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Spieker, V. ; Huang, W.* ; Eichhorn, H. ; Stelter, J.* ; Weiss, K.* ; Zimmer, V.A. ; Braren, R.F.* ; Karampinos, D.C.* ; Hammernik, K.* ; Schnabel, J.A.

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)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
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
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Book Volume Title Deep Generative Models
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14533 LNCS, Issue: , Pages: 183-192 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
Grants Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"
DOI 10.1007/978-3-031-53767-7_18
WOS ID 001207831600018
Scopus ID 85187660449
Erfassungsdatum 2024-04-25
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