| Zugriffsnummer | 52531 |
| Dokumenttyp | Konferenzartikel |
| Peer Review | unbekannt |
| Sprache | Englisch |
| Titel | MR elastography image reconstruction using spatio-temporal neural networks-based regularization |
| Autor(in); Institution |
Schattenfroh, Jakob; Charité - Universitätsmedizin Berlin, Berlin, GERMANY
Sack, Ingolf; Charité - Universitätsmedizin Berlin, Berlin, GERMANY
|
| Quelle/Jahr | Proceedings of the International Society for Magnetic Resonance in Medicine:(2024), 1 - 3 |
| Artikelnummer | 4437 |
| DOI | |
| URL | |
| Verlag | ISMRM |
| Konferenzangaben | Annual Meeting of ISMRM, Singapore, Singapore, Singapore, 04-09, Mai, 2024, Singapore |
| Freie Schlagworte | Quantitative Imaging ; Elastography |
| Zusammenfassung | The mechanical properties of tissue can alter when it is affected by disease. For instance, fibrosis can increase tissue stiffness of the liver. Magnetic Resonance Elastography (MRE)2 is a non-invasive tool to assess pathological changes by transmitting mechanical waves through the tissue. The induced motion is evaluated with motion-encoding gradients and encoded in the phase of the obtained MR images. Subsequently, shear wave speed (SWS) maps can be estimated by applying inversion methods to the complex-valued wavenumber k*. Currently, most of the MRE research is related to the development of these inversion methods. However, they are constrained by the quality of the images used, limiting their applicability to real-time MRE, since this requires acquiring undersampled data. Consequently, the obtained images suffer from reduced quality, resulting in a lack of details within the SWS maps. We propose utilizing unrolled CNN-based iterative reconstruction schemes to fill the gap between data acquisition and elastography inversion algorithms. |
| Förderinformationen (1) | Förderername: Funding from the German Research Foundation acknowledged (GRK2260, BIOQIC). |
Zitierung
Martin, S., Schünke, P., Schattenfroh, J., Kolbitsch, C., Sack, I., & Kofler, A. (2024). MR elastography image reconstruction using spatio-temporal neural networks-based regularization. Annual Meeting of ISMRM, Singapore, Singapore, Singapore, 04-09, Mai, 2024, Singapore. https://doi.org/10.58530/2024/4437