Ms. Tavianne M. Kemp Profile

Tavianne M. Kemp

at Manhattan College

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Proceedings Article | 3 April 2023 Presentation + Paper

Optimizing data acquisition in undersampled magnetic resonance imaging (MRI) using two alternative forced choice (2-AFC) and search tasks

Tavianne Kemp, Tetsuya Kawakita, Rehan Mehta, Angel Pineda

Proceedings Volume 12467, 124670U (2023) https://doi.org/10.1117/12.2654323

KEYWORDS: Magnetic resonance imaging, Data acquisition, Aliasing, Signal detection, Performance modeling, Image quality

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Undersampling in the frequency domain (k-space) in MRI accelerates the data acquisition. Typically, a fraction of the low frequencies is fully collected and the rest are equally undersampled. We used a fixed 1D undersampling factor of 5x where 20% of the k-space lines are collected but varied the fraction of the low k-space frequencies that are fully sampled. We used a range of fully acquired low k-space frequencies from 0% where the primary artifact is aliasing to 20% where the primary artifact is blurring in the undersampling direction. Small lesions were placed in the coil k-space data for fluid-attenuated inversion recovery (FLAIR) brain images from the fastMRI database. The images were reconstructed using a multi-coil SENSE reconstruction with no regularization. We conducted a human observer two-alternative forced choice (2-AFC) study with a signal known exactly and a search task with variable backgrounds for each of the acquisitions. We found that for the 2-AFC task, the average human observer did better with more of the low frequencies being fully sampled. For the search task, we found that after an initial improvement from having none of the low frequencies fully sampled to just 2.5%, the performance remained fairly constant. We found that the performance in the two tasks had a different relationship to the acquired data. We also found that the search task was more consistent with common practice in MRI where a range of frequencies between 5% and 10% of the low frequencies are fully sampled.

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