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Dictionary‐based electric properties tomography
Author(s) -
Hampe Nils,
Herrmann Max,
Amthor Thomas,
Findeklee Christian,
Doneva Mariya,
Katscher Ulrich
Publication year - 2019
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.27401
Subject(s) - helmholtz free energy , imaging phantom , tomography , matching (statistics) , electric field , computer science , noise (video) , pattern recognition (psychology) , artificial intelligence , physics , nuclear magnetic resonance , algorithm , materials science , computational physics , mathematics , optics , statistics , quantum mechanics , image (mathematics)
Purpose To develop and validate a new algorithm called “dictionary‐based electric properties tomography” (dbEPT) for deriving tissue electric properties from measured B 1 maps. Methods Inspired by Magnetic Resonance fingerprinting, dbEPT uses a dictionary of local patterns (“atoms”) of B 1 maps and corresponding electric properties distributions, derived from electromagnetic field simulations. For reconstruction, a pattern from a measured B 1 map is compared with the B 1 atoms of the dictionary. The B 1 atom showing the best match with the measured B 1 pattern yields the optimum electric properties pattern that is chosen for reconstruction. Matching was performed through machine learning algorithms. Two dictionaries, using transmit and transceive phases, were evaluated. The spatial distribution of local matching distance between optimal atom and measured pattern yielded a reconstruction reliability map. The method was applied to reconstruct conductivity of 4 volunteers’ brains. A conventional, Helmholtz‐based Electric properties tomography (EPT) reconstruction was performed for reference. Noise performance was studied through phantom simulations. Results Quantitative values of conductivity agree with literature values. Results of the 2 dictionaries exhibit only minor differences. Somewhat larger differences are visible between dbEPT and Helmholtz‐based EPT. Quantified by the correlation between conductivity and anatomic images, dbEPT depicts brain details more clearly than Helmholtz‐based EPT. Matching distance is minimal in homogeneous brain ventricles and increases with tissue heterogeneity. Central processing unit time was approximately 2 minutes per dictionary training and 3 minutes per brain conductivity reconstruction using standard hardware equipment. Conclusion A new, dictionary‐based approach for reconstructing electric properties is presented. Its conductivity reconstruction is able to overcome the EPT transceive‐phase problem.