An Improved Conjugate Gradient Image Reconstruction Algorithm for Electromagnetic Tomography

Liu, Xianglong; Wang, Ying

doi:10.1007/s11220-021-00374-y

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…Single coil excitation strategy is applied. Sensitivity matrix is the prior information for solving EMT inverse problem, which is calculated by the perturbation method [24,25]. The imaging region is split into 318 elements as shown in Fig.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Liu

Wang

et al. 2023

Sens Imaging

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Electromagnetic tomography (EMT) is a versatile tomographic imaging technique for reconstruction of conductivity and/or permeability distribution due to the advantages of non-contact, non-intrusive, lowcost, simple structure and fast imaging. However, the ill-posed and ill-conditioned features of EMT make it difficult to obtain high quality reconstructed images. To improve the spatial resolution of the high conductivity medium imaging, the L1-L1 framework objective function is presented, which uses L1 norm as both data fidelity term and regularization term to weaken the influence of the data outliers and impose the sparsity feature of the measured objects. An improved Split Bregman method is proposed to solve the complicated optimization problem efficiently, which splits it into several simple sub-tasks.Each subtask can be solved by adopting the proper method. Besides, an acceleration strategy is introduced to improve the convergence rate. Numerical simulations are used to verify the effectiveness and competitive performance of the proposed improved method. The experiments are carried out by the designed modularized EMT system to further verify the effectiveness of the proposed method. The reconstructed images can precisely show the number and positions of the measured objects.

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Section: Numerical Simulation Resultsmentioning

confidence: 99%

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Liu

Wang

et al. 2023

Sens Imaging

Self Cite

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Defect detection of metallic samples by electromagnetic tomography using closed-loop fuzzy PID-controlled iterative Landweber method

Huang,

et al. 2024

Nondestructive Testing and Evaluation

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Space-constrained optimized Tikhonov regularization method for 3D hemorrhage reconstruction by open magnetic induction tomography

Chen

Dong

2022

Phys. Med. Biol.

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Objective: Open magnetic induction tomography (MIT) is a promising technique for detecting the intracranial hemorrhage due to the non-radioactive, non-invasive and portable features. However, severe inhomogeneity of the sensitivity distribution under the open MIT sensor array and the ill-conditioned nature of MIT inverse problem limit the imaging quality in hemorrhage reconstruction. More accurate and robust imaging algorithms are urgently needed in clinical diagnosis. Approach: In this study, the space-constrained optimized Tikhonov regularization (SOTR) method is proposed for 3D hemorrhage reconstruction by open MIT. The sensitivity matrix is optimized according to the characteristics of sensitivity distribution under the open MIT sensor array. To test the performance of the SOTR method, 3D anatomical head models with hemorrhages in different volumes and locations were established. The images of the hemorrhages were reconstructed by the Tikhonov regularization (TR), total variation (TV) regularization, isotropic SOTR, and anisotropic SOTR method. Correlation coefficient CC, localization error LE, and volume error VE were calculated to evaluate the hemorrhage imaging quality. Main results: Compared with the traditional sensitivity matrix, the optimized sensitivity matrix has smaller column number and better uniformity, which alleviates the under-determined and ill-conditioned problem of MIT. The imaging results indicate that both the isotropic and anisotropic SOTR methods can effectively improve the reconstruction accuracy for the location and volume of the hemorrhages. Moreover, compared with the TR and TV methods, the two SOTR methods are more robust against the measurement noise. Significance: The proposed method improves the imaging quality of the intracranial hemorrhage, which promotes the clinical applications of open MIT.

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An Improved Conjugate Gradient Image Reconstruction Algorithm for Electromagnetic Tomography

Cited by 8 publications

References 28 publications

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Defect detection of metallic samples by electromagnetic tomography using closed-loop fuzzy PID-controlled iterative Landweber method

Space-constrained optimized Tikhonov regularization method for 3D hemorrhage reconstruction by open magnetic induction tomography

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