{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Grigoriou A"],"funding":["Banco Bilbao Vizcaya Argentaria (BBVA)","\"la Caixa\" Foundation (Caixa Foundation)","Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)","Prostate Cancer Foundation (PCF)","AstraZeneca","Departament d'Innovació, Universitats i Empresa, Generalitat de Catalunya (Department of Innovation, Education and Enterprise, Government of Catalonia)","Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación)","Fundación Científica Asociación Española Contra el Cáncer (Scientific Foundation, Spanish Association Against Cancer)","EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)","EC | Directorate-General for Employment, Social Affairs and Inclusion | European Social Fund (Fondo Social Europeo)"],"pagination":["1695"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12657972"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(1)"],"pubmed_abstract":["Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the da"],"journal":["Communications biology"],"pubmed_title":["Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework."],"pmcid":["PMC12657972"],"funding_grant_id":["CEX2020-001024-S / AEI / 10.13039 / 501100011033","PREdICT","PI18/01395","18YOUN19","LCF/BQ/PR22/11920010","PRYCO211023SERR","PRE2022-102586","CaixaResearch Advanced Oncology","PI21/01019","801370","2019 BP 00182","2023PROD00178","A way to make Europe","89/2017"],"pubmed_authors":["Grigoriou A","Escriche A","Voronova AK","Sala-Llonch R","Bernatowicz K","Simonetti S","Nuciforo P","Barba I","Toledo R","Perez-Lopez R","Escobar M","Grussu F","Fieremans E","Novikov DS","Serna G","Navarro-Garcia D","Greco E","Abad M","Macarro C","Roson N","Mast R","Vieito M","Garralda E","Merino X","Palombo M"],"additional_accession":[]},"is_claimable":false,"name":"Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.","description":"Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the da","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T21:12:27.253Z","creation":"2026-05-21T03:13:19.728Z"},"accession":"S-EPMC12657972","cross_references":{"pubmed":["41298809"],"doi":["10.1038/s42003-025-09096-3"]}}