Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910499332947968 |
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| author | Eichhorn, Hannah Spieker, Veronika Hammernik, Kerstin Saks, Elisa Weiss, Kilian Preibisch, Christine Schnabel, Julia A. |
| author_facet | Eichhorn, Hannah Spieker, Veronika Hammernik, Kerstin Saks, Elisa Weiss, Kilian Preibisch, Christine Schnabel, Julia A. |
| contents | We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://github.com/HannahEichhorn/PHIMO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08298 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI Eichhorn, Hannah Spieker, Veronika Hammernik, Kerstin Saks, Elisa Weiss, Kilian Preibisch, Christine Schnabel, Julia A. Image and Video Processing We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://github.com/HannahEichhorn/PHIMO. |
| title | Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2403.08298 |