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

doi:10.21203/rs.3.rs-2074772/v1

Download PDF

Research Article

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

https://doi.org/10.21203/rs.3.rs-2074772/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Early diagnosis of traumatic brain injury (TBI) is crucial for its prognosis; however, traditional computed tomography (CT) diagnostic methods rely on large medical devices with an associated lag time to receive results. In the present study, an improved magnetic induction tomography (MIT) data acquisition system was used to detect TBI in an animal model, distinguish the injury level, and locate the injured area. A pneumatically controlled cortical impactor was used to hammer the parietal lobe of anesthetized rabbits twice and three times under the same parameter mode to construct two different TBI rabbit models with significantly different degrees of damage (p = 0.0020). The MIT data acquisition system was used to record data and continuous monitoring for one hour without intervention. A target with increased conductivity is clearly observed in the reconstructed image. The position is relatively fixed and accurate, and the average positioning error of the image is 0.01372 m. The normalized mean reconstruction value of all images increased with time. The slope of the regression line of the normalized mean reconstruction value differed significantly between the two models (p < 0.0001). This indicates that in the animal model, MIT's unique features may facilitate the early diagnosis and localization of TBI and distinguish different degrees of injuries, thereby reducing the risk and mortality of associated complications.

magnetic induction tomography

traumatic brain injury

vivo animal model

Traumatic brain injury (TBI) is a common, severe disease, and a leading cause of death and disability. According to recent reports (Jiang et al., 2019), over 50 million patients suffer from TBI worldwide annually, resulting in economic losses of approximately 400 billion U.S. dollars. Even with effective treatment, the mortality rate of TBI is 20–30%, and the disability rate can be 50–60% (Servadei et al., 2000). It also results in tremendous economic and psychological burdens to society and families (Maas et al., 2017). The primary imaging examination method for TBI is head computed tomography (CT). Examination methods such as the observation of vital signs and intracranial pressure measurements are also essential. Early diagnosis and evaluation of the injury, as well as continuous dynamic monitoring, will greatly contribute to reduced mortality and disability due to craniocerebral injury. CT, which requires large imaging equipment, is most commonly used to diagnose craniocerebral injury (Servadei et al., 2000). Further, CT is only available at medical centers and exposes patients to ionizing radiation. The transfer of patients from the trauma site to the medical center is a blank area for intracranial imaging, and CT cannot be used for continuous monitoring. Thus, an imaging modality that can provide real-time, non-invasive examination to assess the extent of injury and thereby guide treatment is urgently required.

In recent years, the rapid development of bioelectrical impedance has enabled its application in medicine. Several commercialized and clinically approved electrical impedance tomography (EIT) systems are available for intensive care unit monitoring, particularly for the brain and chest (ventilation-perfusion monitoring) (Al-Zeibak & Saunders, 1993; Jang et al., 2019). Traditional EIT has a broad prospect in electrophysiological measurement, diagnosis, and real-time monitoring of brain diseases, such as epilepsy (Witkowska-Wrobel et al., 2018), stroke (Dowrick et al., 2016), and brain edema (Yang et al., 2019). However, the poor conductivity of skull and electrode-skin contact impedance limit the development of EIT in brain disease monitoring. MIT is an emerging non-contact EIT technology that uses the principle of eddy current induction to detect the distribution or change in conductivity inside the human body because of the strong permeability of magnetic fields to the skull (Ma & Soleimani, 2018). Therefore, MIT has advantages in monitoring intracranial injury. Further, compared with current medical imaging technology, MIT is a new technology that has not been applied in a clinical setting to date, which would provide non-contact real-time dynamic detection (Mahdavi & Rosell-Ferrer, 2017; Xiang et al., 2020), ensuring safety and convenience in future medical applications. The unique advantages of MIT make it promising for early detection of TBI.

2.1 Establishment of controlled cortical impact (CCI) Model

Ten healthy adult rabbits (2.0–2.5 kg) were obtained from the Animal Experiment Center of the Fourth Military Medical University (FMMU) in Xi’an, China. All procedures were approved by the Institutional Animal Care and Use Committee of the FMMU and complied with animal experimental guidelines. The rabbits were fasted prior to the operation and all surgeries were performed using an aseptic technique. The hair on the skull was removed, and 3.5% sodium pentobarbital (1 mL kg^− 1) was used to inject anesthesia into the ear vein.

The rabbit was laid prone on the operating table and disinfected, after which the top of the forehead was shaved. A 5-cm midline incision of the head was used to bluntly separate the soft tissue and periosteum, exposing the skull. A dental drill was used to drill through the skull, layer by layer, to perform unilateral bone flap removal 5 mm to the right outside the sagittal suture, and 5 mm behind the coronal suture. The diameter of the bone hole was 5 mm and the dura remained intact. A gelatin sponge was used to stop the bleeding, and the operating field was cleared (Fig. 1a, b). The rabbits were then fixed in a stereotactic frame. A pneumatically controlled cortex impactor (RWD Life Science Co., Ltd., Shenzhen, China) equipped with a firing pin 4 mm in diameter was used for the operation. The motion profile of the tip of the impactor was programmed to exit 20 mm from the surface, then provide a downward stroke of 25 mm (i.e., an indentation depth of 5.0 mm) at a preset speed of 3.0 m/s, and an indentation duration of 0.5 s. The intact dura mater was impacted (Osier & Dixon, 2016), and the impact was performed twice in succession to establish injury in Group A. Three consecutive shocks with the same parameters were used to establish injury in Group B (Fig. 1c).

After the impact, the surgical site was sutured, and the rabbit was immediately placed on the monitoring table for examination using MIT (Fig. 1d), and data were continuously collected for one hour. During this period, the rabbits were kept in an anesthetized static state without any intervention. A series of continuously measured data frames was obtained.

After monitoring, each animal was deeply sedated and perfused with a frozen heparinized phosphate buffer and 4% frozen buffer paraformaldehyde. The brain was removed, fixed at 4°C for 2–5h, and then stored frozen in 30% sucrose for 48h. Coronal sections (25 µm thick) were cut in a cryostat, pasted on glass slides coated with gelatin, and dried overnight at 37°C. A series of graded alcohol was added to distilled water, then soaked in 0.1% cresol violet acetate for 7 min. The brain slices were dehydrated, removed in xylene, and covered with a mounting medium. Images were obtained using a BX60F microscope (Olympus Corporation, Tokyo, Japan). Microsoft ICE software (Microsoft Corporation, Redmond, WA, USA) was used to assimilate the mosaics into the whole-brain image, and a 1× objective lens was used to examine the image (Watts et al., 2015). Six slices were taken from each animal at the center of the brain damage. The injured brain areas were measured, and the largest damaged area was selected for recording (Fig. 4a, b).

2.2 MIT imaging

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. 3a, the rabbit is placed right below the center of the device, and the head area is 5.6 cm away from the bottom coil. Figure 3b shows that the MIT equipment primarily consists of 16 PCB-type coils uniformly arranged on the circumference of the 21-cm diameter, in which the maximum radius of the circular coil on the PCB is 20 mm. The excitation current of the coil is set to 420 mA and the frequency to 21MHz. When one of the coils is the excitation coil, the remaining 15 are the measurement coils; therefore, one frame of measurement data contains 240 values. We set the first frame of all the monitoring data as the reference frame and the subsequent data as the foreground frame. In the present study, a general standard circular finite element model with the same size as the sensing area of hardware equipment is used for image reconstruction. Figure 3d is a schematic diagram of the ROI selection, and the positioning error of the imaging result is evaluated by calculating the difference between the geometric median of the ROI region $({x_{ROI}},{y_{ROI}})$ and the actual position field $({x_{real}},{y_{real}})$. The positioning error is evaluated using the following formula:

$$PE=\sqrt {{{({x_{ROI}} - {x_{real}})}^2}+{{({y_{ROI}} - {y_{real}})}^2}}$$

In the present study, the time-difference eigenvalue threshold regularization algorithm reconstructed the distribution of conductivity changes. The change in conductivity can be obtained using the following formula (Liu et al., 2014) :

$$\Delta \sigma ={({S^T}S)^{ - 1}}{S^T}({\varphi _p} - {\varphi _b})$$

where $\Delta \sigma$is the conductivity change, ${\varphi _p}$is the foreground frame data, ${\varphi _b}$ is the background frame data, is the sensitivity matrix, ${( \cdot )^{ - 1}}$ is the inverse of the matrix, and ${( \cdot )^T}$ is the transposition of the matrix. Let$H={S^T}S$ be the Hessian matrix. In the eigenvalue threshold method, eigenvalue decomposition is first performed on the Hessian matrix, and then the condition number for regularization to obtain a stable reconstruction solution is set (Chen et al., 2020; Liu et al., 2014; Liu et al., 2008). We set the condition number to 5.0×10⁶ for our reconstruction. During the monitoring imaging process, the background frame data remains unchanged, whereas the foreground frame is the current continuously collected data that changes over time. The red, green, and blue areas in the reconstruction results indicate increased conductivity, constant conductivity, and reduced conductivity, respectively.

2.3 MIT analysis

In the MIT reconstruction image, the mean reconstruction value (MRV) of the region of interest (ROI) was calculated by manually selecting the area where the rabbit’s head was located as the ROI. The MRV is calculated as follows (Xu et al., 2010):

$$MRV=\frac{1}{N}\sum {{\sigma _{ij}}} {\text{ }}ij \in {\text{ROI}}$$

where,${\sigma _{ij}}$ is the reconstruction value of the ROI and is the area of the ROI. Considering individual differences, the MRV data of all rabbits were normalized using the mean of the first 10 MRVs. To study the injury effects of different CCI models, this study used the normalized first 300 frames of data (about 1H), and SPSS software (version 19.0, SPSS Inc., Chicago, USA) to perform statistical analysis on both sets of rabbit data.

2.4 Statistical analyses

The data of the largest brain damage area were analyzed using the Student's t-test. The average normalized MRV of the two groups was analyzed using linear regression. Covariance was used to analyze the slope of the regression line between the two sets of data. The values are expressed as average ± standard error of the mean (s.e.m.). Statistical significance was set at P < 0.05. Statistical analysis was performed using SPSS software (version 19.0, SPSS Inc., Chicago, USA).

3.1 Histological manifestations and grading after trauma

Ten rabbits were randomly divided into two groups: A (n = 5) and B (n = 5). Group A underwent two impacts and Group B underwent three impacts. All animals were monitored and their brains were removed after euthanasia. The results of brain tissue anatomy scans revealed visible brain contusions in both groups. Brain injury in group B was more severe than that in group A, and more extensive bleeding was observed in the cerebral cortex (Fig. 2). The brain tissue was fixed and the injury was identified using Nissl staining. Figures 4a and 4b show the coronal views of the affected regions of the rabbit’s brains. The cortical layers of each brain region were stained. Nissl staining showed that Group B experienced more serious brain injuries, more obvious tissue edema, and greater extent of neuronal edema. The average maximum brain injury area in the image analysis was determined using ImageJ (NIH Image J system, Bethesda, MD). The injured areas in Group A and B were 5.03 ± 0.09 and 5.87 ± 0.16 mm², respectively. The Student’s t-test was performed on both models, and the areas of the two groups of data differed significantly (p = 0.0020), Fig. 4c.

3.2 MIT imaging

The Eq. (2) was used to reconstruct the conductivity change image. The red and blue signals indicate increased and decreased conductivity, respectively. As shown in Fig. 6a, after craniocerebral trauma, MIT can clearly locate the area of the trauma, the location of the target area in the reconstructed image is fixed, and the electrical conductivity increases. The red area representing the increase in electrical conductivity appears on the image in a manner consistent with the position of the injury in the rabbit’ head (Fig. 5a). According to formula (1), the PE value of all reconstructed images is lower than the radius of the target object (r = 0.02800m) (Fig. 5b), and the average positioning error of the image is 0.01372 m. The overall positioning error is small, the position of the target area is relatively fixed, and no obvious changes are observed in other positions.

The normalized MRV was used to evaluate the models with different injury levels, and the local change trend of the local area of the brain injury remained nearly the same. As shown in Fig. 6a, the local high-conductivity area increased. When the rabbit's brain was injured, intracranial hemorrhage and brain contusion appeared, and the normalized MRV of all ten rabbits increased (Fig. 6b, c).

3.3 Differences in normalized MRV for different injury levels

Normalized MRV was used to reconstruct the regression of the two models with different injury levels. Figure 7 shows the relationship between the normalized MRV and data frame index and describes the normalized MRV of the two models and their regression lines. The equations of the two sets of regression lines are determined as follows:

0–100 frames:

Group A: $y=0.05297x - 0.22185,{\text{ }}{r^2}=0.98847,{\text{ }}p<0.05$

Group B: $y=0.0413x - 0.20511,{\text{ }}{r^2}=0.98366,{\text{ }}p<0.05$

100–300 frames:

Group A: $y=0.02214x+2.4991,{\text{ }}{r^2}=0.99320,{\text{ }}p<0.05$

Group B: $y=0.01497x+2.1926,{\text{ }}{r^2}=0.99320,{\text{ }}p<0.05$

The regression line is shown in Fig. 7. The electrical conductivity of Groups A and B exhibited a visible upward trend during one hour of monitoring, and the increasing rate of electrical conductivity in the two groups during the first 20 min was higher than that in the last 40 min. However, during the entire monitoring process, the rate of increase in the electrical conductivity of Group B was less than that of Group A. The two groups differed significantly. The results of the covariance analysis showed that the slopes of the two regression lines were statistically different (p < 0.0001).

Mobile charges exist in biological tissues in the form of ions, ionic groups, and electric dipoles. Biological tissues can be viewed as a combination of electrical resistance and capacitance. The electrical conductivity of biological tissue is its biological property, and different pathophysiological states have different electrical conductivities. The contents of various tissues in the skull are in dynamic balance. Once a certain lesion occurs in the skull, the intracranial conductivity also changes accordingly. Therefore, changes in intracranial conductivity can be used to determine whether the brain tissue is affected.

In this study, the intracranial conductivity significantly increased during the acute phase of TBI. This is similar to the increase in blood conductivity of abdominal subcutaneous injections in rabbits monitored by Chen et al. in 2020 (Chen et al., 2020). It is also similar to the use of EIT by Xu to monitor the increase in conductivity of intracranial blood injection in a pig model (Xu et al., 2010). However, the pathophysiological reaction after TBI in a closed cranial cavity is more complex. When TBI occurs, the blood vessels in the subarachnoid space, pia mater, and cerebral parenchyma are ruptured, and the outflow of blood accumulates in the subarachnoid space, pia mater, and brain parenchyma to form an intracranial hematoma, which may be simultaneously accompanied by subarachnoid hemorrhage. When a TBI occurs, it leads to local brain tissue injury and cell rupture, resulting in intracellular fluid outflow coupled with increased intracranial pressure, insufficient blood perfusion, swelling of unruptured nerve cells and glial cells, and tissue cells around the injury area, resulting in vasogenic and non-vasogenic brain edema. We know that at 21 MHz, the electrical conductivity values of cerebrospinal fluid, blood, gray matter, and cranial white matter are 2.0096, 1.1435, 0.38132, and 0.20583 S/m, respectively (Hasgall et al., 2012), whereas the conductivity of edema tissue after TBI will be higher than that of normal tissue (Harting et al., 2010), and the electrical conductivity of blood will be higher than that of brain parenchyma and edematous brain tissue; therefore, intracranial conductivity continues to increase.

The animal model used in this study strictly ensures that the dura mater of the animal remains intact during the occurrence of traumatic craniocerebral injury, that is, to ensure that the cerebrospinal fluid is not artificially drained. The aim is the restoration of intracranial physiological and pathological conditions after craniocerebral injury. In the closed cranial cavity, cerebrospinal fluid has the highest intracranial electrical conductivity (Rojas et al., 2008) and exists in large amounts in the brain. In a previous study, our team established a three-dimensional craniocerebral simulation model including the scalp, skull, cerebrospinal fluid, brain parenchyma, ventricle. MIT was used for monitoring. This study shows that MIT can image and locate the location of intracranial injury after TBI (Zhang et al., 2021). In the present study, the intracranial pressure increased following craniocerebral injury in rabbits, and the compensatory mechanism for intracranial pressure began. When the local intracranial pressure increased, the local cerebrospinal fluid and arterial blood perfusion decreased and reflux increased, resulting in a decrease in cerebrospinal fluid in the intracranial local sulcus gyrus. This is combined with the increase in electrical conductivity caused by local cell fragmentation and intracellular fluid overflow, as well as the increase in local electrical conductivity caused by the increase in edematous tissue and the formation of intracranial hematoma. In this study, the change in electrical conductivity was divided into two stages. In the first stage, the electrical conductivity changes rapidly within 20 min after the occurrence of TBI, mainly due to the increase in intracranial hematoma, formation of brain edema, and increased cell fragmentation. During the second stage, 20 min after the occurrence of TBI, the change in electrical conductivity decreased significantly, partly due to the decline in the increase of electrical conductivity caused by hematoma, edema and cell damage, and partly due to a reduction in local cerebrospinal fluid caused by increased intracranial pressure. This is consistent with the results reported by Yang et al. on magnetic induction phase shift that showed that the maximum mean phase shift was the turning point of hemorrhage from the acute phase to the chronic phase 20 min after head injury (Chen et al., 2017).

The advantage of using the CCI rabbit model to simulate TBI in this experiment is that the mechanical factors, such as the impact time, speed, depth, and number, can be controlled, and this method has high repeatability. We report the first use of multiple impact head injury. Experiments revealed that the two degrees of TBI injury differ significantly and the head injury in this animal model is closed; that is, the dura mater remains intact and intracranial hemorrhage occurs.

Because of the circular arrangement and circumference of the MIT sensor array, the forward model is used as the standard circular domain in this study. Compared with EIT, a forward model with an accurate boundary shape must be built, and MIT shows its advantages. In addition, because differential imaging is used in this study, the accurate prior information of conductivity distribution need not be known. The MRV used in this study, that is, the average reconstruction value of the ROI in the reconstructed image, can only be obtained by selecting the ROI after MIT was used to obtain the reconstructed image.

In this study, we used two or three impacts to simulate different degrees of TBI. Tissue staining revealed that the maximum bleeding areas in the brain tissues of the two models differed significantly. It is noteworthy that the normalized MRVs obtained using MIT monitoring continued to increase, the linear regression coefficients exceeded 0.98, and the slope of the linear regression was statistically different. We observed that MIT monitoring can distinguish between different degrees of TBI. Interestingly, in the two TBI models, the rate of conductivity change in group A was greater than that in group B; however, the degree of brain injury in group A was lower than that in group B. When severe TBI occurs, the local intracranial pressure changes drastically, the local tissue undergoes cell rupture, necrosis occurs, and tissue edema become heavier, and the compensatory mechanism of cerebrospinal fluid and blood flow appears early (Wykes & Vindlacheruvu, 2015).

Although the intracranial pressure may change after head injury, exhibiting a certain relationship with the change in conductivity, an intracranial pressure monitoring device was not used in this study due to the nature of the conditions. We used a closed head injury model, and the intracranial pressure monitor requires the dura to be opened and a probe implanted. This could cause loss of cerebrospinal fluid and affect the mechanism of intracranial pressure compensation after a closed head injury. Although the skull was opened in this model, the dura mater was kept intact and an airtight cranial state was maintained throughout. In the aspect of location, because the MIT instrument used in this study is limited by the size of the hardware system, the monitoring range is large, and the rabbit brain is very small. Therefore, the instrument does not match the animal and therefore cannot clearly reflect the accurate location of brain injury in the tested animals. However, if a real human body is used, it can better adapt to the size of the MIT hardware system as the brain is larger. In theory, it will achieve better imaging results than those obtained using experimental animals. Although the human spinal fluid 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 fluid(Zhang et al., 2021). The imaging in this study is based on two-dimensional imaging, which mainly reflects the changes in electrical conductivity of the plane where the coil is located. Indeed, full-scale three-dimensional imaging can be more conducive to accurate diagnosis in theory, but because of the extremely poor conditioning of the MIT reconstruction image itself, achieving accurate three-dimensional imaging is difficult.

In addition, this is the first preliminary study of MIT for early monitoring of TBI to our knowledge. Only two injury models were constructed in this study, which can only verify that MIT can identify TBI with different levels of injury. The relationship between the changing trend of the MIT's MRVs and the degree of brain damage requires further evaluation. It is hoped that the degree of brain damage can be inferred via real-time monitoring of changes in MRVs of MIT, and that MIT could be used for long-term continuous monitoring of TBI.

In the present study, MIT was used for the first time to our knowledge to perform imaging diagnosis and evaluation in the early stages of TBI. Multiple impact methods were used to establish TBI models with different levels of injury for the first time, and this animal model was a closed TBI model. The study revealed that MIT can accurately locate the craniocerebral injury, reflect the regularity of electrical conductivity changes after craniocerebral injury, distinguish different levels of TBI, and perform imaging. It provides basic evidence for the application of MIT in the early diagnosis of craniocerebral injury, judgment of injury level, and change in injury during long-term monitoring.

TBI: Traumatic brain injury; CT: Computed tomography; MIT: Magnetic induction tomography; EIT: Electrical impedance tomography; CCI: Controlled cortical impact; FMMU: Fourth Military Medical University; MRV: Mean reconstruction value; ROI: Region of interest;

Acknowledgments

We are thankful to Prof. Fusheng You for his technical comments, and to Xuechao Liu for fruitful comments.

Funding statement

This project was funded by the Basic Strengthen Project under Grant 2019-JCJQ-JJ-096; and by the FMMU Military Talent Support Plan under Grants 2021JSRC15; and in part by the National Natural Science Foundation of China under Grants 31771072 and 61771476; and in part by the Science and Technology Program of Zhejiang under Grant 2019C03054.

Ethical statement

All experimental procedures on rabbits were approved by the Ethics Committee of the Fourth Military Medical University(number of ethical permission: FMMU-E-Ⅲ-001(2-7)).

Data and code availability

All used data and relevant code are available from the corresponding authors upon reasonable request.

Declaration of Competing Interest

We have no competing interests.

Consent for publication

Not applicable.

Credit authorship contribution statement

Yang Jiao: Conceptualization, Formal analysis, Methodology, Writing - original draft, Writing –review& editing. Tao Zhang: Visualization, Writing – original draft, Writing –review& editing, Data curation. Chao Fan: Data curation, Formal analysis. Haiyan Cao: Writing - original draft. Min Chao: Data curation, Formal analysis. Liying Han: Data curation, Formal analysis. Weirui Zhang: Data curation, Formal analysis. Lei Mao: Formal analysis. Ruigang Liu: Writing - original draft, Writing –review& editing. Canhua Xu: Methodology, Conceptualization, Supervision, Writing - original draft, Writing –review& editing. Liang Wang: Formal analysis, Methodology, Supervision ,Methodology, Writing - original draft, Writing –review& editing.

Author details

¹Department of Neurosurgery, Tangdu Hospital of Air Force Medical University, Xi’an, China

²Department of Biomedical Engineering, the Fourth Military Medical University, Xi’an, China

³Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China

⁴HangZhou UTRON Technology Co., Ltd, Hang Zhou, China

Al-Zeibak, S., & Saunders, N. H. (1993). A feasibility study of in vivo electromagnetic imaging. Physics in Medicine & Biology, 38(1), 151.
Chen, M., Yan, Q., Sun, J., Jin, G., & Qin, M. (2017). Investigating the Relationship between Cerebrospinal Fluid and Magnetic Induction Phase Shift in Rabbit Intracerebral hematoma expansion Monitoring by MRI. Scientific Reports, 7(1), 11186.
Chen, Q., Liu, R., Wang, C., & Liu, R. (2020). Real-time in vivo magnetic induction tomography in rabbits: a feasibility study. Measurement Science and Technology, 32(3).
Dowrick, T., Blochet, C., & Holder, D. (2016). In vivo bioimpedance changes during haemorrhagic and ischaemic stroke in rats: towards 3D stroke imaging using electrical impedance tomography. Physiol Meas, 37(6), 765-784. https://doi.org/10.1088/0967-3334/37/6/765
Harting, M. T., Smith, C. T., Radhakrishnan, R. S., Aroom, K. R., Dash, P. K., Gill, B., & Cox, C. S. (2010). Regional Differences in Cerebral Edema After Traumatic Brain Injury Identified by Impedance Analysis. Journal of Surgical Research, 159(1), 557-564.
Hasgall, P. A., Neufeld, E., Gosselin, M. C., Klingenböck, A., Kuster, N., Klingenbock, A., Hasgall, P., & Gosselin, M. (2012). It'is Database for thermal and electromagnetic parameters of biological tissues. Journal of Cell Biology, 93(1), 170-170.
Jang, G. Y., Ayoub, G., Kim, Y. E., Tong, I. O., & Woo, E. J. (2019). Integrated EIT system for functional lung ventilation imaging. BioMedical Engineering OnLine, 18(1).
Jiang, J. Y., Gao, G. Y., Feng, J. F., Mao, Q., Chen, L. G., Yang, X. F., Liu, J. F., Wang, Y. H., Qiu, B. H., & Huang, X. J. (2019). Traumatic brain injury in China. The Lancet Neurology, 18(3), 286-295.
Liu, R., Li, Y., Fu, F., You, F., Shi, X., & Dong, X. (2014). Time-difference imaging of magnetic induction tomography in a three-layer brain physical phantom. Measurement Science & Technology, 25(6), 065402.
Liu, R., Li, Y., You, F., Shi, X., Fu, F., & Dong, X. (2008). Preliminary imaging results of magnetic induction tomography based on physical phantom. IEEE.
Ma, L., & Soleimani, M. (2018). Magnetic Induction Spectroscopy for Permeability Imaging. Sci Rep, 8(1), 7025. https://doi.org/10.1038/s41598-018-25507-4
Maas, A. I. R., Menon, D. K., Adelson, P. D., Andelic, N., Bell, M. J., Belli, A., Bragge, P., Brazinova, A., Büki, A., Chesnut, R. M., Citerio, G., Coburn, M., Cooper, D. J., Crowder, A. T., Czeiter, E., Czosnyka, M., Diaz-Arrastia, R., Dreier, J. P., Duhaime, A. C., Ercole, A., van Essen, T. A., Feigin, V. L., Gao, G., Giacino, J., Gonzalez-Lara, L. E., Gruen, R. L., Gupta, D., Hartings, J. A., Hill, S., Jiang, J. Y., Ketharanathan, N., Kompanje, E. J. O., Lanyon, L., Laureys, S., Lecky, F., Levin, H., Lingsma, H. F., Maegele, M., Majdan, M., Manley, G., Marsteller, J., Mascia, L., McFadyen, C., Mondello, S., Newcombe, V., Palotie, A., Parizel, P. M., Peul, W., Piercy, J., Polinder, S., Puybasset, L., Rasmussen, T. E., Rossaint, R., Smielewski, P., Söderberg, J., Stanworth, S. J., Stein, M. B., von Steinbüchel, N., Stewart, W., Steyerberg, E. W., Stocchetti, N., Synnot, A., Te Ao, B., Tenovuo, O., Theadom, A., Tibboel, D., Videtta, W., Wang, K. K. W., Williams, W. H., Wilson, L., & Yaffe, K. (2017). Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol, 16(12), 987-1048. https://doi.org/10.1016/s1474-4422(17)30371-x
Mahdavi, H., & Rosell-Ferrer, J. (2017). In-bed vital signs monitoring system based on unobtrusive magnetic induction method with a concentric planar gradiometer. Physiol Meas, 38(6), 1226-1241. https://doi.org/10.1088/1361-6579/aa6a09
Osier, N., & Dixon, C. E. (2016). The Controlled Cortical Impact Model of Experimental Brain Trauma: Overview, Research Applications, and Protocol. Methods Mol Biol, 1462, 177-192. https://doi.org/10.1007/978-1-4939-3816-2_11
Rojas, R., Rubinsky, B., & González, C. A. (2008). The effect of brain hematoma location on volumetric inductive phase shift spectroscopy of the brain with circular and magnetron sensor coils: a numerical simulation study. Physiol Meas, 29(6), S255-266. https://doi.org/10.1088/0967-3334/29/6/s22
Servadei, Nasi, M. T., Giuliani, G., & F. (2000). CT prognostic factors in acute subdural haematomas: the value of the 'worst' CT scan. British Journal of Neurosurgery, 14(2), 110-116.
Watts, L. T., Long, J. A., Manga, V. H., Huang, S., Shen, Q., & Duong, T. Q. (2015). Normobaric oxygen worsens outcome after a moderate traumatic brain injury. Journal of Cerebral Blood Flow & Metabolism, 35(7), 1137-1144.
Witkowska-Wrobel, A., Aristovich, K., Faulkner, M., Avery, J., & Holder, D. (2018). Feasibility of imaging epileptic seizure onset with EIT and depth electrodes. Neuroimage, 173, 311-321. https://doi.org/10.1016/j.neuroimage.2018.02.056
Wykes, V., & Vindlacheruvu, R. (2015). Intracranial pressure, cerebral blood flow and brain oedema. Surgery, 355-362.
Xiang, J., Dong, Y., & Yang, Y. (2020). Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning. IEEE Trans Med Imaging, 39(12), 4102-4112. https://doi.org/10.1109/tmi.2020.3013100
Xu, C. H., Wang, L., Shi, X. T., You, F. S., Fu, F., Liu, R. G., Dai, M., Zhao, Z. W., Gao, G. D., & Dong, X. Z. (2010). Real-time imaging and detection of intracranial haemorrhage by electrical impedance tomography in a piglet model. J Int Med Res, 38(5), 1596-1604. https://doi.org/10.1177/147323001003800504
Yang, B., Li, B., Xu, C., Hu, S., Dai, M., Xia, J., Luo, P., Shi, X., Zhao, Z., Dong, X., Fei, Z., & Fu, F. (2019). Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema. Neuroimage Clin, 23, 101909. https://doi.org/10.1016/j.nicl.2019.101909
Zhang, T., Liu, X., Zhang, W., Dai, M., Chen, C., Dong, X., Liu, R., & Xu, C. (2021). Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging. Physiol Meas, 42(6). https://doi.org/10.1088/1361-6579/ac05d4

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Figures

1. introduction

2. Materials And Methods

2.1 Establishment of controlled cortical impact (CCI) Model

2.2 MIT imaging

2.3 MIT analysis

2.4 Statistical analyses

3. Results

3.1 Histological manifestations and grading after trauma

3.2 MIT imaging

3.3 Differences in normalized MRV for different injury levels

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1