Imaging of hemorrhagic stroke in magnetic induction tomography: An in vitro study

Abstract

Magnetic Induction Tomography (MIT) has the potential of providing an inexpensive medical device for regular screening and monitoring of patients. MIT could be used to detect hemorrhagic stroke, if high measurement accuracy and spatial resolution can be reached to allow significant contrast between normal brain tissues and hemorrhaged areas. However, this can be challenging because spatial resolution in MIT is limited due to the small number of independent measurements, the inverse problems are severely ill-posed, and erroneous data cause large artefacts in reconstructed images and lower the detectability threshold of bleeding. The noise components degrading signal and image quality may be caused by thermal drift and noise from acquisition systems, environment or by body movements. The objective of the article is to empirically investigate hemorrhagic stroke in MIT based on in vitro study and to improve stroke detectability and visibility to help monitoring stroke patients. The following approaches were evaluated: (i) level setting, (ii) improved spatial filtering, (iii) averaging of multiple measurements, (iv) the combinations of these three approaches, and (v) wavelet denoising. They were evaluated with an in vitro phantom resembling a cerebral stroke in a pig brain. The results showed that these approaches enhanced stroke visibility, lowered stroke detectability threshold from 15 ml to 5 ml, and improved the localization of phantom hemorrhages such that their combination produced the best results. These methods may make it easy to the estimation of actual stroke volume, and clinical interpretation, and it can be used to longterm monitoring of stroke progression.

Deep learning algorithms for brain disease detection with magnetic induction tomography

“…The phase differences between normal and diseased tissues are calculated for reconstruction. Some of the earlier studies 24 using MIT technology to image cerebral hemorrhage were based on the spherical model which is similar to the head's geometry. However, due to the great difference between the spherical model and the real head structure, the inversion error will inevitably be brought when solving the inverse problem, which makes the reconstruction result error larger.…”

Application of expectation maximization algorithm in magnetic induction tomography

“…(4). The corresponding variation problem is described by (6) The research area of tissue can be separated into triangular elements, and the vector magnetic potential A e in each element e can be approximated using the triangular linear interpolation…”

“…INR algorithm which solves the nonlinear least square problem based on Newton method, can find out the optimal distribution of conductivity. Although the three methods above have been widely used for MIT, they cannot obtain the high enough quality of image for practical medical applications yet [6]. This fact is caused by two reasons: first, the number of the measurement voltages is much less than the pixels, which causes the ill-posed problem.…”

Image reconstruction via statistical classification for magnetic induction tomography

“…Image reconstruction is often approximate to linear problem [18]. Expand (10) using a truncated Taylor expansion [19] as the following…”

“…Iterative NR algorithm, which is based on Newton method to solve the nonlinear least square problem, can find out the optimal distribution of conductivity. Although the three methods above have been widely used for MIT, they cannot obtain the high enough resolution for practical medical applications yet [6]. This fact is caused by two reasons: first, the number of the measurements is much less than that of the pixels, which causes the ill-posed problem.…”