Frahm, Jens

[["dc.bibliographiccitation.artnumber","3527269"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.bibliographiccitation.lastpage","11"],["dc.bibliographiccitation.volume","2017"],["dc.contributor.author","Schaetz, Sebastian"],["dc.contributor.author","Voit, Dirk"],["dc.contributor.author","Frahm, Jens"],["dc.contributor.author","Uecker, Martin"],["dc.date.accessioned","2019-07-09T11:45:03Z"],["dc.date.accessioned","2020-05-13T11:05:04Z"],["dc.date.available","2019-07-09T11:45:03Z"],["dc.date.available","2020-05-13T11:05:04Z"],["dc.date.issued","2017"],["dc.description.abstract","To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV) algorithm for dynamic MRI with high frame rates."],["dc.identifier.doi","10.1155/2017/3527269"],["dc.identifier.pmid","29463984"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15017"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59150"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65303"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","Accelerated Computing in Magnetic Resonance Imaging: Real-Time Imaging Using Nonlinear Inverse Reconstruction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber",";e4184"],["dc.bibliographiccitation.journal","NMR in Biomedicine"],["dc.contributor.author","Kollmeier, Jost M."],["dc.contributor.author","Tan, Zhengguo"],["dc.contributor.author","Joseph, Arun A."],["dc.contributor.author","Kalentev, Oleksandr"],["dc.contributor.author","Voit, Dirk"],["dc.contributor.author","Merboldt, K. Dietmar"],["dc.contributor.author","Frahm, Jens"],["dc.date.accessioned","2019-11-14T10:39:33Z"],["dc.date.accessioned","2021-10-27T13:21:27Z"],["dc.date.available","2019-11-14T10:39:33Z"],["dc.date.available","2021-10-27T13:21:27Z"],["dc.date.issued","2019"],["dc.description.abstract","The purpose of this work was to develop an acquisition and reconstruction technique for two- and three-directional (2d and 3d) phase-contrast flow MRI in real time. A previous real-time MRI technique for one-directional (1d) through-plane flow was extended to 2d and 3d flow MRI by introducing in-plane flow sensitivity. The method employs highly undersampled radial FLASH sequences with sequential acquisitions of two or three flow-encoding datasets and one flow-compensated dataset. Echo times are minimized by merging the waveforms of flow-encoding and radial imaging gradients. For each velocity direction individually, model-based reconstructions by regularized nonlinear inversion jointly estimate an anatomical image, a set of coil sensitivities and a phase-contrast velocity map directly. The reconstructions take advantage of a dynamic phase reference obtained by interpolating consecutive flow-compensated acquisitions. Validations include pulsatile flow phantoms as well as in vivo studies of the human aorta at 3 T. The proposed method offers cross-sectional 2d and 3d flow MRI of the human aortic arch at 53 and 67 ms resolution, respectively, without ECG synchronization and during free breathing. The in-plane resolution was 1.5 × 1.5 mm2 and the slice thickness 6 mm. In conclusion, real-time multi-directional flow MRI offers new opportunities to study complex human blood flow without the risk of combining differential phase (i.e., velocity) information from multiple heartbeats as for ECG-gated data. The method would benefit from a further reduction of acquisition time and accelerated computing to allow for extended clinical trials."],["dc.identifier.doi","10.1002/nbm.4184"],["dc.identifier.pmid","31580524"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16662"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92022"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1099-1492"],["dc.relation.issn","1099-1492"],["dc.relation.issn","0952-3480"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.subject.ddc","610"],["dc.title","Real‐time multi‐directional flow MRI using model‐based reconstructions of undersampled radial FLASH – A feasibility study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]