From e5b2654ddad020f256f98d18b9dd5656a8744ba6 Mon Sep 17 00:00:00 2001 From: Pierre-Antoine Comby Date: Fri, 1 Nov 2024 13:11:20 +0100 Subject: [PATCH] Update paper.md --- docs/paper-joss/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/paper-joss/paper.md b/docs/paper-joss/paper.md index 7c3a590d..b5304241 100644 --- a/docs/paper-joss/paper.md +++ b/docs/paper-joss/paper.md @@ -129,7 +129,7 @@ MRI-NUFFT comes with a wide variety of non-Cartesian trajectory generation routi Following the formulation of [@wang_efficient_2023], MRI-NUFFT provides automatic differentiation for all NUFFT backends, with respect to both gradients and data (image or k-space). This enables efficient backpropagation through NUFFT operators and supports research on learned sampling model and image reconstruction network. # MRI-NUFFT utilization -MRI-NUFFT is already used in conjunction with other software such as SNAKE-fMRI [@comby_snake-fmri_2024], deepinv [@tachella_deepinverse_2023] and PySAP-MRI [@farrens_pysap_2020;@gueddarri_pysap-mri_2020] +MRI-NUFFT is already used in conjunction with other software such as SNAKE-fMRI [@comby_snake-fmri_2024], deepinv [@tachella_deepinverse_2023] and PySAP-MRI [@farrens_pysap_2020; @gueddari_pysap-mri_2020] # References