{"doi":"10.1109/tbme.2025.3622570","title":"A Hybrid Sparse Primary Sampling (SPS) Strategy for CBCT","abstract":"Cone Beam Computed Tomography (CBCT) image quality is degraded by the increased scatter from the broad beam geometry, while conventional anti-scatter grid (ASG) only provides partial mitigation at the cost of elevated imaging dose. OBJECTIVE: In this study, we exploit the smooth behavior of the scatter signal with a novel sparse anti-scatter grid to improve the image quality of CBCT. METHODS: We achieve sparse sampling of the primary beam signal by sparsely inserting individual collimators focusing on the X-ray source into a template in front of the detector. The novel sparse primary sampling (SPS) grid is evaluated via Monte Carlo simulations with a synthetic CT phantom, patient head phantom, and patient pelvis phantom. Image reconstruction based on SPS was formulated as a constrained optimization problem with fidelity terms on the total signal and the sparsely sampled primary signals. Image quality improvement was benchmarked using ideal primary signal reconstructed images, worst-case scatter degraded reconstructed images, and the previously studied 3-D Richardson-Lucy fitting scatter correction method using low count Monte Carlo. RESULTS: The novel SPS grid and reconstruction method demonstrated recovery of HU values and image resolution with sampling densities under 0.1%. CONCLUSION: The hybrid hardware-software method supports flexible sampling density and pattern with minimal primary signal loss and image dose increase. SIGNIFICANCE: A novel SPS grid was introduced, and a successful demonstration in the Monte Carlo study shows the feasibility of significantly improving CBCT image quality for interventional and radiotherapy procedures via the SPS strategy.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":578584,"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":0.9579,"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":1488878,"name":"Qihui M. Lyua","orcid":null,"position":1,"is_corresponding":false},{"id":1488879,"name":"Shusen Jinga","orcid":null,"position":2,"is_corresponding":false},{"id":1488880,"name":"Hengjie Liub","orcid":null,"position":3,"is_corresponding":false},{"id":1488881,"name":"Catherine H. Frankb","orcid":null,"position":4,"is_corresponding":false},{"id":1488882,"name":"Lu Jianga","orcid":null,"position":5,"is_corresponding":false},{"id":1488883,"name":"Dan Ruanb","orcid":null,"position":6,"is_corresponding":false},{"id":1488884,"name":"Ke Shenga","orcid":null,"position":7,"is_corresponding":false},{"id":1488877,"name":"Alan R. Lia","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:58:24.957414Z","pmid":"41115082","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":[]}