Falk Christian Mayer1,2, Peter Bachert1,2, Mark E Ladd1,2,3, and Benjamin Knowles1
1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, 3Faculty of Medicine, Heidelberg University, Heidelberg, Germany
1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 2Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, 3Faculty of Medicine, Heidelberg University, Heidelberg, Germany
An implementation of the NUFFT using optimized algorithms on the GPU was measured to perform an adjoint transform within 1ms, and iterative inverse solution found within 10ms. This algorithm may have useful applications in real-time imaging.
Figure 5: Comparison of the execution times for inverse NUFFT using FNUFFT and CUNFFT with increasing grid size, total variation and varying number of iterations for both kernels.
Figure 2: Comparison of the adjoint execution times for the CUNFFT and FNUFFT algorithms for differing grid sizes. The kernel width was 2 for the convolution. The k-space data were reconstructed with 32-channels for both in (a) and with 32 channels for FNUFFT and one channel for CUNNFT in (b)