scholarly journals THREE-DIMENSIONAL MAGNETIC INDUCTION TOMOGRAPHY IMAGING USING A MATRIX FREE KRYLOV SUBSPACE INVERSION ALGORITHM

2012 ◽  
Vol 122 ◽  
pp. 29-45 ◽  
Author(s):  
Hsin-Yu Wei ◽  
Manuchehr Soleimani
Keyword(s):  
Magnetic Induction ◽  
Krylov Subspace ◽  
Three Dimensional ◽  
Inversion Algorithm ◽  
Tomography Imaging ◽  
Magnetic Induction Tomography ◽  
Matrix Free

scholarly journals Three-dimensional Magnetic Induction Tomography: Improved Performance for the Center Regions inside a Low Conductive and Voluminous Body

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1306
Author(s):  
Martin Klein ◽  
Daniel Erni ◽  
Dirk Rueter
Keyword(s):  
Magnetic Induction ◽  
Three Dimensional ◽  
Central Area ◽  
Electromagnetic Properties ◽  
Interior Region ◽  
Noise Floor ◽  
Sensitivity Map ◽  
Improved Performance ◽  
Magnetic Induction Tomography ◽  
Conductive Volume

Magnetic induction tomography (MIT) is a contactless technique that is used to image the distribution of passive electromagnetic properties inside a voluminous body. However, the central area sensitivity (CAS) of this method is critically weak and blurred for a low conductive volume. This article analyzes this challenging issue, which inhibits even faint imaging of the central interior region of a body, and it suggests a remedy. The problem is expounded via two-dimensional (2D) and three-dimensional (3D) eddy current simulations with different transmitter geometries. On this basis, it is shown that a spatially undulating exciter coil can significantly improve the CAS by >20 dB. Consequently, the central region inside a low conductive voluminous object becomes clearly detectable above the noise floor, a fact which is also confirmed by practical measurements. The improved sensitivity map of the new arrangement is compared with maps of more typical circular MIT geometries. In conclusion, 3D MIT reconstructions are presented, and for the same incidence of noise, their performance is much better with the suggested improvement than that with a circular setup.

scholarly journals Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7725
Author(s):  
Martin Klein ◽  
Daniel Erni ◽  
Dirk Rueter
Keyword(s):  
Magnetic Induction ◽  
3D Imaging ◽  
Three Dimensional ◽  
Practical Implementation ◽  
Excitation Field ◽  
Technical Performance ◽  
Central Regions ◽  
Entire Depth ◽  
Magnetic Induction Tomography ◽  
Practical Demonstration

Magnetic induction tomography (MIT) is a contactless, low-energy method used to visualize the conductivity distribution inside a body under examination. A particularly demanding task is the three-dimensional (3D) imaging of voluminous bodies in the biomedical impedance regime. While successful MIT simulations have been reported for this regime, practical demonstration over the entire depth of weakly conductive bodies is technically difficult and has not yet been reported, particularly in terms of more realistic requirements. Poor sensitivity in the central regions critically affects the measurements. However, a recently simulated MIT scanner with a sinusoidal excitation field topology promises improved sensitivity (>20 dB) from the interior. On this basis, a large and fast 3D MIT scanner was practically realized in this study. Close agreement between theoretical forward calculations and experimental measurements underline the technical performance of the sensor system, and the previously only simulated progress is hereby confirmed. This allows 3D reconstructions from practical measurements to be presented over the entire depth of a voluminous body phantom with tissue-like conductivity and dimensions similar to a human torso. This feasibility demonstration takes MIT a step further toward the quick 3D mapping of a low conductive and voluminous object, for example, for rapid, harmless and contactless thorax or lung diagnostics.

Initial Development On Magnetic Induction Tomography Imaging

2012 ◽  
Author(s):  
Nor Muzakkir Nor Ayob ◽  
Zulkarnay Zakaria ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Ruzairi Abdul Rahim ◽  
Sazali Yaacob
Keyword(s):  
Magnetic Induction ◽  
Acceptable Result ◽  
Initial Development ◽  
Tomographic Images ◽  
Tomography Imaging ◽  
Tomographic Method ◽  
Imaging Research ◽  
Sensitivity Map ◽  
Imaging Instrument ◽  
Magnetic Induction Tomography

Sistem pengimejan yang tidak intrusif telah banyak menarik perhatian di dalam banyak aplikasi seperti pemprosesan, industri dan perubatan. Teknik elektrikal khususnya, terbukti menjadi instrumen pengimejan berkos rendah dengan kemampuan resolusi rendah tetapi memadai untuk pengimejan pemprosesan. Tomografi Induktansi Magnetik (TIM) adalah kaedah tomografi elektrikal berdasarkan penggunaan bacaan induktansi untuk memantau distribusi bahan konduksi elektrik dan magnetik dalam kawasan pemerhatian. Karya penyelidikan ini adalah berdasarkan pembangunan perisian untuk kegunaan pengimejan tomografi induktansi magnetik. Hasil penyelidikan tertumpu untuk menghasilkan peta sensitiviti yang diperlukan untuk rekonstruksi semula imej tomografi dari taburan elektromagnetik. Keputusan awal mengenai kajian simulatif telah menunjukkan hasil yang positif iaitu peta sensitiviti yang dihasilkan adalah bersesuaian untuk digunakan dalam pengimejan konduktiviti elektrik. Kata kunci: Tomografi induktansi magnetik; pengimejan tidak-intrusif; induktansi; peta sensitiviti Non–intrusive imaging systems have always been of much interest for use in many applications such as process, industrial and medical. Electrical techniques, in particular, are proving to be an inexpensive imaging instrument with low but sufficient resolution capability on imaging the internal distributions processes. Magnetic Induction Tomography (MIT) is an electrical tomographic method based on the use of inductance measurement for monitoring the distribution of electrically conductive and magnetically permeable material within the sensing area. This paper details the fundamental investigation of the software development for magnetic induction tomography imaging. Research works are concentrated on generating the sensitivity maps needed to reconstruct tomographic images of the electromagnetic distribution. Initial results on the simulative studies have shown acceptable result for using the generated sensitivity map for imaging electrical conductivities. Key words: Magnetic induction tomography; non-intrusive imaging; inductance; sensitivity map

Inspection of defects in CFRP by improved magnetic induction tomography

2019 ◽  
Vol 61 (3) ◽  
pp. 255-259
Author(s):  
Lipan Zhang ◽  
Qifeng Meng ◽  
Kai Song ◽  
Ming Gao ◽  
Zhiyuan Cheng
Keyword(s):  
Magnetic Induction ◽  
Magnetic Induction Tomography

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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