{"doi":"10.1109/tbme.2023.3303445","title":"A Non-Contrast Multi-Parametric MRI Biomarker for Assessment of MR-Guided Focused Ultrasound Thermal Therapies","abstract":"OBJECTIVE: We present the development of a non-contrast multi-parametric magnetic resonance (MPMR) imaging biomarker to assess treatment outcomes for magnetic resonance-guided focused ultrasound (MRgFUS) ablations of localized tumors. Images obtained immediately following MRgFUS ablation were inputs for voxel-wise supervised learning classifiers, trained using registered histology as a label for thermal necrosis. METHODS: VX2 tumors in New Zealand white rabbits quadriceps were thermally ablated using an MRgFUS system under 3 T MRI guidance. Animals were re-imaged three days post-ablation and euthanized. Histological necrosis labels were created by 3D registration between MR images and digitized H&E segmentations of thermal necrosis to enable voxel-wise classification of necrosis. Supervised MPMR classifier inputs included maximum temperature rise, cumulative thermal dose (CTD), post-FUS differences in T2-weighted images, and apparent diffusion coefficient, or ADC, maps. A logistic regression, support vector machine, and random forest classifier were trained in red a leave-one-out strategy in test data from four subjects. RESULTS: ) threshold (0.43) in all subjects. The average Dice scores of overlap with the registered histological label for the logistic regression (0.63) and support vector machine (0.63) MPMR classifiers were within 6% of the acute contrast-enhanced non-perfused volume (0.67). CONCLUSIONS: Voxel-wise registration of MPMR data to histological outcomes facilitated supervised learning of an accurate non-contrast MR biomarker for MRgFUS ablations in a rabbit VX2 tumor model.","journal":"IEEE Transactions on Biomedical Engineering","year":2023,"id":410211,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"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":389947,"name":"Blake E. Zimmerman","orcid":"0000-0003-1769-7943","position":1,"is_corresponding":false},{"id":388327,"name":"Henrik Odéen","orcid":"0000-0003-2055-9795","position":2,"is_corresponding":false},{"id":389949,"name":"Jill Shea","orcid":"0000-0002-8644-7772","position":3,"is_corresponding":false},{"id":389951,"name":"Nicole Winkler","orcid":"0000-0001-6284-9550","position":4,"is_corresponding":false},{"id":317426,"name":"Rachel E. Factor","orcid":"0000-0003-1388-2500","position":5,"is_corresponding":false},{"id":389952,"name":"Sarang Joshi","orcid":"0000-0002-3446-4810","position":6,"is_corresponding":false},{"id":388329,"name":"Allison Payne","orcid":"0000-0002-7724-5001","position":7,"is_corresponding":false},{"id":389948,"name":"Sara Johnson","orcid":"0000-0001-8796-1549","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T01:21:31.144858Z","pmid":"37556341","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":[]}