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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.06257 |
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| _version_ | 1866913119343738880 |
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| author | Lin, Yimeng Wang, Nan Abraham, Daniel Polak, Daniel Cao, Xiaozhi Nurdinova, Aizada Cauley, Stephen Setsompop, Kawin |
| author_facet | Lin, Yimeng Wang, Nan Abraham, Daniel Polak, Daniel Cao, Xiaozhi Nurdinova, Aizada Cauley, Stephen Setsompop, Kawin |
| contents | Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA Lin, Yimeng Wang, Nan Abraham, Daniel Polak, Daniel Cao, Xiaozhi Nurdinova, Aizada Cauley, Stephen Setsompop, Kawin Signal Processing Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data. |
| title | Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.06257 |