| Datensatz noch nicht abgeschlossen | |
| Zugriffsnummer | 55562 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Physics-informed deep learning for shear wave speed estimation in MR elastography |
| Autor(in); Institution |
Sack, Ingolf; Charité - Universitätsmedizin Berlin, GERMANY
|
| Quelle/Jahr | IEEE Transactions on Biomedical Engineering:(2026), 1 - 12 |
| Availability | [preprint |
| ISSN | 0018-9294 (print) ; 1558-2531 (online) |
| DOI | |
| URL | |
| Verlag | Institute of Electrical and Electronics Engineers (IEEE) |
| Freie Schlagworte | Deep Learning ; MR Elastography (MRE) |
| Zusammenfassung | Abstract Objective: Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique for mapping biomechanical properties of in vivo tissue, including shear wave speed (SWS), but involves intrinsically slow data acquisition and an ill-posed wave inversion. Instead of relying on handcrafted image priors, we propose a data-driven approach jointly combining image reconstruction and MRE inversion for robust SWS estimation from undersampled k-space data. Methods: Our physics-informed reconstruction framework comprises two blocks: a model-based neural network (NN)-regularized reconstruction module and a phase-gradient inversion (k-MDEV) calculating SWS from the reconstructed images. Concatenating both blocks yields an end-to-end trainable method to estimate SWS directly from measured k-space data. We evaluated the method on retrospectively highly undersampled brain MRE data and compared it to a total variation (TV) minimization-based approach. We assessed the impact of end-to-end training (qualitative images and SWS maps as targets) versus pre-training (qualitative images as targets) and applied the method also to in vivo data. Results: Our approach significantly reduces NRMSE by 30% compared to TV. End-to-end training improves SWS estimation over separate image reconstruction and SWS calculation. Conclusion: Accurate SWS quantification is possible at acceleration factors up to 19. Our method significantly outperforms TV, highlighting the need for data-driven regularization in this challenging MR problem. Further, our approach successfully generalizes to in vivo data. Significance: We present the first end-to-end trainable MRE reconstruction method for estimating SWS maps directly from k-space. NN-based reconstruction can enable rapid stiffness mapping for dynamic studies, functional imaging, and real-time clinical feedback. |
Zitierung
Martin, S., Schattenfroh, J., Schünke, P., Zimmermann, F. F., Sack, I., Kolbitsch, C., & Kofler, A. (2026). Physics-informed deep learning for shear wave speed estimation in MR elastography. IEEE Transactions on Biomedical Engineering, 1–12. https://doi.org/10.1109/tbme.2026.3666306