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KMID : 0858520140180040303
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Journal of the Korean Society of Magnetic Resonance in Medicine
2014 Volume.18 No. 4 p.303 ~ p.313
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Quantitative Conductivity Estimation Error due to Statistical Noise in Complex B1+ Map
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Shin Jae-Wook
Lee Joon-Sung
Kim Min-Oh
Choi Na-Rae
Seo Jin-Keun
Kim Dong-Hyun
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Abstract
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Purpose: In-vivo conductivity reconstruction using transmit field (B1+) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex B1 + map and provided a parametric model of the conductivity-to-noise ratio value.
Materials and Methods: The B1+ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated B1+ 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 B1+ 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 B1+ phase than to noise in B1+ 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 B1+ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of B1+ 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 B1+ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.
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KEYWORD
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MREPT, Conductivity mapping, Noise analysis
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