{"doi":"10.1109/tuson.2025.3633606","title":"Spatial Coherence-Based Sound Speed Estimation in Plane Wave Ultrasound Imaging","abstract":"A globally constant sound speed of 1540 m/s is often assumed when beamforming ultrasound images, despite variations in tissue properties that lead to heterogeneous sound speeds. As a result, the majority of local sound speeds are often misrepresented, which degrades image quality, resulting in inaccurate target depths, sizes, and shapes, thus reducing the overall effectiveness of diagnostic and interventional ultrasound imaging. In this paper, we introduce a spatial coherence-based approach to estimate local sound speeds within and surrounding coherent and incoherent targets in ultrasound images acquired with multiple plane wave transmissions. Ultrasound data with known ground truth sound speeds were simulated. “Optimal\" sound speeds produced the lowest possible lateral full width at half maximum (FWHM) of point targets, the lowest possible contrast of hypoechoic or anechoic targets, or the highest possible contrast of hyperechoic targets, serving as the ground truth sound speed for experimental data. With our proposed method, sound speeds achieved 99.68% to 100% agreement with the simulated ground truth value. When benchmarked against the optimal sound speeds, our coherence-based method yielded 0.58% and 0.56% mean deviations from optimal sound speeds in tissue-mimicking phantom data and in vivo breast data, respectively, whereas a speckle brightness maximization approach produced larger deviations of 2.50% and 3.41%, respectively. Qualitatively, B-mode images created with coherence-based sound speeds improved lateral FWHM, enhanced contrast, reduced acoustic clutter, better delineated target boundaries, and better defined speckle patterns. These results promise to improve accuracy and enhance the effectiveness of diagnostic and interventional ultrasound procedures.","journal":"IEEE Transactions on Ultrasonics","year":2025,"id":535525,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9465,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":1419152,"name":"Yunlong Zhu","orcid":"0000-0002-8460-2820","position":1,"is_corresponding":false},{"id":275807,"name":"Muyinatu A. Lediju Bell","orcid":"0000-0002-8394-4482","position":2,"is_corresponding":false},{"id":1411790,"name":"Jiaxin Zhang","orcid":"0009-0005-4514-925X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:51:56.297114Z","pmid":null,"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":[]}