Accelerated cardiac cine with spatio‐coil regularized deep learning reconstruction

Abstract

PurposeTo develop an iterative deep learning (DL) reconstruction with spatio‐coil regularization and multichannel k‐space data consistency for accelerated cine imaging.MethodsThis study proposes a Spatio‐Coil Regularized DL (SCR‐DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR‐DL uses shift‐invariant convolutional kernels to interpolate missing k‐space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors. At 8‐fold acceleration, SCR‐DL was compared with Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), sensitivity encoding (SENSE)‐based DL and spatio‐temporal regularized (STR)–DL reconstruction. In the retrospective undersampled cine, images were quantitatively evaluated using normalized mean square error (NMSE) and structural similarity index measure (SSIM). Additionally, agreement for left‐ventricular ejection fraction and left‐ventricular mass were assessed using prospectively accelerated cine images at 2‐fold and 8‐fold accelerations.ResultsThe SCR‐DL algorithm successfully reconstructed highly accelerated cine images. SCR‐DL had significant improvements in NMSE (0.03 ± 0.02) and SSIM (91.4% ± 2.7%) compared with GRAPPA (NMSE: 0.09 ± 0.04, SSIM: 69.9% ± 11.1%; p < 0.001), SENSE‐DL (NMSE: 0.07 ± 0.04, SSIM: 86.9% ± 3.2%; p < 0.001), and STR‐DL (NMSE: 0.04 ± 0.03, SSIM: 90.0% ± 2.5%; p < 0.001) with retrospective undersampled cine. Despite the 3‐fold reduction in scan time, there was no difference between left‐ventricular ejection fraction (59.8 ± 4.5 vs. 60.8 ± 4.8, p = 0.46) or left‐ventricular mass (73.6 ± 19.4 g vs. 73.2 ± 19.7 g, p = 0.95) between R = 2 and R = 8 prospectively accelerated cine images.ConclusionsSCR‐DL enabled highly accelerated cardiac cine imaging, significantly reducing breath‐hold time. Compared with GRAPPA or SENSE‐DL, images reconstructed with SCR‐DL showed superior NMSE and SSIM.