{"doi":"10.1002/mp.16093","title":"Technical note: Phantom‐based training framework for convolutional neural network CT noise reduction","abstract":"BACKGROUND: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoising models is the procurement of training data consisting of paired examples of high-noise and low-noise CT images. Obtaining these paired data are not practical in a clinical setting where the raw projection data is not available. This work outlines a technique to optimize CNN denoising models using methods that are available in a routine clinical setting. PURPOSE: To demonstrate a phantom-based training framework for CNN noise reduction that can be efficiently implemented on any CT scanner. METHODS: The phantom-based training framework uses supervised learning in which training data are synthesized using an image-based noise insertion technique. Ten patient image series were used for training and validation (9:1) and noise-only images obtained from anthropomorphic phantom scans. Phantom noise-only images were superimposed on patient images to imitate low-dose CT images for use in training. A modified U-Net architecture was used with mean-squared-error and feature reconstruction loss. The training framework was tested for clinically indicated whole-body-low-dose CT images, as well as routine abdomen-pelvis exams for which projection data was unavailable. Performance was assessed based on root-mean-square error, structural similarity, line profiles, and visual assessment. RESULTS: When the CNN was tested on five reserved quarter-dose whole-body-low-dose CT images, noise was reduced by 75%, root-mean-square-error reduced by 34%, and structural similarity increased by 60%. Visual analysis and line profiles indicated that the method significantly reduced noise while maintaining spatial resolution of anatomic features. CONCLUSION: The proposed phantom-based training framework demonstrated strong noise reduction while preserving spatial detail. Because this method is based within the image domain, it can be easily implemented without access to projection data.","journal":"Medical Physics","year":2022,"id":277237,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9485,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":948789,"name":"Andrew D. Missert","orcid":null,"position":1,"is_corresponding":false},{"id":355872,"name":"Hao Gong","orcid":"0000-0002-1123-7172","position":2,"is_corresponding":false},{"id":237063,"name":"Shuai Leng","orcid":"0000-0002-6453-9481","position":3,"is_corresponding":false},{"id":237062,"name":"Lifeng Yu","orcid":"0000-0002-4267-1326","position":4,"is_corresponding":false},{"id":237065,"name":"Cynthia H. McCollough","orcid":"0000-0002-5346-332X","position":5,"is_corresponding":false},{"id":772332,"name":"Nathan R. Huber","orcid":"0000-0002-6285-789X","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T00:28:34.768552Z","pmid":"36385704","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}