{"doi":"10.3389/fradi.2023.1168901","title":"Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study","abstract":"Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.","journal":"Frontiers in Radiology","year":2023,"id":371588,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9585,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":383637,"name":"Nan Wang","orcid":"0000-0002-7616-8083","position":1,"is_corresponding":false},{"id":463628,"name":"Srinivas Gaddam","orcid":"0000-0001-6818-6305","position":2,"is_corresponding":false},{"id":289225,"name":"Lixia Wang","orcid":"0000-0002-2304-7536","position":3,"is_corresponding":false},{"id":604766,"name":"Hui Han","orcid":"0000-0002-8890-4295","position":4,"is_corresponding":false},{"id":298266,"name":"Kyunghyun Sung","orcid":"0000-0003-4175-5322","position":5,"is_corresponding":false},{"id":383636,"name":"Anthony Christodoulou","orcid":"0000-0002-9334-8684","position":6,"is_corresponding":false},{"id":463629,"name":"Yibin Xie","orcid":"0000-0002-0333-567X","position":7,"is_corresponding":false},{"id":227742,"name":"Stephen J. Pandol","orcid":"0000-0003-0818-6017","position":8,"is_corresponding":false},{"id":289229,"name":"Debiao Li","orcid":"0000-0001-8560-8231","position":9,"is_corresponding":false},{"id":1109238,"name":"Chaowei Wu","orcid":"0000-0002-1060-9149","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-19T01:15:49.453761Z","pmid":"37731600","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":[]}