Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer

“Compressed-sensing mri with random encoding,”

Justin P Haldar, Diego Hernando, and Zhi-Pei Liang, · 2010

Earlier work this paper cites.

“Mr image reconstruction from highly undersampled k-space data by dictionary learning,”

Saiprasad Ravishankar and Yoram Bresler, · 2010

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“U-net: Convolutional networks for biomedical image segmentation,”

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“A deep cascade of convolutional neural networks for dynamic mr image reconstruction,”

Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony N Price, and Daniel Rueckert, · 2017

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“Attention is all you need,”

Original

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017

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“Kiki-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images,”

Taejoon Eo, Yohan Jun, Taeseong Kim, Jinseong Jang, Ho-Joon Lee, and Dosik Hwang, · 2018

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Puyang Wang, Eric Z Chen, Terrence Chen, Vishal M Patel, and Shanhui Sun, · 2019

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“Prior-guided image reconstruction for accelerated multi-contrast mri via generative adversarial networks,”

Salman UH Dar, Mahmut Yurt, Mohammad Shahdloo, Muhammed Emrullah Ildız, Berk Tınaz, and Tolga Çukur, · 2020

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“Lesion mask-based simultaneous synthesis of anatomic and molecular mr images using a gan,”

Pengfei Guo, Puyang Wang, Jinyuan Zhou, Vishal M Patel, and Shanshan Jiang, · 2020

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brain development.org, ,”

Cited in the paper.

“Improving amide proton transfer-weighted mri reconstruction using t2-weighted images,”

Puyang Wang, Pengfei Guo, Jianhua Lu, Jinyuan Zhou, Shanshan Jiang, and Vishal M Patel, · 2020

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“Learning texture transformer network for image super-resolution,”

Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo, · 2020

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“Over-and-under complete convolutional rnn for mri reconstruction,”

Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel, · 2021

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“Anatomic and molecular mr image synthesis using confidence guided cnns,”

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