  Recent research has explored using neural networks to reconstruct
undersampled magnetic resonance imaging (MRI) data. Because of the complexity
of the artifacts in the reconstructed images, there is a need to develop
task-based approaches of image quality. Common metrics for evaluating image
quality like the normalized root mean squared error (NRMSE) and structural
similarity (SSIM) are global metrics which average out impact of subtle
features in the images. Using measures of image quality which incorporate a
subtle signal for a specific task allow for image quality assessment which
locally evaluates the effect of undersampling on a signal. We used a U-Net to
reconstruct under-sampled images with 2x, 3x, 4x and 5x fold 1-D undersampling
rates. Cross validation was performed for a 500 and a 4000 image training set
with both structural similarity (SSIM) and mean squared error (MSE) losses. A
two alternative forced choice (2-AFC) observer study was carried out for
detecting a subtle signal (small blurred disk) from images with the 4000 image
training set. We found that for both loss functions and training set sizes, the
human observer performance on the 2-AFC studies led to a choice of a 2x
undersampling but the SSIM and NRMSE led to a choice of a 3x undersampling. For
this task, SSIM and NRMSE led to an overestimate of the achievable
undersampling using a U-Net before a steep loss of image quality when compared
to the performance of human observers in the detection of a subtle lesion.
