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Open Access Green as soon as Postprint is submitted to ZB.
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space.
In: Deep Generative Models. Berlin [u.a.]: Springer, 2024. 183-192 (Lect. Notes Comput. Sc. ; 14533 LNCS)
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts. In this work, we generate blurring-free motion-resolved abdominal reconstructions by learning a neural implicit representation directly in k-space (NIK). Using measured sampling points and a data-derived respiratory navigator signal, we train a network to generate continuous signal values. To aid the regularization of sparsely sampled regions, we introduce an additional informed correction layer (ICo), which leverages information from neighboring regions to correct NIK’s prediction. The proposed generative reconstruction methods, NIK and ICoNIK, outperform standard motion-resolved reconstruction techniques and provide a promising solution to address motion artefacts in abdominal MRI.
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Publication type
Article: Edited volume or book chapter
Keywords
Motion-resolved Abdominal Mri ; Mri Reconstruction ; Neural Implicit Representations ; Parallel Imaging
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Book Volume Title
Deep Generative Models
Quellenangaben
Volume: 14533 LNCS,
Pages: 183-192
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
Grants
Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"