Abstract
Magnetic induction tomography (MIT) is a tomographic imaging technique with a wide range of potential industrial applications. Planar array MIT is a convenient setup but unable to access freely from the entire periphery as it only collects measurements from one surface, so it remains challenging given the limited data. This study aims to assess the use of sparse regularization methods for accurate position and depth detection in planar array MIT.
Design/methodology/approach:
The most difficult challenges in MIT are to solve the inverse and forward problems. The inversion of planar MIT is severely ill-posed due to limited access data. Thus, this paper posed a total variation (TV) problem and solved it efficiently with the Split Bregman formulation to overcome this difficulty. Both isotropic and anisotropic TV formulations are compared to Tikhonov regularization with experimental MIT data.
Findings:
The results show that Tikhonov method failed or underestimated the object position and depth. Both isotropic and anisotropic TV led to accurate recovery of depth and position.
Originality/value:
There are numerous potential applications for planar array MIT where access to the materials under testing is restrict. Sparse regularization methods are a promising approach to improving depth detection for limited MIT data.
| Original language | English |
|---|---|
| Journal | Sensor Review |
| Volume | 39 |
| Issue number | 2 |
| Early online date | 2018 |
| DOIs | |
| Publication status | Published - 2 Feb 2019 |
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Keywords
- Anisotropic TV
- Isotropic TV
- Magnetic induction tomography
- Planar array MIT system
- Total variation
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering
Cite this
Planar array magnetic induction tomography further improvement. / Li, Fang; Soleimani, Manuchehr; Abascal, J.
In: Sensor Review, Vol. 39, No. 2, 02.02.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Planar array magnetic induction tomography further improvement
AU - Li, Fang
AU - Soleimani, Manuchehr
AU - Abascal, J
PY - 2019/2/2
Y1 - 2019/2/2
N2 - Purpose:Magnetic induction tomography (MIT) is a tomographic imaging technique with a wide range of potential industrial applications. Planar array MIT is a convenient setup but unable to access freely from the entire periphery as it only collects measurements from one surface, so it remains challenging given the limited data. This study aims to assess the use of sparse regularization methods for accurate position and depth detection in planar array MIT.Design/methodology/approach:The most difficult challenges in MIT are to solve the inverse and forward problems. The inversion of planar MIT is severely ill-posed due to limited access data. Thus, this paper posed a total variation (TV) problem and solved it efficiently with the Split Bregman formulation to overcome this difficulty. Both isotropic and anisotropic TV formulations are compared to Tikhonov regularization with experimental MIT data.Findings:The results show that Tikhonov method failed or underestimated the object position and depth. Both isotropic and anisotropic TV led to accurate recovery of depth and position.Originality/value:There are numerous potential applications for planar array MIT where access to the materials under testing is restrict. Sparse regularization methods are a promising approach to improving depth detection for limited MIT data.
AB - Purpose:Magnetic induction tomography (MIT) is a tomographic imaging technique with a wide range of potential industrial applications. Planar array MIT is a convenient setup but unable to access freely from the entire periphery as it only collects measurements from one surface, so it remains challenging given the limited data. This study aims to assess the use of sparse regularization methods for accurate position and depth detection in planar array MIT.Design/methodology/approach:The most difficult challenges in MIT are to solve the inverse and forward problems. The inversion of planar MIT is severely ill-posed due to limited access data. Thus, this paper posed a total variation (TV) problem and solved it efficiently with the Split Bregman formulation to overcome this difficulty. Both isotropic and anisotropic TV formulations are compared to Tikhonov regularization with experimental MIT data.Findings:The results show that Tikhonov method failed or underestimated the object position and depth. Both isotropic and anisotropic TV led to accurate recovery of depth and position.Originality/value:There are numerous potential applications for planar array MIT where access to the materials under testing is restrict. Sparse regularization methods are a promising approach to improving depth detection for limited MIT data.
KW - Anisotropic TV
KW - Isotropic TV
KW - Magnetic induction tomography
KW - Planar array MIT system
KW - Total variation
UR - http://www.scopus.com/inward/record.url?scp=85054678932&partnerID=8YFLogxK
U2 - 10.1108/SR-02-2018-0027
DO - 10.1108/SR-02-2018-0027
M3 - Article
VL - 39
JO - Sensor Review
JF - Sensor Review
SN - 0260-2288
IS - 2
ER -