{"doi":"10.1002/mp.16611","title":"Feasibility of dual‐energy CBCT material decomposition in the human torso with 2D anti‐scatter grids and grid‐based scatter sampling","abstract":"BACKGROUND: Dual-energy (DE) imaging techniques in cone-beam computed tomography (CBCT) have potential clinical applications, including material quantification and improved tissue visualization. However, the performance of DE CBCT is limited by the effects of scattered radiation, which restricts its use to small object imaging. PURPOSE: This study investigates the feasibility of DE CBCT material decomposition by reducing scatter with a 2D anti-scatter grid and a measurement-based scatter correction method. Specifically, the investigation focuses on iodine quantification accuracy and virtual monoenergetic (VME) imaging in phantoms that mimic head, thorax, abdomen, and pelvis anatomies. METHODS: A 2D anti-scatter grid prototype was utilized with a residual scatter correction method in a linac-mounted CBCT system to investigate the effects of robust scatter suppression in DE CBCT. Scans were acquired at 90 and 140 kVp using phantoms that mimic head, thorax, and abdomen/pelvis anatomies. Iodine vials with varying concentrations were placed in each phantom, and CBCT images were decomposed into iodine and water basis material images. The effect of a 2D anti-scatter grid with and without residual scatter correction on iodine concentration quantification and contrast visualization in VME images was evaluated. To benchmark iodine concentration quantification accuracy, a similar set of experiments and DE processing were also performed with a conventional multidetector CT scanner. RESULTS: In CBCT images, a 2D grid with or without scatter correction can differentiate iodine and water after DE processing in human torso-sized phantom images. However, iodine quantification errors were up to 10 mg/mL in pelvis phantoms when only the 2D grid was used. Adding scatter correction to 2D-grid CBCT reduced iodine quantification errors below 1.5 mg/mL in pelvis phantoms, comparable to iodine quantification errors in multidetector CT. While a noticeable contrast-to-noise ratio improvement was not observed in VME CBCT images, contrast visualization was substantially better in 40 keV VME images in visual comparisons with 90 and 140 kVp CBCT images across all phantom sizes investigated. CONCLUSIONS: This study indicates that accurate DE decomposition is potentially feasible in DE CBCT of the human torso if robust scatter suppression is achieved with 2D anti-scatter grids and residual scatter correction. This approach can potentially enable better contrast visualization and tissue and contrast agent quantification in various CBCT applications.","journal":"Medical Physics","year":2023,"id":351439,"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":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9583,"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":1096126,"name":"Cem Altunbas","orcid":"0000-0002-9030-8054","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T01:12:41.740237Z","pmid":"37477550","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":[]}