{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["95(1138)"],"submitter":["Cao Q"],"pubmed_abstract":["Objectives
To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT).Methods
In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability.Results
For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res mo"],"journal":["The British journal of radiology"],"pagination":["20210125"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9815729"],"repository":["biostudies-literature"],"pubmed_title":["Improving image quality and lung nodule detection for low-dose chest CT by using generative adversarial network reconstruction."],"pmcid":["PMC9815729"],"pubmed_authors":["Yang W","Qin L","Mao Y","Cao Q","Quan G","Yan F"],"additional_accession":[]},"is_claimable":false,"name":"Improving image quality and lung nodule detection for low-dose chest CT by using generative adversarial network reconstruction.","description":"Objectives
To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT).Methods
In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability.Results
For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res mo","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-27T02:28:48.021Z","creation":"2025-04-06T18:33:31.674Z"},"accession":"S-EPMC9815729","cross_references":{"pubmed":["35994298"],"doi":["10.1259/bjr.20210125"]}}