Modeling human observer detection for varying data acquisition in undersampled MRI for two-alternative forced choice (2-AFC) and forced localization tasks.

Undersampling in the frequency domain (k-space) in MRI enables faster data acquisition. In this study, we used a fixed 1D undersampling factor of 5x with only 20% of the k-space collected. The fraction of fully acquired low k-space frequencies were varied from 0% (all aliasing) to 20% (all blurring). The images were reconstructed using a multi-coil SENSE algorithm. We used two-alternative forced choice (2-AFC) and the forced localization tasks with a subtle signal to estimate the human observer performance. The 2-AFC average human observer performance remained fairly constant across all imaging conditions. The forced localization task performance improved from the 0% condition to the 2.5% condition and remained fairly constant for the remaining conditions, suggesting that there was a decrease in task performance only in the pure aliasing situation. We modeled the average human performance using a sparse-difference of Gaussians (SDOG) Hotelling observer model. Because the blurring in the undersampling direction makes the mean signal asymmetric, we explored an adaptation for irregular signals that made the SDOG template asymmetric. To improve the observer performance, we also varied the number of SDOG channels from 3 to 4. We found that despite the asymmetry in the mean signal, both the symmetric and asymmetric models reasonably predicted the human performance in the 2-AFC experiments. However, the symmetric model performed slightly better. We also found that a symmetric SDOG model with 4 channels implemented using a spatial domain convolution and constrained to the possible signal locations reasonably modeled the forced localization human observer results.

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

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.