DEep learning‐based rapid Spiral Image REconstruction (DESIRE) for high‐resolution spiral first‐pass myocardial perfusion imaging

• Published in: NMR in biomedicine Vol. 35; no. 5; pp. e4661 - n/a
• Main Authors: Wang, Junyu, Weller, Daniel S., Kramer, Christopher M., Salerno, Michael
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.05.2022
• Subjects: Biological products; Deep Learning; first‐pass perfusion; Humans; Image enhancement; Image processing; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Indexing; Magnetic Resonance Imaging; Myocardial Perfusion Imaging - methods; Perfusion; Prospective Studies; Retrospective Studies; spiral; Statistical analysis; spiral; deep learning; first-pass perfusion; image reconstruction
• ISSN: 0952-3480; 1099-1492; 1099-1492
• DOI: 10.1002/nbm.4661

The objective of the current study was to develop and evaluate a DEep learning‐based rapid Spiral Image REconstruction (DESIRE) technique for high‐resolution spiral first‐pass myocardial perfusion imaging with whole‐heart coverage, to provide fast and accurate image reconstruction for both single‐slice (SS) and simultaneous multislice (SMS) acquisitions. Three‐dimensional U‐Net–based image enhancement architectures were evaluated for high‐resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal‐to‐noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1‐SPIRiT, MB = 2 DESIRE, and MB = 2 SMS‐slice‐L1‐SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1‐SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1‐SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high‐quality image reconstruction for both SS and SMS MB = 2 whole‐heart high‐resolution spiral perfusion imaging. We developed and evaluated a DEep learning‐based rapid Spiral Image Reconstruction (DESIRE) technique for high‐resolution spiral first‐pass myocardial perfusion imaging with whole‐heart coverage, to provide fast and accurate image reconstruction for both single‐slice (SS) and simultaneous multislice (SMS) acquisitions. A limitation of iterative non‐Cartesian techniques is the long reconstruction time that limits clinical translation. The DESIRE technique can reconstruct dynamic image series in under 1 s with very high image quality, which could facilitate clinical translation of these techniques.