PuSH - Publikationsserver des Helmholtz Zentrums München

Niessen, N.* ; Pirkl, C.M.* ; Solana, A.B.* ; Eichhorn, H. ; Spieker, V. ; Huang, W.* ; Sprenger, T.* ; Menzel, M.I.* ; Schnabel, J.A.

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)
DOI
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Implicit Neural Representation ; Mri Reconstruction ; Multi-contrast Mri ; Quantitative Mri
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 16150 LNCS, Heft: , Seiten: 23-33 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)