{"doi":"10.1109/tbme.2024.3428399","title":"A Physics-Informed Deep Neural Network for Harmonization of CT Images","abstract":"OBJECTIVE: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs). METHODS: An adversarial generative network was trained on virtual CT images acquired under various imaging conditions using a virtual imaging platform with 40 computational patient models. These models featured anthropomorphic lungs with different levels of pulmonary diseases, including nodules and emphysema. Imaging was conducted using a validated CT simulator at two dose levels and varying reconstruction kernels. The trained model was tested on an independent virtual test dataset and two clinical datasets. RESULTS: On the virtual test set, the harmonizer improved the structural similarity index from 79.3 16.4% to 95.8 1.7%, normalized mean squared error from 16.7 9.7% to 9.2 1.7%, and peak signal-to-noise ratio from 27.7 3.7 dB to 32.2 1.6 dB. Moreover, the harmonized images yielded more precise quantification of emphysema-based imaging biomarkers for lung attenuation, LAA -950 from 5.6 8.7% to 0.23 0.16%, Perc 15 from 43.4 45.4 HU to 20.0 7.5 HU, and Lung Mass from 0.3 0.3 g to 0.1 0.2 g. In clinical data, the harmonizer reduced biomarker variability by an average of 70%. For lung nodules, harmonized images improved the detectability index by 6.5-fold and DNN-based precision by 6%. CONCLUSION: The proposed harmonizer significantly enhances image quality and quantification accuracy in CT imaging. SIGNIFICANCE: The study demonstrated the potential utility of image harmonization for consistent CT image quality and reliable quantification, which is crucial for clinical applications and patient management.","journal":"IEEE Transactions on Biomedical Engineering","year":2024,"id":438711,"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":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1164509,"name":"Saman Sotoudeh‐Paima","orcid":"0000-0003-0170-2541","position":1,"is_corresponding":false},{"id":1138982,"name":"Cindy McCabe","orcid":"0009-0001-1099-3494","position":2,"is_corresponding":false},{"id":688238,"name":"Ehsan Abadi","orcid":"0000-0002-9123-5854","position":3,"is_corresponding":false},{"id":292053,"name":"Ehsan Samei","orcid":"0000-0001-7451-3309","position":4,"is_corresponding":false},{"id":1161435,"name":"Mojtaba Zarei","orcid":"0000-0002-0997-7411","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T02:00:43.618468Z","pmid":"39012733","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":[]}