FIGURE 3.

Responses of CNN models to the edge phantom at different angles and across CNR levels. (A) and (B) show profiles across the edge phantom for the raw, Gibbs-corrupted data, x_t, and the CCNN/MCNN models, $f_{\widehat{\theta }}\left({\mathbf{x}}_t\right)$, for edges at different angles. Both the CCNN and the MCNN method reduce the Gibbs artifacts. (C) shows a composite image of CCNN network estimates across a variety of CNRs from 0.25 to 10, while (D) shows the change in estimated FWHM as a function of CNR for the CCNN model. Above a CNR of 1, CNN performance is fairly stable. Below a CNR of 1, the FWHM increases as the CNR approaches 0