Manwaring, Preston K. PhD*; Moodie, Karen L. DVM†; Hartov, Alexander PhD*; Manwaring, Kim H. MD‡; Halter, Ryan J. PhD*§
Electrical impedance tomography (EIT) is an imaging method that can create a cross-sectional image of a region of the body by making use of an array of electrodes typically placed on the skin or surface of an organ being imaged.1,2 EIT has been actively investigated since the 1980s in modeling transthoracic and cranial disease processes.3 Because electrical impedance is highly sensitive to water content, it has also been applied to various tissues such as the human breast for cancer screening and to possibly distinguish early neoplastic transformation from benign cystic processes.4,5 Although such sensitivity to water content could be anticipated to benefit interrogation of various brain pathologies, the cranium has been an impediment to deeper or more central imaging due to its electrically insulating properties limiting high-frequency current penetration.6 Numerous studies exploring cerebral impedance imaging methods for brain injury detection discuss this shortcoming.7–12 However, the unique benefits of EIT, which include continuous imaging without risk of ionizing radiation, warrant further refinement of the method.
EIT uses small magnitude currents and voltages, which are driven and sensed, respectively, between different groups of electrodes. A computational algorithm is implemented to transform these electrical measurements into cross-sectional images of electrical conductivity.13 Equipment required to make such measurements can be relatively inexpensive.
A promising adaptation of previously used circumferential electrode arrays has been recently described by our group to circumvent the impediment to cranial current penetration.14 It is based on extending an external array to include an implanted centrally located EIT electrode. Holder et al.15 showed that a multitude of implanted electrodes significantly increases sensitivity within the cranium. Such techniques generally remain impractical outside of an exploratory acute animal model and are typically dismissed by clinicians because of increased risk for infection. However, certain presently used standard of care monitoring techniques in injured patients include the use of invasive devices and thus lend themselves well to doubling as an internally located electrode. The addition of even one such electrode forces current through the less conductive cranium, and deeper into the central brain, improving resolution and sensitivity in that region.
A routine assessment tool for the clinical management of head trauma in the comatose patient with a Glasgow Coma Score of ≤8 is the insertion of an external ventricular drain for measurement of intracranial pressure (ICP) and drainage of cerebrospinal fluid (CSF) as needed to improve management of rising ICP with brain edema.16 The external ventricular drain is available in various designs. A typical unit consists of a 3-mm outer diameter silicone catheter connected to an external saline column strain gauge or a fixating cranial bolt tapped into the cranium with insertable tubing and ICP sensor.a Some implanted ICP and external ventricular drain systems have recently incorporated additional sensing transducers for tissue oxygen tension (TcO2), pH, temperature, and thermal diffusion as a measure of blood flow.b,c A simple adaptation of any of these designs with an insulated conductive sleeve or wire extending to the tip could provide a central EIT electrode at the deep parenchymal level just superior to a lateral ventricle.
In an effort to demonstrate the detection capability and potential practical application of EIT in human patients suffering severe head trauma, a swine model is described, which incorporates a circumferential scalp electrode array and a cranial bolt for positioning both a combined ICP sensor and intracranial electrode.
Various commonly used manipulations were adapted to simulate aspects of human head trauma and to assess the potential strengths of continuous axial imaging of impedance change. These included induction of mass effect by inflation and deflation of an implanted intraparenchymal balloon, injection of intraparenchymal blood, and cessation of cerebral blood flow by heart stoppage. This article further describes the approach used for continuous EIT, an analysis of the multianimal trial, and results and discussion of the applicability and potential of EIT to monitor traumatic brain injury in a patient.
Eightd domestic piglets (age range 3–4 weeks, weight average 10 kg) were sedated with midazolam followed by induction and maintenance of anesthesia with endotracheal isoflurane 1% to 2.5%. This regimen was selected to minimize impact on cerebral blood flow. The acute study protocol with animal termination at conclusion was approved by the Institutional Animal Care and Use Committee of Dartmouth-Hitchcock Medical Center. After instrumenting the animal for arterial blood pressure (ABP), oxygen saturation, and end-tidal carbon dioxide, each animal was positioned prone. A midline incision along the sagittal suture allowed exposure of the paramedian cranium. A paramedian burr hole was placed 1 cm lateral to the sagittal suture and 1 cm anterior to the coronal suture. A modified hydraulically sealed cranial tapping bolt was inserted into this hole to the level of the dura. A Teflon-insulated stainless steel EIT electrode tube incorporating an ICP sensor (Codman Microsensor, Raynham, MA) was inserted through the bolt to the level of deep white matter just above the right lateral ventricle (Fig. 1). A contralateral paramedian burr hole was created followed by placement of a 3-Fr cannulated 0.15-mL Fogarty catheter (Edwards Lifesciences Corp., Irvine, CA) to the deep parenchyma at the level of the lateral ventricle (Fig. 2). An array of 8 EIT scalp electrodes 1 × 1.5 cm constructed from modified silver chloride electrocardiogram pads (Kendal CA-310, Covidien, Mansfield, MA) was placed on the scalp in a coplanar orientation surrounding the lateral ventricles centrally, and extending from the frontonasal region to occiput and overlying temporalis and occipitalis musculature.
After a 10-minute settling period, each animal underwent the following sequentially induced injury events (Fig. 3A):
* stabilization period without any induced injury
* inflation of the parenchymal balloon (Fogarty catheter) with 0.15 mL computed tomography (CT) contrast dye for 10 minutes, followed by deflation
* instillation of 1 mL of arterial drawn, unclotted blood into the white matter
* termination with heart stoppage by IV Euthasol (Virbac, Ft. Worth, TX) injection
No manipulations were made during the stabilization period. Recording during this period provided a reference with which to examine signal variance.
Electrical Impedance Tomography
Real-time EIT was recorded at 100 frames per second using custom time-division multiplexing hardware based on a National Instruments (Austin, TX) data acquisition platform. The excitation frequency was 50 kHz with 5 mA peak of applied current. Each electrode was driven in round-robin fashion such that at any time 2 electrodes provided the current excitation and all other electrodes recorded the resulting electrical potential. All electrode combinations are used and cycled through 100 times per second. Electrode 1 is the central electrode and 2 to 9 are the scalp surface electrodes. This represents a 4-electrode or tetrapolar measurement scheme.
ICP, PCO2, and invasive ABP were recorded in synchrony with EIT acquisition using a CardioCap/5 (GE Healthcare, Pollards Wood, United Kingdom) or a MP150 (Biopac, Goleta, CA) system. Real-time 2-dimensional (2D) tomographic reconstructions were computed with the pig snout oriented to the right (3 o’clock position) with no attempt to warp a circular axial finite element mesh (the image reconstruction domain) to the ovoid shape of the intracranial space. The data were postprocessed in MATLAB (Mathworks, Natick, MA) for statistical analysis using an average of 100 EIT frames about the time the event occurred. EIT images were scaled in color with red representing the most conductive and blue the least conductive regions. Thus, an insulator such as a silicone balloon, imaged at the frequency used in this study (50 kHz), would appear blue; saline, CSF, and blood would appear red.
The same method of single-step differential computational analysis and image rendering for real-time vascular changes used by Halter et al.13,14,17 was used. This approach produces an image of the change in conductivity between 2 time points (e.g., preballoon and postballoon inflation). Similar to difference imaging in CT, EIT baseline data are collected before an event, and subsequently subtracted from postevent data. A conductivity difference (Δσ) image is computed based on this subtraction (e.g., Δσ ⇒ postinjury − baseline; Fig. 3B). Specifically, a circular finite element grid consisting of 1345 nodes is used to define the imaging domain; the computation produces a Δσ at each of the grid nodes or intersections, the magnitude of which is represented by a colored pixel in the reconstruction image. Regions of interest (ROIs) can be analyzed from each of these images by extracting the nodes and associated Δσ values from within the specified ROI.
The benefits of difference imaging are that electrode positions do not need to be exact, systematic signal errors specific to the instrumentation are reduced, and computations are faster. Every injury image was created in this fashion. The nodal conductivity differences are reported in terms of mS/m (mS = milli-Siemens, S = 1/ohm). Image accuracy in EIT is dependent on a number of factors including the assumed geometry of the reconstruction domain. As discussed previously, the reconstruction domain is not warped to match the shape of the head because imaging methods, such as CT, were not available at the time of data acquisition. The nodal Δσ values are therefore not expected to accurately reflect the actual change in absolute conductivity (because of a mismatch in the reconstruction geometry and actual cranial geometry); however, because the same geometry was used for computing all images, the relationships between the Δσ for the different manipulations and events are expected to be valid representations of relative conductivity changes.
Specific time points associated with the different induced injury events were logged within the data acquisition software and were synchronized with the physiologic and EIT data. Each time point was selected for analysis of synchronized ICP, electrocardiogram, and ABP data (Fig. 3).
One ROI was selected from each image based on the observed Δσ change. The ROI was chosen as the 5% of nodes (68/1345 nodes) corresponding to the most or least conductivity change depending on whether the manipulation resulted in an increase or decrease in conductivity, respectively. In the example of Figure 3, the blue region would be identified by selecting the lower 5% of all nodes (decrease in conductivity associated with the inflating balloon), whereas in Figures 4 and 5 the upper 5% of all nodes would be selected (increase in conductivity associated with balloon deflation and blood injection). Figure 4 has the automatically selected ROI nodes for that particular experiment superimposed over the reconstructed image. In 1 of the 24 cases (8 animals × 3 induced injury events), a few of the initially identified nodes within the lower 5% (during balloon inflation) were found outside the primary population (i.e., at the boundary of the image); these nodes were removed from the analysis so that the ROI consisted of a single contiguous population of nodes. No generally accepted method for selecting ROIs in EIT exists at present; this approach of selecting 5% of the nodes enables consistent interexaminer results.
ROI nodes based on the postinjury images (ROIa; Δσ ⇒ postinjury − baseline) were identified for each animal and injury as described earlier. This same set of nodes was also used to define an ROI within the preinjury images (ROIp; Δσ ⇒ preinjury − baseline). Nodal Δσ values were extracted from each ROIa and ROIp and bootstrapped (random resampling with replacement) 5000 times. The mean of the bootstrapped Δσ values within individual ROI and backgrounds were found to be normally distributed (based on Q-Q plots and Lilliefors tests). Although this step may have been unnecessary, because the results (means and standard deviations [SD]) from the bootstrapped data were the same as for the raw data, these inputs guarantee that the requirements of normality for the statistical tests are met. EIT reconstructions often lack normality because of inherent spatial structure in 2D EIT images and electrode artifacts often observed on the boundary.
Two statistical tests were performed to assess how significant changes in conductivity were. First, an ROIa to background unequal variance unpaired t test was run on each injury event imaged to assess whether the medians of the ROI and background conductivity groups were statistically different from each other. There were 1277 background nodes and 68 ROI nodes due to the 5% selection scheme described earlier. Second, the postinjury 68 ROI nodes (ROIa) were compared with that of the preinjury image nodes (ROIp) using the same unpaired t test with unequal variance. The unpaired t test was used because it is impossible to randomize the order of before and after injury measurements, as one would desire for a paired t test analysis. In this scenario, the degrees of freedom do not need to be adjusted, even when there is a positive correlation between the before/after cases.18,19 This second test yields information about how statistically significant the observed change in conductivity between preinjury and postinjury is. The ICP sensor data are also reported for reference. All values reported are based on 1 SD. All results are calculated based on 95% confidence intervals. Results are shown as mean ± 1 SD.
Control measurements were made for all 8 pigs during the stabilization period. The 8 pigs had a mean conductivity difference of 0.12 ± 0.18 mS/m across the entire imaging domain. As a control, the conductivity difference over the 10-minute period should be near zero. ICP remained within a range of 4 to 10 mm Hg. Mean baseline ICP values were pig dependent (5.87 ± 3.48 mm Hg). PCO2 was maintained between 36 and 42 mm Hg during this and all other manipulations.
Following the injection of 0.15-mL contrast dye into the balloon over a 6-second to 10-second period (Fig. 6), ICP increased from an average preinflation baseline of 7.9 ± 3.8 mm Hg to an average immediate postinflation value of 8.8 ± 3.8 mm Hg. This pressure increase is not statistically or clinically significant, although it is detectable within individual experiments. The balloon emerged graphically in the EIT tomogram in the blue scale (decreasing conductivity) in the correct anatomic location given a 2D representation20 (i.e., the ROIs appear in the central left hemisphere region of the reconstruction, where the injuries occurred). ICP stabilized within minutes postevent and then began to increase gradually. Mean Δσ decreased to −11.4 ± 10.9 mS/m and was detected by automatic ROI selection in every case (Fig. 3, Table 1). This effect was significant to a <0.0001 level for all experiments (Table 1). The SD of the means from all experiments is high, but the SD within a single experiment is low as expected.
The radiopaque dye was extracted over an approximately 30-second period. ICP generally decreased to preballoon inflation levels; from 9.56 ± 3.56 mm Hg to 8.1 ± 3.47 mm Hg. Because of the small size of the balloon, this pressure change was not statistically significant but was detectable. Predeflation pressure was higher than immediate postballoon inflation. Automatic ROI selection was possible in all cases except experiment 8. Experiment 8 suffered from CSF leaking onto the electrodes that changed the imaging domain, causing erroneous data to be collected during deflation and was omitted from the mean calculation. This, however, did not affect the other manipulations performed on experiment 8. Mean ROI Δσ increased to +9.4 ± 8.8 mS/m with experiment 8 omitted (Fig. 4, Table 2). Again, the SD of the means from the group of all experiments was high, but the SD within a single experiment was quite low. All 7 remaining animal experiments showed this manipulation to be statistically significant at a P < 0.0001 level. The ROI locations appeared similarly located in the central left hemisphere of the reconstruction.
Intraventricular Injection of Blood
Following the injection of 1 mL of unclotted arterial blood into the white matter above the left lateral ventricle, ICP increased from an average baseline of 9.1 ± 3.1 mm Hg to a peak of 24.4 ± 7.17 mm Hg. It stabilized initially then began to climb after a few minutes. A marked increase in conductivity is seen as a red circular region in the EIT tomogram (Fig. 5). The mean conductivity difference increase for the pigs was +19.5 ± 11.5 mS/m (Table 3). The SD of the means from the group of all experiments seen in the table is high, but low within single experiments as is expected. A decrease in conductivity is seen in the cortical margins on the same side. Six animal experiments showed this manipulation to be statistically significant (P < 0.0001), with 2 experiment outliers being omitted. Although significant in the ROI test, the conductivity change of experiments 1 and 5 were opposite in magnitude to the other experiments and not physiologically realistic. These 2 results were therefore omitted from the mean. The ROIs in this manipulation appear more central to the reconstruction than in the mass effect injuries due to migration and distribution of the blood into the subdural space.
Cerebral Blood Flow Arrest by Heart Stoppage
Euthanasia by IV injection of Euthasol demonstrated an almost immediate, marked, and symmetric decrease in cerebral conductivity over the entire image for each animal (Fig. 7); ROI analysis was not used because the effect was global. The decrease in conductivity continued for up to 10 minutes postmortem until data recording ended. ICP after a minimum of 5 minutes postmortem was 6.38 ± 3.01 mm Hg. Mean Δσ between the period just before euthanasia and at a minimum of 5 minutes postmortem throughout the region sensed was −12.6 ± 13.2 mS/m.
This study investigated pathophysiologic effects common to severe human head injury to determine sensitivity to detection by enhanced EIT using a central electrode that doubled as an ICP sensor. The manipulations included mass effect by intraparenchymal balloon inflation, injection of freshly drawn, nonclotted arterial blood into the parenchyma to simulate delayed intracerebral hemorrhage, and cessation of cerebral blood flow by heart stoppage during euthanasia. Each of these effects showed a statistically significant change in electrical conductivity and rendered anatomically correct regions of change in the axial tomogram. It is interesting to note that the balloon mass effect and localization was statistically detectable with volume injections of only 0.15 mL contrast dye, but the rise in ICP was both statistically and clinically insignificant (7.9–8.8 mm Hg). Although one could expect a much larger ICP rise if a 1-mL volume of dye were injected (this remains to be established in subsequent studies), this sensitivity points to the potential usefulness of the difference imaging approach for early detection of worrisome change. It was expected, and we believe, that the rapid inflation of the balloon was injurious to the tissue promoting cerebral edema, which was evidenced by gradual increases in ICP until the time of balloon deflation.
Despite the statistically significant changes observed, there is relatively large variability in conductivity difference for the group of experiments (i.e., the means between animals undergoing the same injury do not tightly cluster). This is simply due to the fact that no 2 experimental setups are alike. However, variability within a single experiment is potentially attributable to several factors such as drift, which may be inherent to the EIT instrumentation, electrode drift due to gel ionic transport, and cable movement.
EIT system drift is not a likely source based on our previous laboratory investigations of saline tank phantoms and resistor phantoms that showed good repeatability in measurements over many hours, an essential requirement for difference imaging. Electrode drift due to gel ionic transport across the skin or oxidation could have influenced stability if data sets were far removed temporally from the event of interest. Such drift usually occurs over extended periods of time. Each experimental manipulation was ≤10 minutes and not far removed from the baseline measurements. Cable movement is also not a significant contributor because each cable is actively shielded and carefully fixed in place during the experiments.
The most dominant cause of variability in conductivity measurements within the 10 windows is likely attributable to respiratory effects. Depending on ventilator settings, 100 EIT signal averages may cover a fraction of or >1 breathing cycle. A bias in the averaged data will occur when less than a whole breathing cycle is used for analysis or the effects are otherwise mitigated (e.g., temporarily halting ventilation). Inspiration impedes blood outflow through the jugular, thereby momentarily causing bloodlogging or engorgement of the brain and holding ICP at higher values.
This source of variability was explored in 1 experiment by creating a new set of measurements using ABP data to determine diastolic and systolic phases of the heart. By averaging EIT frames acquired during the 2 phases over several seconds of data, a difference image could be created between the diastole and systole data sets. The breathing signal was removed by rejecting data points with ICP excursions clearly caused by ventilation. The method appeared to refine the blood perfusion pattern in the anterior and middle cerebral arterial regions (Fig. 8).
The method is sensitive to physiologic changes, but the variability presents a challenge for specificity. Ways of decreasing variability in mean Δσ by normalizing parameters to individual cases, instead of looking at the data as a whole, as done here, may be advantageous and should be explored. Another option, once sufficient data are collected, will be to look at data trends and establish statistical thresholds that suggest significance in image conductivity. Another option, suggested by Gómez-Laberge et al.,21 may be to use a pixel-grouping algorithm in conjunction with threshold values, as demonstrated in a study of ventilated pigs.
Mean resting ABP was observed to vary from animal to animal. Several animals appear to be hypotensive. Low blood pressure may affect the change in conductivity beat-to-beat as blood flow is limited, but this was not explored. Mass effect by balloon inflation, intracerebral hematoma, and blood flow cessation are large effects unlikely to be affected by low ABP.
A limitation of the computational method used here is that a 2D circular rendering was used to represent a more complex 3-dimensional (3D) geometry, in which not all electrodes were coplanar and the imaging domain is not circular. Forcing these data acquired from the 3D environment to fit a 2D solution will cause some distortion in the tomogram and localization of the ROIs. Simulation and laboratory experiments have demonstrated that objects tend to move more centrally in such circumstances.20 Three-dimensional models for EIT are becoming more common in the research setting as computational power has improved. However, these implementations are typically performed postacquisition and not in real time using expensive high-end computational hardware and sophisticated computationally intensive algorithms. Our approach represents a technology capable of performing EIT in real time using a midrange portable computer. Although this approach has been demonstrated to provide information regarding pathologic changes in the cranium, localization is not perfect. As higher speed, lower cost computation platforms become available, it would be worth incorporating more anatomically correct 3D renderings into this technology to enhance precision of localization.
Another limitation of the method is that difference imaging requires a baseline. It is difficult to generate images of injury if a preinjury baseline is unavailable. Therefore, the method of difference imaging must be seen as a trend monitor to observe deterioration and the effects of intervention. To be optimally useful in a clinical environment, we anticipate that the baseline data and image obtained following instrumentation with the ICP and EIT central catheter should be closely correlated to timing and perhaps overlaid as an anatomic graphic to the baseline CT scan of the head. Such graphical overlays highlighting areas of functional change are familiar to intensivists, neurologists, and neurosurgeons and include examples of functional magnetic resonance imaging and electroencephalography maps.
Absolute imaging in EIT is an option when baseline or standard reference values of impedance are not available. This method, however, is more sensitive to noise, model inaccuracies, and electrode position and condition (e.g., contact impedance). It is also a much more computationally intensive problem making rapid tomographic rendering a challenge. In the environment of craniocerebral trauma, where scalp and cranium are injured with evolving patterns of edema and hematoma, such absolute imaging may be of less value than trend detection within the cranium.
This animal study was designed to explore the potential benefits and feasibility of a continuous axial image rendering of the human brain. The manipulated variables of mass effect by balloon inflation, intracranial hemorrhage, and cerebral blood flow cessation represent challenging issues of urgent recognition in the intensive care environment in patients who have suffered severe head injuries. At present, such patients undergo intermittent CT imaging with repeat imaging based on the clinical course and protocols for discernment of otherwise unrecognized changes by clinical examination or routinely measured parameters, e.g., ICP and TcO2. As these patients are often deeply sedated or induced into pharmacologic coma and muscular relaxation to optimize control of ICP, a means of monitoring is desirable that can more effectively signal dangerous changes. Furthermore, no transducers presently provide a cross-sectional map of the brain. Local sensing such as TcO2 and thermal or laser cerebral blood flow monitoring cannot be confidently generalized to the brain. CT and magnetic resonance imaging normally require transportation to a radiology center with the attendant risks of detachment from monitoring and the stress of movement.
The electrical path of the very low-level, high-frequency current used in EIT is not straight between 2 electrodes but rather it constitutes a diffuse field following the path of least electrical impedance. This nature of EIT makes high-resolution images (such as in x-ray tomography) difficult. No practical algorithm to fully address this physical limitation is presently anticipated. However, its resolution can be improved by increasing the number of electrodes used, keeping in mind however, that an excessive number may become encumbering to care. The simplicity of 8 circumferential contacts by corkscrew, staple, or adhesive electrodes in the clinical context of typical scalp swelling, lacerations, postoperative scars, skull fracture, or even decompressive craniectomy becomes a limiting necessity. A further practicality is that our present 8 × 1 array can render 100 image updates per second with the computational power of a portable bedside computer. Bedside manipulations such as transient venous occlusion increase in positive end-expiratory pressure, and injection into an external ventricular drain of saline for clearance or compliance testing should become readily apparent in the continuously rendered tomogram.
From an electrical safety stand point, the described levels of current used in this study are noninjurious and are within accepted safety guidelines for externally applied currents22 although long-term effects of an active internal electrode on brain tissue should be established in further studies. By way of comparison, EIT currents may be smaller in magnitude and lower in duty cycle than typical deep-brain stimulators.
While several other EIT research groups have explored the effects of intraperitoneal and intracranial hemorrhage,22–24 to the best of our knowledge, the novel EIT implementation described in this article is at present the only study to show in vivo, real-time, tomographic reconstructions of mass effect, hemorrhage, and cessation of blood flow. Xu et al.25 show in vivo hematoma creation and detection with 16 external electrodes. It is interesting to note that their results show a decrease in conductivity with injection of 5 mL of blood. In contrast, this study shows an increase, which corresponds well with literature indicating that blood is more conductive than brain.
Our adaptation of the external ventricular drain or ICP monitor into a central electrode may be promising to extend the depth of resolution and sensitivity into the central brain region. This technology may improve monitoring and guidance for therapeutic intervention in other pathological conditions beyond head injury where edema, sodium shifts, and mass effect participate. These include stroke, hydrocephalus, and postoperative changes.
We have described a novel and practical combination of a simple circumferential and intracranial electrode EIT system with a standard of care pressure transducer that for the first time, has been used to monitor intracranial injuries in vivo, in real time, with statistically significant detectability.
Name: Preston K. Manwaring, PhD.
Contribution: This author was responsible for the study design, conduction of the study, data collection, data analysis, and manuscript preparation.
Attestation: Preston K. Manwaring approves this manuscript and attests to the originality of the work and integrity of the data collection and analysis. This author is also the archival author.
Name: Karen L. Moodie, DVM.
Contribution: This author assisted in the conduction of the study and data collection.
Name: Alexander Hartov, PhD.
Contribution: This author assisted in the study design and data analysis.
Name: Kim H. Manwaring, MD.
Contribution: This author assisted in the study design and manuscript preparation.
Name: Ryan J. Halter, PhD.
Contribution: This author assisted in the study design, data collection, data analysis, and manuscript preparation. This author is also the study principal investigator on the R21 research grant.
Attestation: Ryan Halter attests to the originality of the work and integrity of the data collection and analysis.
This manuscript was handled by: Dwayne R. Westenskow, PhD.
a CSF External Drainage Devices. Available at: http://emedicine.medscape.com/article/1982472-overview#aw2aab6b2. Accessed February 11, 2012. Cited Here...
b LICOX INFO. Available at: http://www.integra-ls.com/home/search.aspx?qry=licox. Accessed October 19, 2009. Cited Here...
c Intraoperative Cerebral Blood Flow Measurement: Quantitative CBF Monitoring Techniques. Available at: http://www.medscape.com/viewarticle/405659_3. Accessed February 12, 2012. Cited Here...
d Conductivity change due to blood injection represents the limiting manipulation; inflation of an insulative balloon has an almost infinite conductivity contrast compared with brain. Nominal conductivity of brain and blood are 127 mS/m and 680 mS/m, respectively. The conductivity estimation algorithms used in EIT typically do not recover the full contrast actually present; to account for this, the mean expected contrast between preinjury and postinjury Δσ was conservatively assumed to be 1:3 with SDs of 50% (e.g., blood injection would be detected as 3 times the background conductivity irrespective of the actual value). SD represents an estimate of variation between animals for 1 particular injury (e.g., hematoma creation by blood injection). Five samples are necessary to achieve a power of 0.80 with a 95% confidence interval for a 2-tailed Student t test. Eight samples provide added assurance. Cited Here...
1. Metherall P, Barber DC, Smallwood RH, Brown BH. Three-dimensional electrical impedance tomography. Nature. 1996;380:509–12
2. Bayford R, Tizzard A. Bioimpedance imaging: an overview of potential clinical applications. Analyst. 2012;137:4635–43
3. Holder D Electrical Impedance Tomography: Methods, History, and Applications. 2005 London The Institute of Physics Publishing
4. da Silva JE, de Sá JP, Jossinet J. Classification of breast tissue by electrical impedance spectroscopy. Med Biol Eng Comput. 2000;38:26–30
5. Poplack SP, Tosteson TD, Wells WA, Pogue BW, Meaney PM, Hartov A, Kogel CA, Soho SK, Gibson JJ, Paulsen KD. Electromagnetic breast imaging: results of a pilot study in women with abnormal mammograms. Radiology. 2007;243:350–9
6. Gonçalves S, de Munck JC, Verbunt JP, Heethaar RM, da Silva FH. In vivo measurement of the brain and skull resistivities using an EIT-based method and the combined analysis of SEF/SEP data. IEEE Trans Biomed Eng. 2003;50:1124–8
7. Yerworth RJ, Bayford RH, Brown B, Milnes P, Conway M, Holder DS. Electrical impedance tomography spectroscopy (EITS) for human head imaging. Physiol Meas. 2003;24:477–89
8. Bayford RH, Boone KG, Hanquan Y, Holder DS. Improvement of the positional accuracy of EIT images of the head using a Lagrange multiplier reconstruction algorithm with diametric excitation. Physiol Meas. 1996;17:A49–57
9. Shi X, Dong X, Shuai W, You F, Fu F, Liu R. Pseudo-polar drive patterns for brain electrical impedance tomography. Physiol Meas. 2006;27:1071–80
10. Tidswell T, Gibson A, Bayford RH, Holder DS. Three-dimensional electrical impedance tomography of human brain activity. Neuroimage. 2001;13:283–94
11. Tidswell AT, Gibson A, Bayford RH, Holder DS. Validation of a 3D reconstruction algorithm for EIT of human brain function in a realistic head-shaped tank. Physiol Meas. 2001;22:177–86
12. Tidswell AT, Gibson A, Bayford RH, Holder DS. Electrical impedance tomography of human brain activity with a two-dimensional ring of scalp electrodes. Physiol Meas. 2001;22:167–75
13. Halter R, Hartov A, Paulsen K. Video rate electrical impedance tomography of vascular changes: preclinical development. Physiol Meas. 2008;29:349–64
14. Manwaring PK, Halter RJ, Borsic A, Hartov A. A modified electrode configuration for brain EIT. J Phys Conf Ser. 2010;224:012062
15. Holder DS, Rao A, Hanquan Y. Imaging of physiologically evoked responses by electrical impedance tomography with cortical electrodes in the anaesthetized rabbit. Physiol Meas. 1996;17(Suppl 4A):A179–86
16. Maas AI, Stocchetti N, Bullock R. Moderate and severe traumatic brain injury in adults. Lancet Neurol. 2008;7:728–41
17. Soni NK, Paulsen KD, Dehghani H, Hartov A. Finite element implementation of Maxwell’s equations for image reconstruction in electrical impedance tomography. IEEE Trans Med Imaging. 2006;25:55–61
18. Proschan MA. On the distribution of the unpaired t-statistic with paired data. Stat Med. 1996;15:1059–63
19. Diehr P, Martin DC, Koepsell T, Cheadle A. Breaking the matches in a paired t-test for community interventions when the number of pairs is small. Stat Med. 1995;14:1491–504
20. Halter RJ, Hartov A, Paulsen KD. Experimental justification for using 3D conductivity reconstructions in electrical impedance tomography. Physiol Meas. 2007;28:115–27
21. Gómez-Laberge C, Hogan MJ, Elke G, Weiler N, Frerichs I, Adler A. Data-driven classification of ventilated lung tissues using electrical impedance tomography. Physiol Meas. 2011;32:903–15
22. International Electrotechnical Commission. IEC-6060–1 Medical Equipment: Part 1: General Requirements for Basic Safety and Essential Performance. Geneva, Switzerland International Electrotechnical Commission, 2005
23. Xu C, Dai M, You F, Shi X, Fu F, Liu R, Dong X. An optimized strategy for real-time hemorrhage monitoring with electrical impedance tomography. Physiol Meas. 2011;32:585–98
24. Sadleir RJ, Tang T, Tucker AS, Borum P, Weiss M. Detection of intraventricular blood using EIT in a neonatal piglet model. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:3169–72
25. Xu CH, Wang L, Shi XT, You FS, Fu F, Liu RG, Dai M, Zhao ZW, Gao GD, Dong XZ. Real-time imaging and detection of intracranial haemorrhage by electrical impedance tomography in a piglet model. J Int Med Res. 2010;38:1596–604