• Published: 11 September 2024

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• Ju Zhang 1 ,
• Lieli Ye 1 ,
• Weiwei Gong 2 ,
• Mingyang Chen 1 ,
• Guangyu Liu 1 &
• Yun Cheng 3  

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

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This work is partially supported by the National Natural Science Foundation of China under Grant 60974042.

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College of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310030, China

Ju Zhang, Lieli Ye, Mingyang Chen & Guangyu Liu

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

Weiwei Gong

Department of Medical Imaging, Zhejiang Hospital, Hangzhou, 310058, China

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Dr. Ju Zhang is responsible for the method scheme, and funding, and for ensuring that the descriptions are accurate. Dr. Yun Cheng is responsible for coordinating, and assessing the results, making the manuscript agreed by all authors. Lieli Ye investigates the algorithm and is responsible for the preparation of the draft of the manuscript. Weiwei Gong is responsible for verification. Mingyang Chen is responsible for experiments. Guangyu Liu is responsible for the coding of the proposed scheme.

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Correspondence to Yun Cheng .

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Zhang, J., Ye, L., Gong, W. et al. A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention. j Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01254-z

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Received : 09 June 2024

Revised : 15 August 2024

Accepted : 22 August 2024

Published : 11 September 2024

DOI : https://doi.org/10.1007/s10278-024-01254-z

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Article Contents

Hepatitis a vaccine immunogenicity and boostability in adults receiving immunosuppressive therapy and adults living with hiv: a prospective single-centre cohort study.

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Jenny L Schnyder, Hannah M Garcia Garrido, Michael W Tanck, Irma Maurer, Agnes M Harskamp, Neeltje Kootstra, Martin P Grobusch, Abraham Goorhuis, Hepatitis a vaccine immunogenicity and boostability in adults receiving immunosuppressive therapy and adults living with HIV: a prospective single-Centre cohort study, Journal of Travel Medicine , 2024;, taae125, https://doi.org/10.1093/jtm/taae125

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Hepatitis A (hepA) vaccination is highly immunogenic in healthy individuals, however there is uncertainty about the immunogenicity in immunocompromised populations (ICPs).

In this prospective cohort study, people living with HIV (PLWH), patients on immunosuppressive mono- and combination therapy, and controls received two hepA vaccine doses at months 0 and 6–12, or three combined hepA/B vaccine doses at months 0, 1 and 6–12. Antibody levels were measured before and at different time-points post-vaccination (T2, 6, 8, 12 months). The primary endpoint was the seroconversion rate (SCR) at T8, defined as hepA antibodies ≥20 mIU/ml. To assess boostability, an additional vaccine dose was administered 1–5 years after T12 in those with antibodies < 50 mIU/ml, with antibody measurements before and seven days after the booster dose.

We included 150 participants. At T2 SCRs ranged between 35–58% in ICPs versus 94% in controls. Among PLWH, patients on monotherapy, combination therapy and controls SCRs at T8 were 33/34 (97%), 32/34 (94%), 25/30 (83%) and 28/28 (100%) respectively. The booster dose resulted in 71% additional seroconversion (17/24), with only patients using combination therapy not responding.

HepA vaccination is highly immunogenic in virologically suppressed PLWH and patients on immunosuppressive monotherapy, with SCRs after the complete hepA vaccination schedule similar to controls and adequate booster responses in case of waning immunity. However, patients using immunosuppressive combination therapy as well as all ICPs who did not receive the complete hepA vaccination schedule, are at risk of non-response to vaccination and post-vaccination antibody measurements are recommended.

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