FIG. S1 Rates of speed increase for Toeplitz compared to NUFFT for the gradient computation (i.e., for NUFFT and for Toeplitz) for both CPU and GPU implementations. Results for both 3D radial and 3D cones trajectories are shown. The left panels show trends as the kernel width and oversampling ratio are varied, while the undersampling rate is fixed at 4 and the FOV size factor relative to the nominal FOV of is fixed at 1.5. The right panel shows trends as the undersampling rate and size factor are varied, while the NUFFT oversampling and kernel width are fixed to 1.5 and 4, respectively. The speed increase rates in decibels (dB) were computed using . The 3D radial trajectory has greater speed increases because it has more k‐space samples, which increases the duration of NUFFT convolutions. FIG. S2 Convergence of normalized root‐mean‐square‐error (RMSE) for 3D cones and 3D radial trajectories when κ = 0. When an initialization obtained from two conjugate gradient (CG) iterations is used (dashed), early iterations have a much lower RMSE than when an initialization of zero is used (solid). However, for highly accurate solutions the number of iterations required is similar for both cases. As κ is increased, the differences between these choices of initialization are diminished further.