Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385890355> ?p ?o ?g. }
Showing items 1 to 65 of
65
with 100 items per page.
- W4385890355 abstract "Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which requires high temporal and spatial resolution to depict rapid changes in a small fetal heart. The ability of deep learning methods to recover undersampled data could help to optimise the kt-SENSE acquisition strategy and improve non-gated kt-SENSE reconstruction quality. In this work, we explore supervised deep learning networks for reconstruction of kt-SENSE style acquired data using an extensive in vivo dataset. Having access to fully-sampled low-resolution multi-coil fetal cardiac MRI, we study the performance of the networks to recover fully-sampled data from undersampled data. We consider model architectures together with training strategies taking into account their application in the real clinical setup used to collect the dataset to enable networks to recover prospectively undersampled data. We explore a set of modifications to form a baseline performance evaluation for dynamic fetal cardiac MRI on real data. We systematically evaluate the models on coil-combined data to reveal the effect of the suggested changes to the architecture in the context of fetal heart properties. We show that the best-performers recover a detailed depiction of the maternal anatomy on a large scale, but the dynamic properties of the fetal heart are under-represented. Training directly on multi-coil data improves the performance of the models, allows their prospective application to undersampled data and makes them outperform CTFNet introduced for adult cardiac cine MRI. However, these models deliver similar qualitative performances recovering the maternal body very well but underestimating the dynamic properties of fetal heart. This dynamic feature of fast change of fetal heart that is highly localised suggests both more targeted training and evaluation methods might be needed for fetal heart application." @default.
- W4385890355 created "2023-08-17" @default.
- W4385890355 creator A5006461848 @default.
- W4385890355 creator A5010565192 @default.
- W4385890355 creator A5055171667 @default.
- W4385890355 creator A5067756486 @default.
- W4385890355 creator A5085369076 @default.
- W4385890355 creator A5088524211 @default.
- W4385890355 date "2023-08-15" @default.
- W4385890355 modified "2023-10-12" @default.
- W4385890355 title "The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning" @default.
- W4385890355 doi "https://doi.org/10.48550/arxiv.2308.07885" @default.
- W4385890355 hasPublicationYear "2023" @default.
- W4385890355 type Work @default.
- W4385890355 citedByCount "0" @default.
- W4385890355 crossrefType "posted-content" @default.
- W4385890355 hasAuthorship W4385890355A5006461848 @default.
- W4385890355 hasAuthorship W4385890355A5010565192 @default.
- W4385890355 hasAuthorship W4385890355A5055171667 @default.
- W4385890355 hasAuthorship W4385890355A5067756486 @default.
- W4385890355 hasAuthorship W4385890355A5085369076 @default.
- W4385890355 hasAuthorship W4385890355A5088524211 @default.
- W4385890355 hasBestOaLocation W43858903551 @default.
- W4385890355 hasConcept C108583219 @default.
- W4385890355 hasConcept C111919701 @default.
- W4385890355 hasConcept C119666444 @default.
- W4385890355 hasConcept C119857082 @default.
- W4385890355 hasConcept C121332964 @default.
- W4385890355 hasConcept C151730666 @default.
- W4385890355 hasConcept C154945302 @default.
- W4385890355 hasConcept C163985040 @default.
- W4385890355 hasConcept C2779343474 @default.
- W4385890355 hasConcept C2780226545 @default.
- W4385890355 hasConcept C41008148 @default.
- W4385890355 hasConcept C62520636 @default.
- W4385890355 hasConcept C86803240 @default.
- W4385890355 hasConceptScore W4385890355C108583219 @default.
- W4385890355 hasConceptScore W4385890355C111919701 @default.
- W4385890355 hasConceptScore W4385890355C119666444 @default.
- W4385890355 hasConceptScore W4385890355C119857082 @default.
- W4385890355 hasConceptScore W4385890355C121332964 @default.
- W4385890355 hasConceptScore W4385890355C151730666 @default.
- W4385890355 hasConceptScore W4385890355C154945302 @default.
- W4385890355 hasConceptScore W4385890355C163985040 @default.
- W4385890355 hasConceptScore W4385890355C2779343474 @default.
- W4385890355 hasConceptScore W4385890355C2780226545 @default.
- W4385890355 hasConceptScore W4385890355C41008148 @default.
- W4385890355 hasConceptScore W4385890355C62520636 @default.
- W4385890355 hasConceptScore W4385890355C86803240 @default.
- W4385890355 hasLocation W43858903551 @default.
- W4385890355 hasOpenAccess W4385890355 @default.
- W4385890355 hasPrimaryLocation W43858903551 @default.
- W4385890355 hasRelatedWork W2795261237 @default.
- W4385890355 hasRelatedWork W3014300295 @default.
- W4385890355 hasRelatedWork W3164822677 @default.
- W4385890355 hasRelatedWork W4223943233 @default.
- W4385890355 hasRelatedWork W4225161397 @default.
- W4385890355 hasRelatedWork W4312200629 @default.
- W4385890355 hasRelatedWork W4360585206 @default.
- W4385890355 hasRelatedWork W4364306694 @default.
- W4385890355 hasRelatedWork W4380075502 @default.
- W4385890355 hasRelatedWork W4380086463 @default.
- W4385890355 isParatext "false" @default.
- W4385890355 isRetracted "false" @default.
- W4385890355 workType "article" @default.