Abstract
Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is a non-invasive technique for quantifying cerebral blood flow (CBF). Limited by the T1 decay rate of the labeled spins, very short time is available for data acquisition after one spin labeling cycle, resulting in a low spatial resolution. The traditional strategy to achieve high spatial resolution in ASL MRI is to add more labeling cycles. However, the total acquisition time is exponentially prolonged, making it highly sensitive to motions. Moreover, signal-to-noise-ratio (SNR) drops as spatial resolution increases. There needs an alternative approach to improve spatial resolution and SNR for ASL MRI without increasing scan time. Therefore, we propose a novel two-stage multi-loss super-resolution (SR) network (TSMLSRNet) for reconstruction of high resolution ASL images. Specifically, the first stage network uses the mean squared error (MSE) loss function to produce a first SR estimate, while the second stage network adopts the gradient sensitive (GS) loss function to further improve high-frequency details for the output SR image. The multi-loss joint training strategy is finally used to preserve both the low-frequency and high-frequency information of the ASL images. Moreover, the noise in ASL images is simultaneously reduced. Validation results using in-vivo data clearly show the effectiveness of the proposed ASL SR algorithm that outperforms state-of-the-art image reconstruction algorithms.
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References
Detre, J.A., Leigh, J.S., Williams, D.S., et al.: Perfusion imaging. Magn. Reson. Med. 23(1), 37–45 (1992)
Alsop, D.C., Detre, J.A., Golay, X., et al.: Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn. Reson. Med. 73(1), 102–116 (2015)
Du, A.T., Jahng, G.H., Hayasaka, S., et al.: Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology 67(7), 1215–1220 (2006)
Asllani, I., Borogovac, A., Brown, T.R.: Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med. 60(6), 1362–1371 (2008)
Meurée, C., Maurel, P., Ferré, J.C., et al.: Patch-based super-resolution of arterial spin labeling magnetic resonance images. Neuroimage 189, 85–94 (2019)
Oktay, O., Bai, W., Lee, M., et al.: Multi-input cardiac image super-resolution using convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, vol. 9902, pp. 246–254. Springer, Cham (2016)
Shi, J., Liu, Q., Wang, C., et al.: Super-resolution reconstruction of MR image with a novel residual learning network algorithm. Phys. Med. Biol. 63(8), 085011 (2018)
Shi, J., Li, Z., Ying, S., et al.: MR image super-resolution via wide residual networks with fixed skip connection. IEEE J. Biomed. Health Inform. 23(3), 1129–1140 (2018)
Tong, T., Li, G., Liu, X., et al.: Image super-resolution using dense skip connections. In: ICCV, pp. 4799–4807 (2017)
Zhang, Y., Tian, Y., Kong, Y., et al.: Residual dense network for image super-resolution. In: CVPR, pp. 2472–2481 (2018)
Huang, G., Liu, Z., Van Der Maaten, L., et al.: Densely connected convolutional networks. In: CVPR, pp. 4700–4708 (2017)
Guo, Y., Chen, Q., Chen, J., et al. Dual reconstruction nets for image super-resolution with gradient sensitive loss. arXiv preprint arXiv:1809.07099 (2018)
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: CVPR, pp. 1646–1654 (2016)
Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: CVPR, pp. 3147–3155 (2017)
Zhang, Y., Li, K., Li, K., et al.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 286–301. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18
Zhang, K., Zuo, W., Chen, Y., et al.: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)
Chen, H., Zhang, Y., Kalra, M.K., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524–2535 (2017)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (81830058), the Shanghai Science and Technology Foundation (18010500600) and the NIH/NIA (R01AG060054).
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Li, Z. et al. (2019). A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_2
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DOI: https://doi.org/10.1007/978-3-030-32248-9_2
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