Inspired by the recently proposed Magnetic Resonance Fin- gerprinting technique, we develop a principled compressed sensing framework for quantitative MRI. The three key com- ponents are: a random pulse excitation sequence following the MRF technique; a random EPI subsampling strategy and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, as long as the ex- citation sequence possesses an appropriate form of persistent excitation, we are able to achieve accurate recovery of the proton density, T1, T2 and off-resonance maps simultane- ously from a limited number of samples.
Rakesh Chawla, Arvind Shah, Andrea Rizzi, Matthias Finger, Federica Legger, Matteo Galli, Sun Hee Kim, Jian Zhao, João Miguel das Neves Duarte, Tagir Aushev, Hua Zhang, Alexis Kalogeropoulos, Yixing Chen, Tian Cheng, Ioannis Papadopoulos, Gabriele Grosso, Valérie Scheurer, Meng Xiao, Maren Tabea Meinhard, Qian Wang, Michele Bianco, Varun Sharma, Jessica Prisciandaro, Joao Varela, Sourav Sen, Ashish Sharma, Seungkyu Ha, David Vannerom, Csaba Hajdu, Sanjeev Kumar, Sebastiana Gianì, Kun Shi, Abhisek Datta, Miao Hu, Siyuan Wang, Muhammad Waqas, Anton Petrov, Jian Wang, Yi Zhang, Muhammad Ansar Iqbal, Yong Yang, Xin Sun, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
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