| Brain magnetic induction tomography (BMIT) using a magnetic field as the excitationsource can effectively avoid the rays on the human head injury, in addition this technique canalso imaging functional metabolism of brain tissue and having a non-contact characteristics, tomeet the clinical requirements of continuous long time monitoring lesions. Improved imagereconstruction algorithm is the key to promoting brain magnetic induction tomography earlyclinical application. An back-projective iteration image reconstruction algorithm of BMIT ispresented in this paper.Firstly, the design idea of back-projective iteration image reconstruction algorithm isintroduced. Then the back-projection matrix is simply weighted based on the difference ofback-projection paths and the sensitivity to conductivity change of the pixel in the imagingregion. And by setting the number of D-ideal conditions to correct the Hessian matrix, therebyimproving the degree of morbid reconstruction process.Secondly, in order to elect a more accurate forward problem calculation model used forthe inverse problem, the results of two different mesh models are compared with thecommercial software. After standardized and position corrected, the detected data is used forreconstructed image. Moreover, through the image processing technology with CT images, theboundary information of the skull is obtained so that the reconstructed results has realisticboundary shape of skull. And the reconstructed image correctted by the brain tissue’s priorinformation of conductivity.The result shows that the back-projective iteration image reconstructed algorithm canaccurately reflect the distribution of conductivity and the object position in the imaging regionwith a high resolution. The anti-noise performance of the algorithm could meet the standard ofapplications. For brain magnetic induction tomography imaging technology applied to clinicalmonitoring laid a solid foundation. |
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