{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["67(13)"],"submitter":["Han P"],"funding":["NIBIB NIH HHS","NCI NIH HHS","National Institutes of Health"],"pubmed_abstract":["<i>Objective.</i>To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency.<i>Approach.</i>In SPIDERM, a 'prep' scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A 'live' scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the 'prep'-learned subspace and 'live' coordinates, real-time 3D images are generated on-the-fly with compu"],"journal":["Physics in medicine and biology"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9680913"],"repository":["biostudies-literature"],"pubmed_title":["Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation."],"pmcid":["PMC9680913"],"funding_grant_id":["R01 EB028146","R01 EB029088","R21 CA234637"],"pubmed_authors":["Christodoulou AG","Han P","Yang W","Cao M","Chen J","Hu Z","Li D","Fan Z","Xiao J","Ling DC","Han F"],"additional_accession":[]},"is_claimable":false,"name":"Single projection driven real-time multi-contrast (SPIDERM) MR imaging using pre-learned spatial subspace and linear transformation.","description":"<i>Objective.</i>To develop and test the feasibility of a novel Single ProjectIon DrivEn Real-time Multi-contrast (SPIDERM) MR imaging technique that can generate real-time 3D images on-the-fly with flexible contrast weightings and a low latency.<i>Approach.</i>In SPIDERM, a 'prep' scan is first performed, with sparse k-space sampling periodically interleaved with the central k-space line (navigator data), to learn a subject-specific model, incorporating a spatial subspace and a linear transformation between navigator data and subspace coordinates. A 'live' scan is then performed by repeatedly acquiring the central k-space line only to dynamically determine subspace coordinates. With the 'prep'-learned subspace and 'live' coordinates, real-time 3D images are generated on-the-fly with compu","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2025-04-05T10:57:51.522Z","creation":"2025-04-05T10:57:51.522Z"},"accession":"S-EPMC9680913","cross_references":{"pubmed":["35697010"],"doi":["10.1088/1361-6560/ac783e"]}}