{"doi":"10.1002/mp.14318","title":"Experimental assessment of microwave ablation computational modeling with MR thermometry","abstract":"PURPOSE: Computational models are widely used during the design and characterization of microwave ablation (MWA) devices, and have been proposed for pretreatment planning. Our objective was to assess three-dimensional (3D) transient temperature and ablation profiles predicted by MWA computational models with temperature profiles measured experimentally using magnetic resonance (MR) thermometry in ex vivo bovine liver. MATERIALS AND METHODS: We performed MWA in ex vivo tissue under MR guidance using a custom, 2.45 GHz water-cooled applicator. MR thermometry data were acquired for 2 min prior to heating, during 5-10 min microwave exposures, and for 3 min following heating. Fiber-optic temperature sensors were used to validate the accuracy of MR temperature measurements. A total of 13 ablation experiments were conducted using 30-50 W applied power at the applicator input. MWA computational models were implemented using the finite element method, and incorporated temperature-dependent changes in tissue physical properties. Model-predicted ablation zone extents were compared against MRI-derived Arrhenius thermal damage maps using the Dice similarity coefficient (DSC). RESULTS: Prior to heating, the observed standard deviation of MR temperature data was in the range of 0.3-0.7°C. Mean absolute error between MR temperature measurements and fiber-optic temperature probes during heating was in the range of 0.5-2.8°C. The mean DSC between model-predicted ablation zones and MRI-derived Arrhenius thermal damage maps for 13 experimental set-ups was 0.95. When comparing simulated and experimentally (i.e. using MRI) measured temperatures, the mean absolute error (MAE %) relative to maximum temperature change was in the range 5%-8.5%. CONCLUSION: We developed a system for characterizing 3D transient temperature and ablation profiles with MR thermometry during MWA in ex vivo liver tissue, and applied the system for experimental validation of MWA computational models.","journal":"Medical Physics","year":2020,"id":102342,"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":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9519,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":500331,"name":"Paul Keselman","orcid":null,"position":1,"is_corresponding":false},{"id":475625,"name":"Hojjatollah Fallahi","orcid":"0000-0001-9643-7183","position":2,"is_corresponding":false},{"id":380917,"name":"Punit Prakash","orcid":"0000-0001-6467-722X","position":3,"is_corresponding":false},{"id":380918,"name":"Pegah Faridi","orcid":"0000-0001-9304-7913","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-18T22:40:51.552773Z","pmid":"32506550","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":[]}