Key result
SCR-DL reconstruction reduces normalized mean square error ~67% versus GRAPPA for accelerated cardiac cine imaging.
Why the study?
To develop an iterative deep learning reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging.
Does Spatio-Coil Regularized DL (SCR-DL) improve image quality and maintain functional assessment accuracy in highly accelerated cardiac cine imaging compared to standard reconstruction methods?
Does Spatio-Coil Regularized DL (SCR-DL) improve image quality and maintain functional assessment accuracy in highly accelerated cardiac cine imaging compared to standard reconstruction methods?
Absolute Event Rate: 0.03% vs 0.09%
p-value: p=< 0.001
SCR-DL enables highly accelerated cardiac cine imaging with superior image quality compared to standard methods, significantly reducing breath-hold time without compromising functional assessment.
SCR-DL may enhance image quality in accelerated cine MRI; leaves open prospective validation before clinical use.
PURPOSE: To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging. METHODS: This 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. RESULTS: The 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. CONCLUSIONS: SCR-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.
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Demirel et al. (2024) studied Cardiac cine imaging. Spatio-Coil Regularized Deep Learning (SCR-DL) reconstruction vs. GRAPPA, SENSE-DL, and STR-DL reconstruction was evaluated on Normalized mean square error (NMSE) (p=< 0.001). Spatio-Coil Regularized Deep Learning (SCR-DL) reconstruction for accelerated cardiac cine imaging significantly improved normalized mean square error (0.03 vs 0.09) compared with GRAPPA (p < 0.001).
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