Literature DB >> 19203883

Optimum receiver array design for magnetic induction tomography.

Doga Gürsoy1, Hermann Scharfetter.   

Abstract

Magnetic induction tomography (MIT) is an imaging modality that aims at mapping the distribution of the electrical conductivity inside the body. Eddy currents are induced in the body by magnetic induction and the resulting fields are measured by an array of receiver coils. In MIT, the location of the receivers affects the quality of the image reconstruction. In this paper, a fast deterministic algorithm was applied to obtain optimum receiver array designs for a given specific excitation. The design strategy is based on the iterative exclusion of receiver locations, which yield poor conductivity information, from the space spanning all possible locations until a feasible design is reached. The applicability of "regionally focused" MIT designs that increase the image resolution at a particular region was demonstrated. Currently used design geometries and the corresponding reconstructed images were compared to the images obtained by optimized designs. The eigenvalue analysis of the Hessian matrix showed that the algorithm tends to maintain identical conductivity information content sensed by the receivers. Although the method does not guarantee finding the optimum design globally, the results demonstrate the practical usability of this algorithm in MIT experimental designs.

Mesh:

Year:  2009        PMID: 19203883     DOI: 10.1109/TBME.2009.2013936

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  2 in total

Review 1.  Advancements in transmitters and sensors for biological tissue imaging in magnetic induction tomography.

Authors:  Zulkarnay Zakaria; Ruzairi Abdul Rahim; Muhammad Saiful Badri Mansor; Sazali Yaacob; Nor Muzakkir Nor Ayub; Siti Zarina Mohd Muji; Mohd Hafiz Fazalul Rahiman; Syed Mustafa Kamal Syed Aman
Journal:  Sensors (Basel)       Date:  2012-05-29       Impact factor: 3.576

Review 2.  Screening and Biosensor-Based Approaches for Lung Cancer Detection.

Authors:  Lulu Wang
Journal:  Sensors (Basel)       Date:  2017-10-23       Impact factor: 3.576

  2 in total

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