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Optimizing data acquisition in undersampled magnetic resonance imaging (MRI) using two alternative forced choice
Tavianne M Kemp1, Tetsuya A Kawakita1, Rehan Mehta1
1Mathematics Department, Manhattan College, Riverdale, NY, 10471, USA.
Summary
Optimizing undersampling in MRI frequency domain (k-space) improves image quality. Fully sampling more low frequencies enhances lesion detection in 2-AFC tasks, while search tasks show consistent performance after 2.5% low-frequency sampling.
Area of Science:
- Medical Imaging
- Radiology
- Magnetic Resonance Imaging
Background:
- Accelerated data acquisition in MRI is crucial for reducing scan times.
- Undersampling in k-space is a common technique, typically involving full acquisition of low frequencies and undersampling of higher frequencies.
- The trade-off between undersampling extent and image quality, particularly for lesion detection, requires further investigation.
Purpose of the Study:
- To investigate the impact of varying the fraction of fully sampled low k-space frequencies on lesion detectability in undersampled MRI.
- To compare human observer performance in signal detection and search tasks under different undersampling strategies.
- To determine the optimal k-space sampling strategy for fluid-attenuated inversion recovery (FLAIR) brain imaging.
Main Methods:
- Utilized a fixed 1D undersampling factor of 5x for k-space data acquisition.
- Varied the percentage of fully acquired low k-space frequencies from 0% to 20%.
- Reconstructed images using multi-coil SENSE with no regularization and evaluated performance using human observer 2-AFC and search tasks with simulated lesions on fastMRI FLAIR brain images.
Main Results:
- Human observer performance in the 2-AFC task improved with an increasing fraction of fully sampled low frequencies.
- For the search task, performance initially improved with 2.5% low-frequency sampling and then plateaued.
- Different tasks exhibited distinct relationships between performance and acquired data, with the search task aligning with common clinical practice (5-10% low-frequency sampling).
Conclusions:
- The optimal strategy for undersampling in MRI depends on the specific task (detection vs. search).
- Increasing the fully sampled low-frequency content benefits tasks requiring precise detection of small lesions.
- Current clinical practice for MRI undersampling appears to balance performance across various detection and search scenarios.
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