TY - GEN
T1 - Complementary Phase Encoding for Pair-Wise Neural Deblurring of Accelerated Brain MRI
AU - Hod, Gali
AU - Green, Michael
AU - Waserman, Mark
AU - Konen, Eli
AU - Shrot, Shai
AU - Nelkenbaum, Ilya
AU - Kiryati, Nahum
AU - Mayer, Arnaldo
N1 - Funding Information:
Acknowledgements. Gali Hod would like to thank the Israeli Ministry of Science and Technology for supporting her under the scholarship program during the research period.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - MRI has become an invaluable tool for diagnostic brain imaging, providing unrivalled qualitative and quantitative information to the radiologist. However, due to long scanning times and capital costs, access to MRI lags behind CT. Typical brain protocols lasting over 30 min set a clear limitation to patient experience, scanner throughput, operation profitability, and lead to long waiting times for an appointment. As image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration, significant scanning acceleration must successfully address challenging image degradation. In this work, we consider the scan acceleration scenario of a strongly anisotropic acquisition matrix. We propose a neural approach that jointly deblurs scan pairs acquired with mutually orthogonal phase encoding directions. This leverages the complementarity of the respective phase encoded information as blur directions are also mutually orthogonal between the scans in the pair. The proposed architecture, trained end-to-end, is applied to T1w scan pairs consisting of one scan with contrast media injection (CMI), and one without. Qualitative and quantitative validation is provided against state-of-the-art deblurring methods, for an acceleration factor of 4 beyond compressed sensing acceleration. The proposed method outperforms the compared methods, suggesting its possible clinical applicability for this challenging task.
AB - MRI has become an invaluable tool for diagnostic brain imaging, providing unrivalled qualitative and quantitative information to the radiologist. However, due to long scanning times and capital costs, access to MRI lags behind CT. Typical brain protocols lasting over 30 min set a clear limitation to patient experience, scanner throughput, operation profitability, and lead to long waiting times for an appointment. As image quality, in terms of spatial resolution and noise, is strongly dependent on acquisition duration, significant scanning acceleration must successfully address challenging image degradation. In this work, we consider the scan acceleration scenario of a strongly anisotropic acquisition matrix. We propose a neural approach that jointly deblurs scan pairs acquired with mutually orthogonal phase encoding directions. This leverages the complementarity of the respective phase encoded information as blur directions are also mutually orthogonal between the scans in the pair. The proposed architecture, trained end-to-end, is applied to T1w scan pairs consisting of one scan with contrast media injection (CMI), and one without. Qualitative and quantitative validation is provided against state-of-the-art deblurring methods, for an acceleration factor of 4 beyond compressed sensing acceleration. The proposed method outperforms the compared methods, suggesting its possible clinical applicability for this challenging task.
KW - Deblurring
KW - Deep learning
KW - Medical imaging
KW - MRI acceleration
KW - Multi-modal mri
UR - http://www.scopus.com/inward/record.url?scp=85151166316&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25066-8_13
DO - 10.1007/978-3-031-25066-8_13
M3 - Conference contribution
AN - SCOPUS:85151166316
SN - 9783031250651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 268
EP - 280
BT - Computer Vision – ECCV 2022 Workshops, Proceedings
A2 - Karlinsky, Leonid
A2 - Michaeli, Tomer
A2 - Nishino, Ko
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th European Conference on Computer Vision, ECCV 2022
Y2 - 23 October 2022 through 27 October 2022
ER -