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http://dx.doi.org/10.13104/jksmrm.2014.18.4.303

Quantitative Conductivity Estimation Error due to Statistical Noise in Complex $B_1{^+}$ Map  

Shin, Jaewook (Department of Electrical and Electronic Engineering, Yonsei University)
Lee, Joonsung (SIRIC, Yonsei University)
Kim, Min-Oh (Department of Electrical and Electronic Engineering, Yonsei University)
Choi, Narae (Department of Electrical and Electronic Engineering, Yonsei University)
Seo, Jin Keun (Department of Computer Science & Engineering, Yonsei University)
Kim, Dong-Hyun (Department of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Investigative Magnetic Resonance Imaging / v.18, no.4, 2014 , pp. 303-313 More about this Journal
Abstract
Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.
Keywords
MREPT; Conductivity mapping; Noise analysis;
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