Deep learning‐based rapid image reconstruction and motion correction for high‐resolution cartesian first‐pass myocardial perfusion imaging at 3T

Abstract Purpose To develop and evaluate a deep learning (DL) ‐based rapid image reconstruction and motion correction technique for high‐resolution Cartesian first‐pass myocardial perfusion imaging at 3T with whole‐heart coverage for both single‐slice (SS) and simultaneous multi‐slice (SMS) acquisitions. Methods 3D physics‐driven unrolled network architectures were utilized for the reconstruction of high‐resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U‐Net based motion corrected network was proposed, and the temporal fidelity and second‐order derivative were calculated to assess the performance of the motion correction. Results Excellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 4.3, 4.4, 4.5 4.4, 4.6, 4.3 4.3, 4.4, and 4.5 4.3, 4.5) for SS DL and SS L1‐SENSE, MB = 2 DL and MB = 2 SMS‐L1‐SENSE, respectively, showing no statistically significant difference ( p > 0.05 for SS and SMS) between (SMS)‐L1‐SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)‐L1‐SENSE with GPU acceleration was approximately 30 min. Conclusion The proposed DL‐based image reconstruction and motion correction technique enabled rapid and high‐quality reconstruction for SS and SMS MB = 2 high‐resolution Cartesian first‐pass perfusion imaging at 3T.