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INR meets multi-contrast MRI reconstruction.
In: (Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis). Berlin [u.a.]: Springer, 2026. 23-33 (Lect. Notes Comput. Sc. ; 16150 LNCS)
Multi-contrast MRI sequences allow for the acquisition of images with varying tissue contrast within a single scan. The resulting multi-contrast images can be used to extract quantitative information on tissue microstructure. To make such multi-contrast sequences feasible for clinical routine, the usually very long scan times need to be shortened e.g. through undersampling in k-space. However, this comes with challenges for the reconstruction. In general, advanced reconstruction techniques such as compressed sensing or deep learning-based approaches can enable the acquisition of high-quality images despite the acceleration. In this work, we leverage redundant anatomical information of multi-contrast sequences to achieve even higher acceleration rates. We use undersampling patterns that capture the contrast information located at the k-space center, while performing complementary undersampling across contrasts for high frequencies. To reconstruct this highly sparse k-space data, we propose an implicit neural representation (INR) network that is ideal for using the complementary information acquired across contrasts as it jointly reconstructs all contrast images. We demonstrate the benefits of our proposed INR method by applying it to multi-contrast MRI using the MPnRAGE sequence, where it outperforms the state-of-the-art parallel imaging compressed sensing (PICS) reconstruction method, even at higher acceleration factors. Our code is available at https://github.com/compai-lab/2025-miccai-niessen.
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Publication type
Article: Conference contribution
Keywords
Implicit Neural Representation ; Mri Reconstruction ; Multi-contrast Mri ; Quantitative Mri
Language
english
Publication Year
2026
Prepublished in Year
2025
HGF-reported in Year
2025
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis
Quellenangaben
Volume: 16150 LNCS,
Pages: 23-33
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
Scopus ID
105019644183
Erfassungsdatum
2025-10-31