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MR electrical properties imaging using a generalized image‐based method
Author(s) -
Soullié Paul,
Missoffe Alexia,
Ambarki Khalid,
Felblinger Jacques,
Odille Freddy
Publication year - 2021
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.28458
Subject(s) - imaging phantom , computer science , image (mathematics) , noise (video) , relevance (law) , algorithm , partial differential equation , experimental data , field (mathematics) , permittivity , mathematics , mathematical analysis , artificial intelligence , physics , optics , dielectric , statistics , optoelectronics , law , political science , pure mathematics
Purpose To develop a fast and easy‐to‐use electrical properties tomography (EPT) method based on a single MR scan, avoiding both the need of a B 1 ‐map and transceive phase assumption, and that is robust against noise. Theory Derived from Maxwell’s equations, conductivity, and permittivity are reconstructed from a new partial differential equation involving the product of the RF fields and its derivatives. This also allows us to clarify and revisit the relevance of common assumptions of MREPT. Methods Our new governing equation is solved using a 3D finite‐difference scheme and compared to previous frameworks. The benefits of our method over selected existing MREPT methods are demonstrated for different simulation models, as well as for both an inhomogeneous agar phantom gel and in vivo brain data at 3T. Results Simulation and experimental results are illustrated to highlight the merits of the proposed method over existing methods. We show the validity of our algorithm in versatile configurations, with many transition regions notably. Complex admittivity maps are also provided as a complementary MR contrast. Conclusion Because it avoids time‐consuming RF field mapping and generalizes the use of standard MR image for electrical properties reconstruction, this contribution is promising as a new step forward for clinical applications.