Deep Learning for Automated Segmentation and Quantitative Mapping with UTE MRI

Author:

Lu Xing,Jang Hyungseok,Ma Yajun,Du Jiang

Publisher

Springer International Publishing

Reference53 articles.

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2. Yang Y, Sun J, Li H, Xu Z. Deep ADMM-Net for compressive sensing MRI. In: Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, editors. Advances in neural information processing systems, vol. 29. NeuralIPS; 2016. p. 10–8.

3. Wang S, Su Z, Ying L, Peng X, Zhu S, Liang F, et al. Accelerating magnetic resonance imaging via deep learning. In: Proc IEEE Int Symp Biomed Imaging. IEEE; 2016. p. 514–7.

4. Qin C, Hajnal JV, Rueckert D, Schlemper J, Caballero J, Price AN. Convolutional recurrent neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging. 2019;38(1):280–90.

5. Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555(7697):487–92.

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