{"doi":"10.1093/radadv/umaf031","title":"Liver fat quantification using deep silicon photon-counting CT: an <i>in silico</i> imaging study","abstract":"Abstract Background Accurate liver fat quantification is essential for early diagnosis and effective management of fatty liver disease. Purpose To investigate the potential clinical utility of a deep silicon-based photon-counting CT (dSi-PCCT), currently in development, for liver fat quantification using human models in an in silico imaging study. Materials and methods dSi-PCCT is a cutting-edge photon-counting CT (GE HealthCare), with several investigational systems installed globally, used under IRB approval for imaging animals and human volunteers to support FDA clearance. We developed a dSi-PCCT simulator and benchmarked its imaging performance with respect to a prototype. We imaged a computational Gammex phantom with fat fractions (FF) ranging from 0% to 100%, along with five XCAT human models with liver FF ranging from 1% to 50%, using an abdominal CT protocol. The resulting spectral sinograms were processed using a material decomposition (MD) technique. We calculated HU-based Proton Density Fat Fraction (PDFF) from single-energy images in XCAT models and compared it against the MD-derived FF. The MD-derived FF of both datasets was assessed against the digitally defined ground truth values. Results We observed a strong correlation (R2 = 0.98) between MD-derived, HU-based PDFF, and ground-truth FF in a Gammex and XCAT models. There was no statistically significant difference (P = .52) in FF quantification accuracy between Gammex and the XCAT human models. The root mean square errors were 4.7% for Gammex and 2.7% for XCAT. Bland–Altman analysis further confirmed good agreement between the ground truth and MD-derived FF, with differences in FF ranging from −6.9% to 7% for Gammex and −3.0% to 37.6% for XCAT. Conclusion The results indicate that dSi-PCCT could enable accurate liver fat quantification across a wide range of FFs in multiple objects. These findings suggest that the potential utility of dSi-PCCT for accurate liver fat assessment should be explored in vivo.","journal":"Radiology Advances","year":2025,"id":526737,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9292,"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":954848,"name":"Zhye Yin","orcid":"0000-0003-1524-9258","position":1,"is_corresponding":false},{"id":1105028,"name":"Fredrik Grönberg","orcid":"0000-0003-1428-8351","position":2,"is_corresponding":false},{"id":1112327,"name":"Benjamin Wildman‐Tobriner","orcid":"0000-0002-7311-5947","position":3,"is_corresponding":false},{"id":1066855,"name":"Mridul Bhattarai","orcid":"0000-0003-4745-0546","position":4,"is_corresponding":false},{"id":688238,"name":"Ehsan Abadi","orcid":"0000-0002-9123-5854","position":5,"is_corresponding":false},{"id":624327,"name":"Paul Segars","orcid":null,"position":6,"is_corresponding":false},{"id":292053,"name":"Ehsan Samei","orcid":"0000-0001-7451-3309","position":7,"is_corresponding":false},{"id":1403375,"name":"Raj Kumar Panta","orcid":null,"position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-19T02:50:34.851930Z","pmid":"41058741","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":[]}