Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909072641490944 |
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| author | Abraham, Daniel Nishimura, Mark Cao, Xiaozhi Liao, Congyu Setsompop, Kawin |
| author_facet | Abraham, Daniel Nishimura, Mark Cao, Xiaozhi Liao, Congyu Setsompop, Kawin |
| contents | MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when imaging in the presence of field imperfections. This work (implicit GROG) provides an efficient approach to transform the field corrupted non-Cartesian data into clean Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_10823 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction Abraham, Daniel Nishimura, Mark Cao, Xiaozhi Liao, Congyu Setsompop, Kawin Signal Processing MRI data is acquired in Fourier space/k-space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). Reconstruction complexity is further exacerbated when imaging in the presence of field imperfections. This work (implicit GROG) provides an efficient approach to transform the field corrupted non-Cartesian data into clean Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI. |
| title | Implicit Representation of GRAPPA Kernels for Fast MRI Reconstruction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2310.10823 |