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http://echo.ismrm.org/p/ISMRM2021/2446
2446
ISMRM Abstract
SRDTI: Deep learning-based super-resolution for diffusion tensor MRI
Accepted
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1. Basser, P. J., Mattiello, J. & LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophysical journal 66, 259-267 (1994).
2. Pierpaoli, C., Jezzard, P., Basser, P. J., Barnett, A. & Di Chiro, G. Diffusion tensor MR imaging of the human brain. Radiology 201, 637-648 (1996).
3. Sotiropoulos, S. N. et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. NeuroImage 80, 125-143 (2013).
4. McNab, J. A. et al. Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex. NeuroImage 69, 87-100 (2013).
5. Wu, W., Koopmans, P. J., Andersson, J. L. & Miller, K. L. Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER). Magnetic resonance in medicine 82, 107-125 (2019).
6. Setsompop, K. et al. High‐resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (g S lider‐SMS). Magnetic resonance in medicine 79, 141-151 (2018).
7. Liao, C. et al. High-fidelity, high-isotropic-resolution diffusion imaging through gSlider acquisition with and T1 corrections and integrated ΔB0/Rx shim array. Magnetic Resonance in Medicine 83, 56-67 (2020).
8. Bilgic, B. et al. Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction. Magnetic Resonance in Medicine 82, 1343-1358, doi:10.1002/mrm.27813 (2019). [doi]
9. Hu, Y. et al. Motion‐robust reconstruction of multishot diffusion‐weighted images without phase estimation through locally low‐rank regularization. Magnetic resonance in medicine 81, 1181-1190 (2019).
10. Hu, Y. et al. Multi-shot diffusion-weighted MRI reconstruction with magnitude-based spatial-angular locally low-rank regularization (SPA-LLR). Magnetic Resonance in Medicine 83, 1596-1607 (2020).
11. Alexander, D. C. et al. Image quality transfer and applications in diffusion MRI. NeuroImage 152, 283-298 (2017).
12. Elsaid, N. M. & Wu, Y.-C. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2830-2834 (IEEE).
13. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-124 (2013).
14. Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing 26, 3142-3155 (2017).
15. Kim, J., Kwon Lee, J. & Mu Lee, K. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1646-1654 (2016).
16. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at: https://arxiv.org/abs/1409.1556 (2014).
17. Skare, S., Hedehus, M., Moseley, M. E. & Li, T.-Q. Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. Journal of Magnetic Resonance 147, 340-352 (2000).
18. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000-1016 (2012).