{"doi":"10.1109/tbme.2025.3550823","title":"Time of Flight Transmission Mode Ultrasound Computed Tomography With Expected Gradient and Boundary Optimization","abstract":"OBJECTIVE: Quantitative time of flight in transmission mode ultrasound computed tomography (TFTM USCT) is a promising, cost-effective, and non-invasive modality, particularly suited for functional imaging. However, TFTM USCT encounters resolution challenges due to path information concentration in specific medium regions and uncertainty in transducer positioning. This study proposes a method to enhance resolution and robustness, focusing on low-frequency TFTM USCT for pulmonary imaging. METHODS: The proposed technique improves the orientation of steepest descent algorithm steps, preventing resolution degradation due to path information concentration, while allowing for a posteriori sensor positioning retrieval. Total variation regularization is employed to stabilize the inverse problem, and a modified Barzilai-Borwein method determined the step size in the steepest descent algorithm. The proposed method was validated through simulations of data on healthy and abnormal cross-sections of a human chest using MATLAB's k-Wave toolbox. Additionally, experimental data were collected using a Verasonics Vantage 64 low-frequency system and a ballistic gel torso-mimicking phantom to assess robustness under a more realistic environment, closer to that of a clinical situation. RESULTS: The results showed that the proposed method significantly improved image quality and successfully retrieved sensor locations from imprecise positioning. SIGNIFICANCE: This study is the first to address transducer location uncertainty on a transducer belt in TFTM USCT and to apply an estimated gradient approach. Additionally, low-frequency USCT for lung imaging is quite novel, and this work addresses practical questions that will be important for translational development.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":562922,"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":0.961,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":986639,"name":"Andre Vieira Pigatto","orcid":"0000-0002-3175-998X","position":1,"is_corresponding":false},{"id":162373,"name":"Richard C. Aster","orcid":"0000-0002-0821-4906","position":2,"is_corresponding":false},{"id":1465324,"name":"Chi Nan Pai","orcid":"0000-0002-2061-1082","position":3,"is_corresponding":false},{"id":305508,"name":"Jennifer L. Mueller","orcid":"0000-0002-2583-5771","position":4,"is_corresponding":false},{"id":771906,"name":"S.S. Furuie","orcid":"0000-0002-1557-3018","position":5,"is_corresponding":false},{"id":1465323,"name":"Roberto Costa Ceccato","orcid":"0000-0002-9244-2572","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T02:56:09.638214Z","pmid":"40072864","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":[]}