title: Measurement of induced magnetic flux density using injection current nonlinear encoding (ICNE) in MREIT.
abstract: Magnetic resonance electrical impedance tomography (MREIT) measures induced magnetic flux densities subject to externally injected currents in order to visualize conductivity distributions inside an electrically conducting object. Injection currents induce magnetic flux densities that appear in phase parts of acquired MR image data. In the conventional current injection method, we inject currents during the time segment between the end of the first RF pulse and the beginning of the reading gradient in order to ensure the gradient linearity. Noting that longer current injections can accumulate more phase changes, we propose a new pulse sequence called injection current nonlinear encoding (ICNE) where the duration of the injection current pulse is extended until the end of the reading gradient. Since the current injection during the reading gradient disturbs the gradient linearity, we first analyze the MR signal produced by the ICNE pulse sequence and suggest a novel algorithm to extract the induced magnetic flux density from the acquired MR signal. Numerical simulations and phantom experiments show that the new method is clearly advantageous in terms of the reduced noise level in measured magnetic flux density data. The amount of noise reduction depends on the choice of the data acquisition time and it was about 24% when we used a prolonged data acquisition time of 10.8 ms. The ICNE method will enhance the clinical applicability of the MREIT technique when it is combined with an appropriate phase artefact minimization method.
authors.affiliation: College of Electronics and Information, Kyung Hee University, Korea.
authors.forename: Chunjae
authors.initials: C
authors.lastname: Park
authors.forename: Byung Il
authors.initials: BI
authors.lastname: Lee
authors.forename: Ohin
authors.initials: O
authors.lastname: Kwon
authors.forename: Eung Je
authors.initials: EJ
authors.lastname: Woo
mesh_terms: D000465:Algorithms; D000818:Animals; D002645:Chickens; D003198:Computer Simulation; D004566:Electrodes; D020342:Finite Element Analysis; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008953:Models, Anatomic; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000033:anatomy & histology; D017711:Nonlinear Dynamics; D013552:Swine