{"doi":"10.1088/1361-6560/abca02","title":"Signal intensity form of the Tofts model for quantitative analysis of prostate dynamic contrast enhanced MRI data","abstract":"Abstract The aim of this study is to develop a signal intensity ( S ( t )) form of the standard Tofts pharmacokinetic model that avoids the need to calculate tissue contrast agent concentration ( C ( t )) as function of time ( t ). We refer to this as ‘SI-Tofts’ model. Physiological parameters ( K trans and v e ) calculated using the SI-Tofts and standard Tofts models were compared by using simulations and human prostate dynamic contrast enhanced (DCE) MRI data. This approach was also applied to the Patlak model to compare K trans values calculated from C ( t ) and S ( t ). Simulations were performed on DCE-MRI data from the quantitative imaging biomarkers alliance to validate SI-Tofts model. In addition, ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The C ( t ) was calculated for each ROI using the standard model with measured pre-contrast tissue T 1 values. Both the simulation and clinical results showed strong correlation ( r = 0.87–0.99, p < 0.001) for K trans and v e calculated from the SI-Tofts and standard Tofts models. The SI-Tofts model with a correction factor using the T 1 ratio of blood to tissue significantly improved the K trans estimates. The correlation of K trans obtained from the Patlak model with C ( t ) vs S ( t ) was also strong ( r = 0.95–0.99, p < 0.001). These preliminary results suggest that physiological parameters from DCE-MRI can be reliably estimated from the SI-Tofts model without contrast agent concentration calculation.","journal":"Physics in Medicine and Biology","year":2020,"id":110685,"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":0.9409,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":515258,"name":"Aritrick Chatterjee","orcid":"0000-0003-4022-7106","position":1,"is_corresponding":false},{"id":526695,"name":"Milica Medved","orcid":"0000-0003-2945-5511","position":2,"is_corresponding":false},{"id":320348,"name":"Aytekin Oto","orcid":"0000-0003-4325-2489","position":3,"is_corresponding":false},{"id":515262,"name":"Gregory S. Karczmar","orcid":"0000-0002-2455-5858","position":4,"is_corresponding":false},{"id":526694,"name":"Xiaobing Fan","orcid":"0000-0001-9004-029X","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-18T23:13:01.914939Z","pmid":"33181487","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":[]}