{"doi":"10.17615/86nd-kz59","title":"Breast tomosynthesis imaging configuration optimization based on computer simulation","abstract":"Digital tomosynthesis is an innovative imaging technology for early breast cancer detection by providing three-dimensional anatomical information with fast image acquisition and low-dose radiation. Most of current breast tomosynthesis systems utilize a design where a single X-ray tube moves along an arc above objects over a certain angular range. The mechanical movement and patient motion during the scan may degrade image quality. With a carbon nanotube-based multibeam X-ray source, a new breast tomosynthesis modality is innovated, which will potentially produce better image quality with stationary beam sources and faster scan and it enables a variety of beam distributions. In this study, several beam distributions, such as beam sources spanning along a one-dimensional (1-D) parallel configuration and sources over a two-dimensional (2-D) rectangle shape are investigated based on computer simulations. Preliminary results show that 2-D rectangle shapes outperform 1-D parallel shapes by providing better Z-resolution, enhanced image contrast, reduced out-ofplane blur and artifacts and lower reconstruction noise. These benefits may expand tomosynthesis applications to diagnostic and interventional procedures.","journal":"Carolina Digital Repository (University of North Carolina at Chapel Hill)","year":2022,"id":313566,"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":0.9576,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1011793,"name":"Junru Lu","orcid":"0000-0003-4668-5351","position":1,"is_corresponding":false},{"id":1011792,"name":"O. Zhou","orcid":"0009-0008-1730-295X","position":2,"is_corresponding":false},{"id":145139,"name":"Y. Chen","orcid":null,"position":3,"is_corresponding":false},{"id":1011794,"name":"Shuo Xu","orcid":"0009-0001-2827-3858","position":4,"is_corresponding":false},{"id":1012327,"name":"Yuan-Yu Lee","orcid":null,"position":5,"is_corresponding":false},{"id":1012326,"name":"L. Cong","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T00:33:44.454239Z","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":[]}