1. Eitel, I, Friedrich, MG. T2-weighted cardiovascular magnetic resonance in acute cardiac disease. J Cardiovasc Magn Reson 2011; 13: 13.
Google Scholar | Crossref | Medline | ISI2. Keegan, J, Gatehouse, PD, Prasad, SK, et al. Improved turbo spin-echo imaging of the heart with motion-tracking. J Magn Reson Imaging 2006; 24: 563–570.
Google Scholar | Crossref | Medline3. Zhu, Y, Yang, D, Zou, L, et al. T2STIR preparation for single-shot cardiovascular magnetic resonance myocardial edema imaging. J Cardiovasc Magn Reson 2019; 21: 72.
Google Scholar | Crossref | Medline4. Cocker, MS, Shea, SM, Strohm, O, et al. A new approach towards improved visualization of myocardial edema using T2-weighted imaging: a cardiovascular magnetic resonance (CMR) study. J Magn Reson Imaging 2011; 34: 286–292.
Google Scholar | Crossref | Medline5. Yasaka, K, Akai, H, Kunimatsu, A, et al. Deep learning with convolutional neural network in radiology. Jpn J Radiol 2018; 36: 257–272.
Google Scholar | Crossref | Medline6. Leiner, T, Rueckert, D, Suinesiaputra, A, et al. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson 2019; 21: 61.
Google Scholar | Crossref | Medline7. Jiang, D, Dou, W, Vosters, L, et al. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Jpn J Radiol 2018; 36: 566–574.
Google Scholar | Crossref | Medline8. Zhang, K, Zuo, W, Chen, Y, et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 2017; 26: 3142–3155.
Google Scholar | Crossref | Medline9. Kingma, DP and, Ba, J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Google Scholar10. Constantinides, CD, Atalar, E, McVeigh, ER. Signal-to-noise measurements in magnitude images from NMR phased arrays. Magn Reson Med 1997; 38: 852–857.
Google Scholar | Crossref | Medline | ISI11. Downs, RK, Bashir, MH, Ng, CK, et al. Quantitative contrast ratio comparison between T1 (TSE at 1.5T, FLAIR at 3T), magnetization prepared rapid gradient echo and subtraction imaging at 1.5T and 3T. Quantitative Imaging Med Surg 2013; 3: 141–146.
Google Scholar | Medline12. Kidoh, M, Shinoda, K, Kitajima, M, et al. Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers. Magn Reson Med Sci 2020; 19(3): 195–206.
Google Scholar | Crossref | Medline13. Payne, AR, Casey, M, McClure, J, et al. Bright-blood T2-weighted MRI has higher diagnostic accuracy than dark-blood short tau inversion recovery MRI for detection of acute myocardial infarction and for assessment of the ischemic area at risk and myocardial salvage. Circ Cardiovasc Imaging 2011; 4: 210–219.
Google Scholar | Crossref | Medline14. Hammernik, K, Klatzer, T, Kobler, E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018; 79: 3055–3071.
Google Scholar | Crossref | Medline15. Ramzi, Z, Ciuciu, P and, Starck, J-L. Benchmarking MRI reconstruction neural networks on large public datasets. Appl Sci 2020; 10: 1816.
Google Scholar | Crossref16. Sriram, A, Zbontar, J, Murrell, T, et al. Grappanet: combining parallel imaging with deep learning for multi-coil MRI reconstruction. In: IEEE/CVF Conference on computer vision and pattern recognition (CVPR), Seattle, WA, 13-19 June 2020, pp. 14303–14310, 2020. IEEE.
Google Scholar