Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Chao, Lianying; Wang, Yanli; Zhang, TaoTao; Shan, Wenqi; Zhang, Haobo; Wang, Zhiwei; Li, Qiang

doi:10.1016/j.compbiomed.2023.107830

Computers in Biology and Medicine

2024

DOI: 10.1016/j.compbiomed.2023.107830

|View full text |Cite

Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Lianying Chao,

Yanli Wang,

TaoTao Zhang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Alanazi,

Mercorelli

2024

Mathematics

View full text Add to dashboard Cite

Medical imaging is significant for accurate diagnosis, and here, noise often degrades image quality, thus making it challenging to identify important information. Denoising is a component of traditional image pre-processing that helps prevent incorrect disease diagnosis. Mitigating the noise becomes difficult if there are differences in the low-level segment features. Therefore, a Generative Adversarial Network (GAN)-aided Low-Noise Discriminator (LND) is introduced to improve the denoising effectiveness in medical images with a balanced image resolution with noise mitigation. The LND function is a key that distinguishes between high- and low-noise areas based on segmented features, which are also achieved by tuning the peak signal-to-noise ratio (PSNR). Considering the training sequences, the LND-identified intervals lessen the sequences to improve the changes in pixel reconstruction. The generator function in this method is responsible for increasing the PSNR improvements over the different pixels cumulatively. The proposed method successfully improves the pixel reconstruction by 11.05% and PSNR by 9.75%, with 9.75% less reconstruction time and 13.11% less extraction error for the higher pixel distribution ratios than other contemporary methods.

show abstract

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Alanazi,

Mercorelli

2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Cited by 1 publication

References 38 publications

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Contact Info

Product

Resources

About