The material is based upon work partially supported by the National Science Foundation under Grant No. IIS0758410. The data was supplied by Carolinas Healthcare System.

1. Methodology Overview
The proposed framework processes the brain CT images of traumatic brain injury (TBI) patients to automatically calculate midline shift in pathological cases and use it as well as other extracted information to predict intracranial pressure (ICP). Figure 1 shows the schematic diagram of the entire framework. The automated midline shift measurement can be divided into three steps. First, the ideal midline of the brain, i.e. the midline before injury, is found via a hierarchical search based on skull symmetry and tissue features3. Secondly, the ventricular system is segmented for each brain CT image4. Thirdly, the actual midline is estimated from the segmented deformed ventricular system using a shape matching method5. The horizontal shift of the ventricular system is then estimated based on the estimation of the ideal midline and the actual midline. After the midline shift is successively estimated, features including midline shift, texture information of CT images, as well as other demographic information are used to predict ICP. Machine learning algorithms are used to model the relationship between the ICP and the extracted features6.
2. Ideal Midline Estimation
<ol>
 <li>This step detects the approximate ideal midline using symmetry of the skull. First, using grayscale thresholding, the skull needs to be segmented from the rest of the content in the CT image. After which, perform an exhaustive search to identify the rotation angles around the mass center of the skull. The optimal rotation angle is defined as the angle that maximizes the symmetry of the resulting halves of the skull. The approximate ideal midline is the line passing through the mass center point and has the optimal rotation angel with respect to the original vertical direction of the CT image.</li>
 <li>This step detects the posterior falx cerebri and anterior falx attachment to the margins of the sagittal sulcus. This step is performed to refine the identified approximate ideal midline. First define two searching rectangles centered at the two intersection points between the approximate ideal midline and the calvarium. Next, choose the sizes of the rectangles empirically so that they cover the anatomical features to be detected as explained below. The anterior falx attachment is detected as the peak point of the ridge on the calvarium and the falx cerebri is detected as the gray line in the posterior region3. </li>
 <li>This step uses those features detected above to refine the ideal midline position. Once the peak point of the anterior falx attachment and the furthest point in the posterior falx cerebri from the calvarum are specified, the refined ideal midline is the line connecting the two points. </li>
</ol>
3. Ventricle Segmentation
<ol>
 <li>First apply a low-level segmentation using a Gaussian mixture model (GMM) for each CT slice4,7. CT images can be divided into 4 types of tissue: bone/blood, cerebrospinal fluid (CSF), gray matter and white matter. For the initialization of the Gaussian Mixture Model, the parameters are estimated based on an iterative K-means segmentation result of the CT image. Then use the expectation-maximization (EM) method to optimize GMM iteratively to better represent the CT image. The hard segmentation result can be obtained by partitioning the CT image into regions based on the maximum probability of the membership that each pixel belongs to different region types.
 </li>
 <li>After the low-level segmentation of GMM, apply further constraints on the segmentation result in order to recognize ventricular regions. Only ventricular regions with size above a certain threshold are retained. Apply constraints on the location of ventricular regions as well using the brain bounding box and the set of ventricle templates. Extract the set of ventricle templates from a standard brain MR images and then enlarge it using the morphological dilation to accommodate variations among different subjects and pathological cases. </li>
</ol>
4. Actual Midline Estimation
<ol>
 <li>Specify the feature points on the ventricle templates extracted from MR. </li>
 <li>Do the Multiple Regions Shape Matching5,8 between segmented ventricles and the MR template. </li>
 <li>Estimate the actual midline based on the identified feature points on the ventricle shapes using shape matching. Then use the average of left side mean and right side mean of the x-coordinates of those feature points to define the x-coordinate of the midline. </li>
</ol>
5. More Feature Extractions
<ol>
 <li>Measure the intracranial hematoma/bleeding volume based on the Gaussian mixture model (GMM) segmentation results obtained from the CT images. The segmented result may include small regions including venous sinus blood and falx cerebri, but they can usually be neglected in comparison to the regions of hematoma. Then count the number of pixels classified as blood for each slice and sum them up. The final sum quantifies the extravasated blood volume in the CT examination. </li>
 <li>Extract texture features. First select six windows in each CT image which captures the gray and white matter but avoid the blood and ventricle structures in the CT image, see Figure 6. Then extract the corresponding texture features by using the following methods: Gray Level Run length9, Histogram analysis, Fourier analysis, Dual Tree Complex Wavelet analysis10. </li>
 <li>Demographic information and injury severity score are also collected. </li>
 <li>All extracted features of each CT image are aggregated to represent the entire CT examination. Specifically, min(f), max(f), median(f), mean(f), std(f) are calculated among all the selected features belonging to the particular CT examination, e.g. a feature &fnof; of the midline shift or a texture feature. For the intracranial hemorrhage amount feature, besides the 5 operators listed above, sum(f) is also added to record the total blood volume. </li>
</ol>
6. ICP Estimation
 The main idea of ICP estimation is to apply machine learning techniques to build a model based on a set of training samples. Then the built model is evaluated on the remaining test samples. Because of the high dimension of extracted features including those from the CT scans and demographic information, feature selection is important to remove unrelated features for a relatively simple thus stable model. Therefore there are two steps to be performed for ICP estimation/prediction. First, select the relative features which are informative in predicting ICPs. The second step is to use Support Vector Machines (SVM) as the learning algorithm to develop and evaluate the training model. Software such as RapidMiner11 is ideal for this task because it is a very well developed tool for most of machine learning algorithms and provides very powerful interfaces to train and evaluate models.
<ol>
 <li>Perform the feature selection in two stages. First use the information gain ratio criterion in the first stage to select the top 50 features. In the second stage, use a genetic algorithm incorporating SVM to further optimize the feature selection. Then use the final set of selected features to build the model for ICP prediction in the following step. </li>
 <li>Perform ICP classification and evaluation through machine learning techniques. The top level diagram of the training and testing are shown in RapidMiner in Figure 8. Apply a 10 fold cross-validation scheme as the outmost layer for evaluation. In order to build a model with better generalization and avoid over-fitting to the training data, nest another layer of cross-validation inside each training fold. In these modules, first apply the feature selection process described above and then use a SVM for classification with its own parameter selection module6. </li>
</ol>

<ol>
	<li>Langlois, J. A., Rutland-Brown, W., Thomas, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Traumatic brain injury in the united states: emergency department visits, hospitalizations, and deaths. , (2006).</li><li>Moore, E. E., Feliciano, D. V., Mattox, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Trauma. , (2003).</li><li>Chen, W., Smith, R., Ji, S. Y., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+Ventricular+Systems+Segmentation+in+Brain+CT+Images+by+Combining+Low-level+Segmentation+and+High-level+Template+Matching.">Automated Ventricular Systems Segmentation in Brain CT Images by Combining Low-level Segmentation and High-level Template Matching.</a> BMC Medical Informatics and Decision Making. 9, (2009).</li><li>Chen, W., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+Ventricles+in+Brain+CT+Images+Using+Gaussian+Mixture+Model+Method.">Segmentation of Ventricles in Brain CT Images Using Gaussian Mixture Model Method.</a> , 15-20 (2009).</li><li>Chen, W., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Actual+Midline+Estimation+from+Brain+CT+Scan+Using+Multiple+Regions+Shape+Matching.">Actual Midline Estimation from Brain CT Scan Using Multiple Regions Shape Matching.</a> , 2552-2555 (2010).</li><li>Chen, W., Cockrell, C., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracranial+Pressure+Level+Prediction+in+Traumatic+Brain+Injury+by+Extracting+Features+from+Multiple+Sources+and+Using+Machine+Learning+Methods.">Intracranial Pressure Level Prediction in Traumatic Brain Injury by Extracting Features from Multiple Sources and Using Machine Learning Methods.</a> , 510-515 (2010).</li><li>Greenspan, H., Ruf, A., Goldberger, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constrained+Gaussian+mixture+model+framework+for+automatic+segmentation+of+MR+brain+images.">Constrained Gaussian mixture model framework for automatic segmentation of MR brain images.</a> IEEE Trans. Med. Imaging. 25 (9), 1233-1245 (2006).</li><li>Belongie, S., Malik, J., Puzicha, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shape+matching+and+object+recognition+using+shape+contexts.">Shape matching and object recognition using shape contexts.</a> IEEE Trans. Pattern Anal. Mach. Intell. 24, 509-522 (2002).</li><li>Weszaka, J. S., Dyer, C. R., Rosenfeld, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+of+texture+measures+for+terrain+classification.">A comparative study of texture measures for terrain classification.</a> IEEE Trans. on Syst., Man, Cyber. , (1976).</li><li>Kingsbury, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+wavelets+for+shift+invariant+analysis+and+filtering+of+signals.">Complex wavelets for shift invariant analysis and filtering of signals.</a> Applied and Computational Harmonic Analysis. 10 (3), 234-253 (2002).</li><li>Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=+Yale:+Rapid+prototyping+for+complex+data+mining+tasks."> Yale: Rapid prototyping for complex data mining tasks.</a> , 935-940 (2006).</li></ol>

No conflicts of interest declared.

In this study, an intuitive and flexible framework is proposed to address two challenging problems: the estimation of the midline shift in CT images and ICP level prediction based on extracted features. The evaluation results show the effectiveness of the proposed method. As far as we know, this is the first time of a systematic study in addressing these two problems. We notice that based on the general framework, there are many potential improvements that can be achieved. For example, in the proposed segmentation, the low level segmentation and high level recognition are separated and currently there is no feedback from the high level to the low level segmentation. This is different from human visual inspection, which has interactions between the low level vision and high level recognition. One potential approach to combine these two levels together is the so-called "model based low-level segmentation". In this method, the low-level segmentation is guided by the high level atlas models of the target structure. For example, a registration algorithm can be applied in the first step to align the CT images to a standard CT image. This may further improve the accuracy of ventricle recognition as well as the estimation of the midline shift because it may provide more accurate mapping in the brain structures between the CT images and the standard CT image. For the ICP prediction, even though the result is promising based on the dataset tested, we have to notice that the size of the samples is limited. It will be more persuasive to validate the results based on another independent dataset. In the application of machine learning for ICP prediction, the sample size is a very important factor. A larger dataset of CT examinations, which may contain more different patterns in both CT images and ICP signals, may give a more informative evaluation of the proposed framework.

Every year there are about 1.4 million traumatic brain injuries (TBI) related emergency department cases in the United States, of which, over 50,000 result in death1. Severe TBI is usually accompanied by an increase in intracranial pressure (ICP) with symptoms such as hematomas and swelling brain tissue. These result in reduced cerebral perfusion pressure and cerebral blood flow, placing the injured brain in additional risk. Severe ICP increase can be fatal, so monitoring ICP for patients with TBI is crucial. This typically requires placement of indwelling catheters directly into the brain for monitoring of pressure, a risky procedure for patients that can only be performed at specialized medical centers. The procedure also involves risk such as infection. However, some signs of elevated ICP may be noticeable in medical imaging. In particular, midline shift is often associated with an increase in the ICP and can be captured from the brain computed tomography (CT) images. As such, these images provide an opportunity for non-invasive detection of elevated ICP which can be used as a pre-screening step before cranial trepanation. CT imaging is still the gold standard for initial TBI assessment among all other imaging modalities, e.g. MRI, because of its high speed and relative low cost2. Furthermore, a CT examination does not require strict patient immobility, and has advantage in revealing severe abnormalities such as bone fractures and hematomas. While CT is commonly used for detection of injuries in the brain, based on the current technology, midline shift is not automatically measured and therefore physicians must assess this important factor by visual inspection. Inaccurate or inconsistent CT interpretation is often associated with the nature of the human visual system and the complex structure of the brain. While small midline shifts are elusive, they are often invaluable for assessment of brain injury, in particular at early stages of injury before a patient's condition becomes more severe. On the other side of the spectrum, large midline shift suggests highly elevated ICP and more severe TBI. However, it is a very challenging task for humans to visually inspect CT images and predict the level of ICP quantitatively. Due to advances in automated computational techniques, features extracted from CT images, such as midline shift, hematoma volume, and texture of brain CT images, can be measured accurately and automatically using advanced image processing methods. However, the relationship between ICP and midline shift as well as other features such as degree of bleeding, the texture from CT images has not been explored. In this paper, a computational framework has been proposed to measure the midline shift measurement as well as other physiological / anatomical features on brain CT images and then predict the degree of ICP non-intrusively using machine learning techniques.

automated midline shift and intracranial pressure estimation based on brain ct images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

The testing CT datasets were provided by the Carolinas Healthcare System (CHS) under Institutional Review Board approval. All subjects were diagnosed with mild to severe TBI when first admitted to hospital. For each patient, the ICP value was recorded every hour using ICP probes inside the ventricle region both before and after CT scans were obtained. To associate the ICP value with each CT scan, average the two closest measurements of ICP to the time of CT scan, both of which are within an hour of the CT scan. Then assign the average as the estimated ICP value at the time of the CT scan. Group the ICP values into two classes: elevated ICP if ICP &gt;12 mm Hg and normal ICP if ICP &le; 12 mm Hg. The datasets contain 17 patients. From this set, 391 axial CT scan images are selected that show ventricles or regions that should have contained ventricles. Figure 2 shows the result of the ideal midline detection. In Figure 3, the ventricles are segmented. Figure 4 shows the estimated actual midline. Figure 5 shows the estimation of midline shift. Figure 7 shows the segmentation of blood using GMM. A quantitative evaluation of the performance is also performed. In most slices (over 80%), the error between the ideal midline estimated by the framework and the manual annotation are around 2 pixels, which is about 1mm. For the actual midline, above 80% has less than 2.25 mm difference provided that the quality of the ventricular segmentation is relatively good (it is defined as "relatively good" if the quality of the segmentation result can be used for estimation of the actual midlines manually). There are 57 CT scans in the evaluation of ICP prediction. The result of the proposed method is evaluated using the following three measures: sensitivity, specificity and accuracy. Sensitivity is defined as
sensitivity = #(true positives) / #(positives).
The specificity is defined as 
specificity = #(true negatives) / #(negatives).
The predication accuracy is defined as
accuracy = #(corrected predicted) / #(total samples). 
An accuracy of about 70% was achieved in our study using 10 fold cross validation. The sensitivity was found to be about 65% and specificity of about 73%. This may suggest a certain predictive power of the proposed method on this data set. The following step would be replication of similar results on other independent data sets. 
<img src="/files/ftp_upload/3871/3871fig1.jpg" alt="Figure 1" /> 
Figure 1. The top level framework of the method. There are three feature extraction modules for the raw CT images: midline shift measurement, texture analysis and blood amount estimation. All the extracted features and other recorded information, such as demographic information are fed into the classification module to predict the ICP levels.
<img src="/files/ftp_upload/3871/3871fig2.jpg" alt="Figure 2" /> 
Figure 2. The result of the ideal midline detection. The red line is the approximate ideal midline. The two rectangular boxes cover the bone protrusion and the lower falx cerebri respectively. These boxes are used to reduce the regions of interest. The green dash line is the final detected ideal midline, which captures the bone protrusion and the lower falx cerebri accurately.
<img src="/files/ftp_upload/3871/3871fig3.jpg" alt="Figure 3" /> 
Figure 3. The result of the ventricle segmentation. The left image is the original CT image. The right image consists of the following lines: the interior edge of the skull, the inner bounding box formed by four lines, the outer bounding box formed by another four lines, the edge of the matched enlarged ventricle template, the red regions representing the detected ventricular regions, the gray regions representing other regions rejected being ventricular regions after applying several constraints on recognizing ventricles.
<img src="/files/ftp_upload/3871/3871fig4.jpg" alt="Figure 4" /> 
Figure 4. The result of the actual midline estimation. This figure shows the estimation results in different CT images with different ventricular shapes. The white regions are segmented ventricular regions. The blue contours are the edge of the matched ventricular templates. The red points are detected feature points representing the inner edges of the ventricles used to calculate the actual midline. The green line is the final estimated actual midline. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/3871/3871fig4large.jpg" target="_blank"> Click here to view larger figure</a>.
<img src="/files/ftp_upload/3871/3871fig5.jpg" alt="Figure 5" /> 
Figure 5. The result of the midline shift estimation. The left image shows the input CT slice. The right image shows the processed result similar to Figure 3. The left vertical green line represents the estimated ideal midline, and the right vertical green line represents the estimated actual midline. The distance between the two lines is the estimated midline shift.
<img src="/files/ftp_upload/3871/3871fig6.jpg" alt="Figure 6" /> 
Figure 6. The six selected windows for texture analysis. The selected windows are the red rectangles avoiding the ventricles.
<img src="/files/ftp_upload/3871/3871fig7.jpg" alt="Figure 7" /> 
Figure 7. The blood segmentation. The left image is the input CT image. The right image shows the segmentation map produced by the GMM method. The brightest region corresponds to the blood region.
<img src="/files/ftp_upload/3871/3871fig8.jpg" alt="Figure 8" /> 
Figure 8. The top level cross-validation in RapidMiner. The left window shows the modules in the training process and the right window shows the modules in the testing process. In the training process, first we use the information gain module and the genetic algorithm module to select features. Then a SVM is used to do classification. The parameter tuning of the SVM is done through a nested cross-validation in the Optimize Parameter process. The final full trained model is the output from the OuterSVM process using all the training data. In the testing module, the selected features are used and the trained model is evaluated. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/3871/3871fig8large.jpg" target="_blank"> Click here to view larger figure</a>.

Watch this Scientific Journal Video about Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images at JoVE.com

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

An automated midline shift estimation and intracranial pressure (ICP) pre-screening system based on computed tomography (CT) images for patients with traumatic brain injury (TBI) is proposed using image processing and machine learning techniques.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift ...

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42.4K Views.  Virginia Commonwealth University. An automated midline shift estimation and intracranial pressure (ICP) pre-screening system based on computed tomography (CT) images for patients with traumatic brain injury (TBI) is proposed using image processing and machine learning techniques.

Video: Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Epidural Intracranial Pressure Measurement in Rats Using a Fiber-optic Pressure Transducer

Elevated intracranial pressure (ICP) is a significant problem in several forms of ischemic brain injury including stroke, traumatic brain injury and cardiac arrest. This elevation may result in further neurological injury, in the form of transtentorial herniation1,2,3,4, midbrain compression, neurological deficit or increased cerebral infarct2,4. Current therapies are often inadequate to control elevated ICP in the clinical setting5,6,7 . Thus there is a need for accurate methods of ICP measurement in animal models to further our understanding of the basic mechanisms and to develop new treatments for elevated ICP.
In both the clinical and experimental setting ICP cannot be estimated without direct measurement. Several methods of ICP catheter insertion currently exist. Of these the intraventricular catheter has become the clinical 'gold standard' of ICP measurement in humans8. This method involves the partial removal of skull and the instrumentation of the catheter through brain tissue. Consequently, intraventricular catheters have an infection rate of 6-11%9. For this reason, subdural and epidural cannulations have become the preferred methods in animal models of ischemic injury. 
Various ICP measurement techniques have been adapted for animal models, and of these, fluid-filled telemetry catheters10 and solid state catheters are the most frequently used11,12,13,14,15. The fluid-filled systems are prone to developing air bubbles in the line, resulting in false ICP readings. Solid state probes avoid this problem (Figure 1). An additional problem is fitting catheters under the skull or into the ventricles without causing any brain injury that might alter the experimental outcomes. Therefore, we have developed a method that places an ICP catheter contiguous with the epidural space, but avoids the need to insert it between skull and brain. 
An optic fibre pressure catheter (420LP, SAMBA Sensors, Sweden) was used to measure ICP at the epidural location because the location of the pressure sensor (at the very tip of the catheter) was found to produce a high fidelity ICP signal in this model. There are other manufacturers of similar optic fibre technologies13 that may be used with our methodology. Alternative solid state catheters, which have the pressure sensor located at the side of the catheter tip, would not be appropriate for this model as the signal would be dampened by the presence of the monitoring screw. 
Here, we present a relatively simple and accurate method to measure ICP. This method can be used across a wide range of ICP related animal models.

A novel technique to record the pressures within the skull is described. The minimally invasive method uses a fibre-optic pressure sensing system to accurately measure intracranial pressure (ICP) in anaesthetized rats without causing significant brain trauma. The technique may be used in a wide range of experimental models.

Elevated intracranial pressure (ICP) is a significant problem in several forms of ischemic brain injury including stroke, traumatic brain injury and ...

A Novel High-resolution In vivo Imaging Technique to Study the Dynamic Response of Intracranial Structures to Tumor Growth and Therapeutics

We have successfully integrated previously established Intracranial window (ICW) technology 1-4 with intravital 2-photon confocal microscopy to develop a novel platform that allows for direct long-term visualization of tissue structure changes intracranially. Imaging at a single cell resolution in a real-time fashion provides supplementary dynamic information beyond that provided by standard end-point histological analysis, which looks solely at 'snap-shot' cross sections of tissue.
Establishing this intravital imaging technique in fluorescent chimeric mice, we are able to image four fluorescent channels simultaneously. By incorporating fluorescently labeled cells, such as GFP+ bone marrow, it is possible to track the fate of these cells studying their long-term migration, integration and differentiation within tissue. Further integration of a secondary reporter cell, such as an mCherry glioma tumor line, allows for characterization of cell:cell interactions. Structural changes in the tissue microenvironment can be highlighted through the addition of intra-vital dyes and antibodies, for example CD31 tagged antibodies and Dextran molecules.
Moreover, we describe the combination of our ICW imaging model with a small animal micro-irradiator that provides stereotactic irradiation, creating a platform through which the dynamic tissue changes that occur following the administration of ionizing irradiation can be assessed.
Current limitations of our model include penetrance of the microscope, which is limited to a depth of up to 900 &mu;m from the sub cortical surface, limiting imaging to the dorsal axis of the brain. The presence of the skull bone makes the ICW a more challenging technical procedure, compared to the more established and utilized chamber models currently used to study mammary tissue and fat pads 5-7. In addition, the ICW provides many challenges when optimizing the imaging.

A Novel High-resolution In vivo Imaging Technique to Study the Dynamic Response of Intracranial Structures to Tumor Growth and Therapeutics

We describe a novel in vivo imaging technique that couples fluorescent chimeric mice with intracranial windows and high-resolution 2-photon microscopy. This imaging platform aids studies of dynamic changes in brain tissue and microvasculature, at a single-cell level, following pathological insults and is adaptable to assess intracranial drug delivery and distribution.

We have successfully integrated previously established Intracranial window (ICW) technology 1-4 with intravital 2-photon confocal microscopy to develop a ...

Creating Anatomically Accurate and Reproducible Intracranial Xenografts of Human Brain Tumors

Orthotopic tumor models are currently the best way to study the characteristics of a tumor type, with and without intervention, in the context of a live animal &#8211;&#160;particularly in sites with unique physiological and architectural qualities such as the brain. In vitro and ectopic models cannot account for features such as vasculature, blood brain barrier, metabolism, drug delivery and toxicity, and a host of other relevant factors. Orthotopic models have their limitations too, but with proper technique tumor cells of interest can be accurately engrafted into tissue that most closely mimics conditions in the human brain. By employing methods that deliver precisely measured volumes to accurately defined locations at a consistent rate and pressure, mouse models of human brain tumors with predictable growth rates can be reproducibly created and are suitable for reliable analysis of various interventions. The protocol described here focuses on the technical details of designing and preparing for an intracranial injection, performing the surgery, and ensuring successful and reproducible tumor growth and provides starting points for a variety of conditions that can be customized for a range of different brain tumor models.

The brain is a unique site with qualities that are not well represented by in vitro or ectopic analyses. Orthotopic mouse models with reproducible location and growth characteristics can be reliably created with intracranial injections using a stereotaxic fixation instrument and a low pressure syringe pump.

Orthotopic tumor models are currently the best way to study the characteristics of a tumor type, with and without intervention, in the context of a live ...

Assessing Cortical Cerebral Microinfarcts on High Resolution MR Images

Cerebral microinfarcts are frequent findings in the post-mortem human brain, and are related to cognitive decline and dementia. Due to their small sizes it is challenging to study them on clinical MRI scans. It was recently demonstrated that cortical microinfarcts can be depicted with MRI scanners using high magnetic field strengths (7T). Based on this experience, a proportion of these lesions is also visible on lower resolution 3T MRI. These findings were corroborated with ex vivo imaging of post-mortem human brain tissue, accompanied by histopathological verification of possible cortical microinfarcts.
Here an ex vivo imaging protocol is presented, for the purpose of validating MR observed cerebral microvascular pathology with histological evaluation. Furthermore, guidelines are provided for the assessment of cortical microinfarcts on both in vivo 7T and 3T MR images. These guidelines provide researchers with a tool to rate cortical microinfarcts on in vivo images of larger patient samples, to further unravel their clinical relevance in cognitive decline and dementia, and establish these lesions as a novel biomarker of cerebral small vessel disease.

A high resolution ex vivo 7T MR imaging protocol is presented, to perform MR-guided histopathological validation of microvascular pathology in post-mortem human brain tissue. Furthermore, guidelines are provided for the assessment of cortical microinfarcts on in vivo 7T as well as 3T MR images.

Cerebral microinfarcts are frequent findings in the post-mortem human brain, and are related to cognitive decline and dementia. Due to their small sizes ...

Proximal Cadaveric Femur Preparation for Fracture Strength Testing and Quantitative CT-based Finite Element Analysis

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength. Because ex vivo biological tissues are prone to lose their mechanical properties over time, specimen preparation for experimental testing must be performed carefully to obtain reliable results that represent in vivo conditions. For that reason, we designed a protocol and a set of fixtures to prepare the femoral specimens such that their mechanical properties experienced minimal changes. The femora were kept in a frozen state except during preparation steps and mechanical testing. The relevant clinical measures of total hip and femoral neck bone mineral density (BMD) were obtained with a clinical dual X-ray absorptiometry (DXA) bone densitometer, and the 3D geometry and distribution of bone mineral were obtained using CT with a calibration phantom for quantitative estimations based on the greyscale values. Any possible bone disease, fracture, or the presence of implants or artifacts affecting the bone structure, was ruled out with X-ray scans. For preparation, all bones were carefully cleaned of excess soft tissue, and were cut and potted at the internal rotation angle of interest. A cutting fixture allowed the distal end of the bone to be cut off leaving the proximal femur at a desired length. To allow positioning of the femoral neck at prescribed angles during later CT scanning and mechanical testing, the proximal femoral shafts were potted in polymethylmethacrylate (PMMA) using a fixture designed specifically for desired orientations. The data collected from our experiments were then used for validation of quantitative computed tomography (QCT)-based finite element analysis (FEA), as described in a different protocol. In this manuscript, we present the protocol for the precise bone preparation for mechanical testing and subsequent QCT/FEA modeling. The current protocol was successfully applied to prepare about 200 cadaveric femora over a 6-year time period.

We present a robust protocol on how to carefully preserve and prepare cadaveric femora for fracture testing and quantitative computed tomography imaging. The method provides precise control over input conditions for the purpose of determining relationships between bone mineral density, fracture strength, and defining finite element model geometry and properties.

Cadaveric fracture testing is routinely used to understand factors that affect proximal femur strength.  Because ex vivo biological tissues are prone to ...

A Tissue Displacement-based Contusive Spinal Cord Injury Model in Mice

Producing a consistent and reproducible contusive spinal cord injury (SCI) is critical to minimizing behavioral and histological variabilities between experimental animals. Several contusive SCI models have been developed to produce injuries using different mechanisms. The severity of the SCI is based on the height that a given weight is dropped, the injury force, or the spinal cord displacement. In the current study, we introduce a novel mouse contusive SCI device, the Louisville Injury System Apparatus (LISA) impactor, which can create a displacement-based SCI with high injury velocity and accuracy. This system utilizes laser distance sensors combined with advanced software to produce graded and highly-reproducible injuries. We performed a contusive SCI at the 10th thoracic vertebral (T10) level in mice to demonstrate the step-by-step procedure. The model can also be applied to the cervical and lumbar spinal levels.

We introduce a tissue displacement-based contusive spinal cord injury model that can produce a consistent contusive spinal cord injury in adult mice.

Producing a consistent and reproducible contusive spinal cord injury (SCI) is critical to minimizing behavioral and histological variabilities between ...

Determining Glucose Metabolism Kinetics Using 18F-FDG Micro-PET/CT

This paper describes the use of 18F-FDG and micro-PET/CT imaging to determine in vivo glucose metabolism kinetics in mice (and is transferable to rats). Impaired uptake and metabolism of glucose in multiple organ systems due to insulin resistance is a hallmark of type 2 diabetes. The ability of this technique to extract an image-derived input function from the vena cava using an iterative deconvolution method eliminates the requirement of the collection of arterial blood samples. Fitting of tissue and vena cava time activity curves to a two-tissue, three compartment model permits the estimation of kinetic micro-parameters related to the 18F-FDG uptake from the plasma to the intracellular space, the rate of transport from intracellular space to plasma and the rate of 18F-FDG phosphorylation. This methodology allows for multiple measures of glucose uptake and metabolism kinetics in the context of longitudinal studies and also provides insights into the efficacy of therapeutic interventions.

Determining Glucose Metabolism Kinetics Using 18F-FDG Micro-PET/CT

This study describes a protocol that uses 18F-FDG and positron emission tomography/computed tomography (PET/CT) imaging, together with kinetic modelling, to quantify the in vivo, real-time uptake of 18F-FDG into tissues.

This paper describes the use of 18F-FDG and micro-PET/CT imaging to determine in vivo glucose metabolism kinetics in mice (and is transferable to rats). ...

Improved Method for the Establishment of an In Vitro Blood-Brain Barrier Model Based on Porcine Brain Endothelial Cells

The aim of this protocol presents an optimized procedure for the purification and cultivation of pBECs and to establish in vitro blood-brain barrier (BBB) models based on pBECs in mono-culture (MC), MC with astrocyte-conditioned medium (ACM), and non-contact co-culture (NCC) with astrocytes of porcine or rat origin. pBECs were isolated and cultured from fragments of capillaries from the brain cortices of domestic pigs 5-6 months old. These fragments were purified by careful removal of meninges, isolation and homogenization of grey matter, filtration, enzymatic digestion, and centrifugation. To further eliminate contaminating cells, the capillary fragments were cultured with puromycin-containing medium. When 60-95% confluent, pBECs growing from the capillary fragments were passaged to permeable membrane filter inserts and established in the models. To increase barrier tightness and BBB characteristic phenotype of pBECs, the cells were treated with the following differentiation factors: membrane permeant 8-CPT-cAMP (here abbreviated cAMP), hydrocortisone, and a phosphodiesterase inhibitor, RO-20-1724 (RO). The procedure was carried out over a period of 9-11 days, and when establishing the NCC model, the astrocytes were cultured 2-8 weeks in advance. Adherence to the described procedures in the protocol has allowed the establishment of endothelial layers with highly restricted paracellular permeability, with the NCC model showing an average transendothelial electrical resistance (TEER) of 1249 &#177; 80 &#937; cm2, and paracellular permeability (Papp) for Lucifer Yellow of 0.90 10-6 &#177; 0.13 10-6 cm sec-1 (mean &#177; SEM, n=55). Further evaluation of this pBEC phenotype showed good expression of the tight junctional proteins claudin 5, ZO-1, occludin and adherens junction protein p120 catenin. The model presented can be used for a range of studies of the BBB in health and disease and, with the highly restrictive paracellular permeability, this model is suitable for studies of transport and intracellular trafficking.

Improved Method for the Establishment of an In Vitro Blood-Brain Barrier Model Based on Porcine Brain Endothelial Cells

The aim of the protocol is to present an optimized procedure for the establishment of an in vitro blood-brain barrier (BBB) model based on primary porcine brain endothelial cells (pBECs). The model shows high reproducibility, high tightness, and is suitable for studies of transport and intracellular trafficking in drug discovery.

The aim of this protocol presents an optimized procedure for the purification and cultivation of pBECs and to establish in vitro blood-brain barrier (BBB) ...

Induction and Micro-CT Imaging of Cerebral Cavernous Malformations in Mouse Model

Mutations in the CCM1 (aka KRIT1), CCM2, or CCM3 (aka PDCD10) gene cause cerebral cavernous malformation (CCM) in humans. Mouse models of CCM disease have been established by tamoxifen induced deletion of Ccm genes in postnatal animals. These mouse models provide invaluable tools to investigate molecular mechanism and therapeutic approaches for CCM disease. An accurate and quantitative method to assess lesion burden and progression is essential to harness the full value of these animal models. Here, we demonstrate the induction of CCM disease in a mouse model and the use of the contrast enhanced X-ray micro computed tomography (micro-CT) method to measure CCM lesion burden in mouse brains. At postnatal day 1 (P1), we used 4-hydroxytamoxifen (4HT) to activate Cre recombinase activity from the Cdh5-CreErt2 transgene to cleave the floxed allele of Ccm2. CCM lesions in mouse brains were analyzed at P8. For micro-CT, iodine based Lugol&#39;s solution was used to enhance contrast in brain tissue. We have optimized the scan parameters and utilized a voxel dimension of 9.5 &#181;m, which lead to a minimum feature size of approximately 25 &#181;m. This resolution is sufficient to measure CCM lesion volume and number globally and accurately, and provide high-quality 3-D mapping of CCM lesions in mouse brains. This method enhances the value of the established mouse models to study the molecular basis and potential therapies for CCM and other cerebrovascular diseases.

This protocol demonstrates the induction of cerebral cavernous malformation disease in a mouse model and uses contrast enhanced micro computed tomography to measure lesion burden. This method enhances the value of established mouse models to study the molecular basis and potential therapies for cerebral cavernous malformation and other cerebrovascular diseases.

Mutations in the CCM1 (aka KRIT1), CCM2, or CCM3 (aka PDCD10) gene cause cerebral cavernous malformation (CCM) in humans. Mouse models of CCM disease have ...

Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT

Gastric cancer remains fourth in cancer incidence worldwide with a five-year survival of only 20%-30%. Peritoneal metastasis is the most frequent type of metastasis that accompanies unresectable gastric cancer and is a definitive determinant of prognosis. Preventing and controlling the development of peritoneal metastasis could play a role in helping to prolong the survival of gastric cancer patients. A non-invasive and efficient imaging technique will help us to identify the invasion and metastasis process of peritoneal metastasis and to monitor the changes in tumor nodules in response to treatments. This will enable us to obtain an accurate description of the development process and molecular mechanisms of gastric cancer. We have recently described experiment using dual energy CT (DECT) and positron emission tomography/computed tomography (PET/CT) platforms for the detection and monitoring of gastric tumor metastasis in nude mice models. We have shown that weekly continuous monitoring with DECT and PET/CT can identify dynamic changes in peritoneal metastasis. The sFRP1-overexpression in gastric cancer mice models showed positive radiological performance, a higher FDG uptake and increasing enhancement, and the SUVmax (standardized uptake value) of nodules demonstrated an obvious alteration trend in response to targeted therapy of TGF-&#946;1 inhibitor. In this article, we described the detailed non-invasive imaging procedures to conduct more complex research on gastric cancer peritoneal metastasis using animal models and provided representative imaging results. The use of non-invasive imaging techniques should enable us to better understand the mechanisms of tumorigenesis, monitor tumor growth, and evaluate the effect of therapeutic interventions for gastric cancer.

This protocol describes the value of dual energy CT and PET/CT imaging methods in tumor imaging and efficacy evaluation. This article demonstrates the research methods and results acquired by dual energy CT and PET/CT to evaluate the gene regulation and targeted treatment of gastric cancer peritoneal metastasis.

Gastric cancer remains fourth in cancer incidence worldwide with a five-year survival of only 20%-30%. Peritoneal metastasis is the most frequent type of ...