{"doi":"10.1088/1361-6560/ada7be","title":"Accurate image reconstruction within and beyond the field-of-view of CT system from data with truncation","abstract":"Abstract Objective: Accurate image reconstruction from data with truncation in X-ray computed tomography (CT) remains a topic of research interest; and the works reported previously in the literature focus largely on reconstructing an image only within the scanning field-of-view (FOV). We develop algorithms to invert the data model with truncation for accurate image reconstruction within the entire subject support or a region slightly smaller than the subject support. Methods: We formulate image reconstruction from data with truncation as an optimization program, which includes hybrid constraints on image total variation (TV) and image L1-norm for effectively suppressing truncation artifacts. An algorithm, referred to as the TV-L1 algorithm, is developed for image reconstruction (i.e., inversion of the data model) from data with truncation through solving the optimization program. Results: We perform numerical studies to evaluate accuracy and stability of the TV-L1 algorithm by using simulated and real CT data. Accurate images can be obtained stably by use of the TV-L1 algorithm within a region substantially larger than the FOV from data with truncation of varying degrees. Conclusions: The TV-L1 algorithm can invert the data model with truncation to accurately and stably reconstruct images within the subject support or a region slightly smaller than the subject support, which is substantially larger than the FOV. Significance: Accurate image reconstruction within a region substantially larger than the FOV from data with truncation can be of theoretical interest and practical implication. The insights and TV-L1 algorithm may also be generalized to image reconstruction from data with truncation in other tomographic imaging modalities.","journal":"Physics in Medicine and Biology","year":2025,"id":549941,"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":0.9612,"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":459928,"name":"Buxin Chen","orcid":"0000-0002-5884-8173","position":1,"is_corresponding":false},{"id":459930,"name":"Dan Xia","orcid":"0000-0001-8567-1038","position":2,"is_corresponding":false},{"id":459931,"name":"Emil Y. Sidky","orcid":"0000-0002-6951-2456","position":3,"is_corresponding":false},{"id":459932,"name":"Xiaochuan Pan","orcid":"0000-0002-3074-9771","position":4,"is_corresponding":false},{"id":459929,"name":"Zheng Zhang","orcid":"0000-0002-6975-6274","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:54:12.321988Z","pmid":"39778342","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":[]}