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://scholar.google.com/scholar?hl=en&safe=off&q=author%3aLanglois%2C+JA+%22+Traumatic+brain+injury+in+the+united+states%3A+emergency+department+visits%2C+hospitalizations%2C+and+deaths%22"> Traumatic brain injury in the united states: emergency department visits, hospitalizations, and deaths</a>. , Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Atlanta, GA. (2006).</li>
<li>Moore, E. E., Feliciano, D. V., Mattox, K. L. Trauma. , 5th, McGraw-Hill Professional. (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/pubmed?term=((Automated+Ventricular+Systems+Segmentation+in+Brain+CT+Images+by+Combining+Low-level+Segmentation+and+High-level+Template+Matching%5BTitle%5D)+AND+%22BMC+Medical+Informatics+and+Decision+Making%22%5BJournal%5D)">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, Suppl 1. (2009).</li>
<li>Segmentation of Ventricles in Brain CT Images Using Gaussian Mixture Model Method. Chen, W., Najarian, K. 2009 IEEE/ICME International Conference on Complex Medical Engineering (ICME2009), , 15-20 (2009).</li>
<li>Actual Midline Estimation from Brain CT Scan Using Multiple Regions Shape Matching. Chen, W., Ward, K. R., Najarian, K. International Conference on Pattern Recognition, , 2552-2555 (2010).</li>
<li>Intracranial Pressure Level Prediction in Traumatic Brain Injury by Extracting Features from Multiple Sources and Using Machine Learning Methods. Chen, W., Cockrell, C., Ward, K. R., Najarian, K. IEEE International Conference on Bioinformatics & Biomedicine, , 510-515 (2010).</li>
<li>Greenspan, H., Ruf, A., Goldberger, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Constrained+Gaussian+mixture+model+framework+for+automatic+segmentation+of+MR+brain+images%5BTitle%5D)+AND+%22IEEE+Trans.+Med.+Imaging%22%5BJournal%5D)">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/pubmed?term=((Shape+matching+and+object+recognition+using+shape+contexts%5BTitle%5D)+AND+%22IEEE+Trans.+Pattern+Anal.+Mach.+Intell%22%5BJournal%5D)">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/pubmed?term=((A+comparative+study+of+texture+measures+for+terrain+classification%5BTitle%5D)+AND+%22IEEE+Trans.+on+Syst.%2C+Man%2C+Cyber%22%5BJournal%5D)">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/pubmed?term=((Complex+wavelets+for+shift+invariant+analysis+and+filtering+of+signals%5BTitle%5D)+AND+%22Applied+and+Computational+Harmonic+Analysis%22%5BJournal%5D)">Complex wavelets for shift invariant analysis and filtering of signals.</a> Applied and Computational Harmonic Analysis. 10 (3), 234-253 (2002).</li>
<li> Yale: Rapid prototyping for complex data mining tasks. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T. KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, , 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 l...

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 assi...

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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 ...

automated-midline-shift-intracranial-pressure-estimation-based-on

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JoVE Journal

Medicine

42.5K Views.  Virginia Commonwealth University. Een geautomatiseerde middellijn shift schatting en intracraniële druk (ICP) pre-screening op basis van computertomografie (CT) beelden voor patiënten met traumatisch hersenletsel (TBI) wordt voorgesteld met behulp van beeldverwerking en machine learning technieken.

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

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

Neuroscience

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are exacerbated when significant brain atrophy and age-related white matter changes (e.g. Leukoaraiosis) are present. Lesion Explorer (LE) is an accurate and reliable neuroimaging pipeline specifically developed to address such issues commonly observed on MRI of Alzheimer's disease and normal elderly. The pipeline is a complex set of semi-automatic procedures which has been previously validated in a series of internal and external reliability tests1,2. However, LE's accuracy and reliability is highly dependent on properly trained manual operators to execute commands, identify distinct anatomical landmarks, and manually edit/verify various computer-generated segmentation outputs.
LE can be divided into 3 main components, each requiring a set of commands and manual operations: 1) Brain-Sizer, 2) SABRE, and 3) Lesion-Seg. Brain-Sizer's manual operations involve editing of the automatic skull-stripped total intracranial vault (TIV) extraction mask, designation of ventricular cerebrospinal fluid (vCSF), and removal of subtentorial structures. The SABRE component requires checking of image alignment along the anterior and posterior commissure (ACPC) plane, and identification of several anatomical landmarks required for regional parcellation. Finally, the Lesion-Seg component involves manual checking of the automatic lesion segmentation of subcortical hyperintensities (SH) for false positive errors.
While on-site training of the LE pipeline is preferable, readily available visual teaching tools with interactive training images are a viable alternative. Developed to ensure a high degree of accuracy and reliability, the following is a step-by-step, video-guided, standardized protocol for LE's manual procedures.

Lesion Explorer (LE) is a semi-automatic, image-processing pipeline developed to obtain regional brain tissue and subcortical hyperintensity lesion volumetrics from structural MRI of Alzheimer's disease and normal elderly. To ensure a high level of accuracy and reliability, the following is a video-guided, standardized protocol for LE's manual procedures.

Obtaining in vivo human brain tissue volumetrics from MRI is often complicated by various technical and biological issues. These challenges are ...

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

Behavior

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway.

We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality ...

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data remains difficult because segmentation in all volumetric frames is time-consuming. We aimed to analyze joint kinematics using a sequential 3D-3D registration technique to provide the kinematics of the moving bone with respect to the fixed bone semiautomatically using 4DCT DICOM data and existing software. Surface data of the source bones are reconstructed from 3DCT. The trimmed surface data are respectively matched with surface data from the first frame in 4DCT. These trimmed surfaces are sequentially matched until the last frame. These processes provide positional information for target bones in all frames of the 4DCT. Once the coordinate systems of the target bones are decided, translation and rotation angles between any two bones can be calculated. This 4DCT analysis offers advantages in kinematic analyses of complex structures such as carpal or tarsal bones. However, fast or large-scale motions cannot be traced because of motion artifacts.

We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data ...

Bioengineering

A Detailed Protocol for Physiological Parameters Acquisition and Analysis in Neurosurgical Critical Patients

Intracranial pressure (ICP) monitoring is now widely used in neurosurgical critical patients. Besides mean ICP value, the ICP derived parameters such as ICP waveform, amplitude of pulse (AMP), the correlation of ICP amplitude and ICP mean (RAP), pressure reactivity index (PRx), ICP and arterial blood pressure (ABP) wave amplitude correlation (IAAC), and so on, can reflect intracranial status, predict prognosis, and can also be used as guidance of proper treatment. However, most of the clinicians focus only on the mean ICP value while ignoring these parameters because of the limitations of the current devices. We have recently developed a multimodality monitoring system to address these drawbacks. This portable, user-friendly system will use a data collecting and storing device to continuously acquire patients&#39; physiological parameters first, i.e., ABP, ICP, and oxygen saturation, and then analyze these physiological parameters. We hope that the multimodality monitoring system will be accepted as a key measure to monitor physiological parameters, to analyze the current clinical status, and to predict the prognosis of the neurosurgical critical patients.

Recently, we have developed a portable multimodality monitoring system for the monitoring of various physiological parameters in neurosurgical critical patients. Detailed protocols on how to use this multimodality monitoring system are presented here.

Intracranial pressure (ICP) monitoring is now widely used in neurosurgical critical patients. Besides mean ICP value, the ICP derived parameters such as ...

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to ...

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

Intracranial pressure (ICP) monitoring is a cornerstone of the intensive care management of patients with severe acute brain injuries, including traumatic brain injury. While elevations in ICP are common, data regarding the measurement and treatment of these ICP elevations are conflicting. There is increasing recognition that changes in the balance between supply and demand of brain tissue are critically important and therefore the measurement of multiple modalities is required. Approaches are not standard, and therefore this article provides a description of a bedside, single burr hole approach to multimodality monitoring that allows the passage of probes designed to measure not only ICP but brain tissue oxygen, blood flow, and intracranial electroencephalography. Patient selection criteria, operative procedures, and practical considerations for securing probes during critical care are described. This method is readily performed, safe, secure, and flexible for the adoption of a variety of multimodality monitoring approaches aimed at detecting or preventing secondary brain injuries.

A method of recording multimodality monitoring signals in patients with severe brain injuries using a bedside, single burr hole technique is described.

Intracranial pressure (ICP) monitoring is a cornerstone of the intensive care management of patients with severe acute brain injuries, including traumatic ...

Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. The model can be used for planning and simulating critical steps of a surgical approach. Therefore, it is important that anatomical structures such as blood vessels inside a tumor can be printed to be colored not only on their surface, but throughout their whole volume. During simulation this allows for the removal of certain parts (e.g., with a high-speed drill) and revealing internally located structures of a different color. Thus, diagnostic information from various imaging modalities (e.g., CT, MRI) can be combined in a single compact and tangible object.
However, preparation and printing of such a fully colored anatomical model remains a difficult task. Therefore, a step-by-step guide is provided, demonstrating the fusion of different cross-sectional imaging data sets, segmentation of anatomical structures, and creation of a virtual model. In a second step the virtual model is printed with volumetrically colored anatomical structures using a plaster-based color 3D binder jetting technique. This method allows highly accurate reproduction of patient-specific anatomy as shown in a series of 3D-printed petrous apex chondrosarcomas. Furthermore, the models created can be cut and drilled, revealing internal structures that allow for simulation of surgical procedures.

The protocol describes the fabrication of fully colored three-dimensional prints of patient-specific, anatomical skull models to be used for surgical simulation. The crucial steps of combining different imaging modalities, image segmentation, three-dimensional model extraction, and production of the prints are explained.

Three-dimensional (3D) printing technologies offer the possibility of visualizing patient-specific pathologies in a physical model of correct dimensions. ...

Enhancing Neuroimaging Analysis in Brain Disorders

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for brain maturation, immune/ inflammatory regulation, and behavioral/cognitive functioning. However, current automated neuroimaging segmentation tools are poor at accurately and reliably segmenting the lateral ventricle choroid plexus. Furthermore, there is no existing tool that segments the choroid plexus located in the third and fourth ventricles of the brain. Thus, a protocol delineating how to segment the choroid plexus in the lateral, third, and fourth ventricle is needed to increase the reliability and replicability of studies examining the choroid plexus in neurodevelopmental and brain disorders. This protocol provides detailed steps to create separately labeled files in 3D Slicer for the choroid plexus based on DICOM or NIFTI images. The choroid plexus will be manually segmented using the axial, sagittal, and coronal planes of T1w images making sure to exclude voxels from gray or white matter structures bordering the ventricles. Windowing will be adjusted to assist in the localization of the choroid plexus and its anatomical boundaries. Methods for assessing accuracy and reliability will be demonstrated as part of this protocol. Gold standard segmentation of the choroid plexus using manual delineations can be used to develop better and more reliable automated segmentation tools that can be openly shared to elucidate changes in the choroid plexus across the lifespan and within various brain disorders.

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies 

Despite the crucial role of the choroid plexus in the brain, neuroimaging studies of this structure are scarce due to the lack of reliable automated segmentation tools. The present protocol aims to ensure gold-standard manual segmentation of the choroid plexus that can inform future neuroimaging studies.

The choroid plexus has been implicated in neurodevelopment and a range of brain disorders. Evidence demonstrates that the choroid plexus is critical for ...

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

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