Figure 4.

Artifacts: (a–d) Elimination of hardware artifacts at 6.5 mT—Two slices from a 3D bSSFP (NA = 50) are shown. When reconstructed with IFFT (a,b), a vertical artifact (red arrows) is present across slices. When the same raw data was reconstructed with AUTOMAP (c,d), the artifacts are eliminated. The error maps of each slice with respect to a reference scan (NA = 100) is shown for both IFFT and AUTOMAP reconstruction. (e–g) Uncorrupted k-space (NA = 50) was reconstructed with AUTOMAP (e) and IFFT (f). The reference NA = 100 scan is shown in (g). (h–m) AUTOMAP reconstruction of simulated k-space artifacts. Two slices of the hybrid k-space from the 11-min (NA = 50) brain scan was corrupted with simulated spikes (h). In (i), the data was reconstructed with AUTOMAP trained on the standard corpus of white Gaussian noise corrupted brain MRI images. In (j), the k-space data was reconstructed with AUTOMAP with a training corpus of k-space data including variable number of random spikes. IFFT reconstructed images are shown in (k), where the spiking artifacts are clearly seen. Denoised IFFT with DnCNN reconstructed images are shown in (I) and denoised IFFT with BM3D reconstructed images are shown in (m). (n) The table summarizes image quality metrics for the reconstruction task of the three slices, both with- and without spike corruption. PSNR, SSIM and RMSE were evaluated for reconstruction using IFFT, denoised IFFT with either DnCNN or BM3D, and AUTOMAP trained on either the standard Gaussian noise-corrupted corpus or on a spike- and Gaussian noise-corrupted corpus.