  Purpose: Task-based assessment of image quality in undersampled magnetic
resonance imaging provides a way of evaluating the impact of regularization on
task performance. In this work, we evaluated the effect of total variation (TV)
and wavelet regularization on human detection of signals with a varying
background and validated a model observer in predicting human performance.
  Approach: Human observer studies used two-alternative forced choice (2-AFC)
trials with a small signal known exactly task but with varying backgrounds for
fluid-attenuated inversion recovery images reconstructed from undersampled
multi-coil data. We used a 3.48 undersampling factor with TV and a wavelet
sparsity constraints. The sparse difference-of-Gaussians (S-DOG) observer with
internal noise was used to model human observer detection.
  Results: We observed a trend that the human observer detection performance
remained fairly constant for a broad range of values in the regularization
parameter before decreasing at large values. A similar result was found for the
normalized ensemble root mean squared error. Without changing the internal
noise, the model observer tracked the performance of the human observers as the
regularization was increased but overestimated the PC for large amounts of
regularization for TV and wavelet sparsity, as well as the combination of both
parameters.
  Conclusions: For the task we studied, the S-DOG observer was able to
reasonably predict human performance with both TV and wavelet sparsity
regularizers over a broad range of regularization parameters. We observed a
trend that task performance remained fairly constant for a range of
regularization parameters before decreasing for large amounts of
regularization.
