| Autor: |
Singh NM; Massachusetts Institute of Technology., Dey N; Massachusetts Institute of Technology., Hoffmann M; Athinoula A. Martinos Center for Biomedical Imaging.; Harvard Medical School., Fischl B; Massachusetts Institute of Technology.; Athinoula A. Martinos Center for Biomedical Imaging.; Harvard Medical School., Adalsteinsson E; Massachusetts Institute of Technology., Frost R; Athinoula A. Martinos Center for Biomedical Imaging.; Harvard Medical School., Dalca AV; Massachusetts Institute of Technology.; Athinoula A. Martinos Center for Biomedical Imaging.; Harvard Medical School., Golland P; Massachusetts Institute of Technology. |
| Jazyk: |
angličtina |
| Zdroj: |
Proceedings of machine learning research [Proc Mach Learn Res] 2024; Vol. 227, pp. 368-381. |
| Abstrakt: |
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone. Our network produces a reconstruction as a function of two inputs: corrupted k-space data and motion parameters. We train the network using simulated, motion-corrupted k-space data generated with known motion parameters. At test-time, we estimate unknown motion parameters by minimizing a data consistency loss between the motion parameters, the network-based image reconstruction given those parameters, and the acquired measurements. Intra-slice motion correction experiments on simulated and realistic 2D fast spin echo brain MRI achieve high reconstruction fidelity while providing the benefits of explicit data consistency optimization. Our code is publicly available at https://www.github.com/nalinimsingh/neuroMoCo. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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