Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

Abstract

Objective. Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs. Approach. In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms. Main results. Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms. Significance. The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected to promote brain injury monitoring imaging via MIT.

Real-time imaging of traumatic brain injury using magnetic induction tomography

“…MIT was used for monitoring. This previous study showed that MIT can image and identify the location of intracranial injury after TBI (Zhang et al 2021). In the present study, the intracranial pressure increased following craniocerebral injury in rabbits, initiating compensatory mechanisms.…”

“…In theory, it will achieve better imaging results than those obtained using experimental animals. Although the human spinal uid layer will be thicker, previous simulation studies performed by our group show that better imaging can be performed even in the presence of cerebro-spinal uid (Zhang et al, 2021). The imaging in this study is based on twodimensional imaging, which mainly re ects the changes in electrical conductivity of the plane where the coil is located.…”

“…The MIT monitoring data system is an improved FMMU MIT system developed through cooperation between the FMMU and Hangzhou Utron Technology Co. Ltd. (Zhang et al, 2021). As shown in Fig.…”

“…The MIT monitoring data system is an improved FMMU MIT system developed through cooperation between the FMMU and Hangzhou Utron Technology Co., Ltd (Zhang et al 2021). As shown in figure 3(a), the rabbit was placed right below the center of the device, and the head area was 5.6 cm away from the bottom coil.…”

A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems

“…We have applied the split Bregman iterative algorithm to solve a similar problem but with the gradient penalty in [17] . Recently, split-Bregman algorithm has attracted a lot of attention in various image processing applications [20] , [26] , [29] .…”