{"doi":"10.1259/bjr.20220907","title":"Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning","abstract":"OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. METHODS: weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. RESULTS: For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. CONCLUSION: The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. ADVANCES IN KNOWLEDGE: PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.","journal":"British Journal of Radiology","year":2023,"id":341576,"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":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9239,"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":1078138,"name":"Raanan Marants","orcid":null,"position":1,"is_corresponding":false},{"id":849941,"name":"Yuan Gao","orcid":"0000-0002-6988-4217","position":2,"is_corresponding":false},{"id":1078139,"name":"Matthew Goette","orcid":null,"position":3,"is_corresponding":false},{"id":448971,"name":"Jessica Scholey","orcid":"0000-0002-8822-7861","position":4,"is_corresponding":false},{"id":267373,"name":"Jeffrey D. Bradley","orcid":"0000-0003-3047-3151","position":5,"is_corresponding":false},{"id":1054062,"name":"Tian Liu","orcid":"0009-0007-8567-3608","position":6,"is_corresponding":false},{"id":465985,"name":"Jun Zhou","orcid":"0000-0002-6078-9424","position":7,"is_corresponding":false},{"id":335142,"name":"Atchar Sudhyadhom","orcid":"0000-0002-0397-0884","position":8,"is_corresponding":false},{"id":236226,"name":"Xiaofeng Yang","orcid":"0000-0001-9023-5855","position":9,"is_corresponding":false},{"id":849942,"name":"Chih‐Wei Chang","orcid":"0000-0002-3818-4381","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T01:11:08.077724Z","pmid":"37660372","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":[]}