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Fast chemical exchange saturation transfer imaging based on PROPELLER acquisition and deep neural network reconstruction
Author(s) -
Guo Chenlu,
Wu Jian,
Rosenberg Jens T.,
Roussel Tangi,
Cai Shuhui,
Cai Congbo
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28376
Subject(s) - propeller , imaging phantom , computer science , artificial neural network , artificial intelligence , data acquisition , sampling (signal processing) , acceleration , contrast (vision) , iterative reconstruction , computer vision , physics , optics , engineering , filter (signal processing) , classical mechanics , operating system , marine engineering
Purpose To develop a method for fast chemical exchange saturation transfer (CEST) imaging. Methods The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method. Results The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST‐PROPELLER reconstructed images under an acceleration factor of 8. Conclusion Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.