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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2405.15003 |
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| _version_ | 1866909209873874944 |
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| author | Sakitis, Chase J Rowe, Daniel B |
| author_facet | Sakitis, Chase J Rowe, Daniel B |
| contents | In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated per-scan and fixed over time, to fill in the missing spatial frequencies of the subsampled k-space. Hence, we propose a Bayesian approach to GRAPPA (BGRAPPA) where space measurement uncertainty are assessed from the a priori calibration k-space arrays. The prior information is utilized to estimate the missing spatial frequency values from the posterior distribution and reconstruct into full field-of-view images. Our BGRAPPA technique successfully reconstructed both a simulated and experimental single slice image with less artifacts, reduced noise leading to an increased signal-to-noise ratio (SNR), and stronger power of task detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15003 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Bayesian Approach to GRAPPA Parallel FMRI Image Reconstruction Increases SNR and Power of Task Detection Sakitis, Chase J Rowe, Daniel B Applications In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated per-scan and fixed over time, to fill in the missing spatial frequencies of the subsampled k-space. Hence, we propose a Bayesian approach to GRAPPA (BGRAPPA) where space measurement uncertainty are assessed from the a priori calibration k-space arrays. The prior information is utilized to estimate the missing spatial frequency values from the posterior distribution and reconstruct into full field-of-view images. Our BGRAPPA technique successfully reconstructed both a simulated and experimental single slice image with less artifacts, reduced noise leading to an increased signal-to-noise ratio (SNR), and stronger power of task detection. |
| title | A Bayesian Approach to GRAPPA Parallel FMRI Image Reconstruction Increases SNR and Power of Task Detection |
| topic | Applications |
| url | https://arxiv.org/abs/2405.15003 |