Name: brain infarct segmentation and registration on mri or ct for lesion-symptom mapping
Uploaded: 2019-09-25T13:00:00
Description: Watch this Scientific Journal Video about Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping at JoVE.com

The work of Dr. Biesbroek is supported by a Young Talent Fellowship from the Brain Center Rudolf Magnus of the University Medical Center Utrecht. This work and the Meta VCI Map consortium are supported by Vici Grant 918.16.616 from ZonMw, The Netherlands, Organisation for Health Research and Development, to Geert Jan Biessels. The authors would like to thank Dr. Tanja C.W. Nijboer for sharing scans that were used in one of the figures.

This protocol follows the guidelines of our institutions human research ethics committee.
1. Collection of Scans and Clinical Data
<ol>
	<li>Collect brain CT or MRI scans of patients with ischemic stroke. Most scanners save the scans as DICOM (Digital Imaging and Communications in Medicine) files that can be copied to a hard disk or server. 
	NOTE: Scans from every scanner type, scan protocol, and MRI field strength can be used, as long as 1) the time window requirement...

LSM is a powerful tool to study the functional architecture of the human brain. A crucial step in any lesion-symptom mapping study is the preprocessing of imaging data, segmentation of the lesion and registration to a brain template. Here, we report a standardized pipeline for lesion segmentation and registration for the purpose of lesion-symptom mapping. This method can be performed with freely available image processing tools, can be used to process both CT and structural MRI scans, and covers the entire process of pre...

Lesion-symptom mapping (LSM), also called lesion-behavior mapping, is an important tool for studying the functional architecture of the human brain1. In lesion studies, brain function is inferred and localized by studying patients with acquired brain lesions. The first case studies linking neurological symptoms to specific brain locations performed in the nineteenth century&#160;already provided fundamental insights into the anatomical correlates of language and several other cognitive processes2. Yet, the neuroanatomical correlates of many aspects of cognition and&#160;other brain functions remained elusive. In the past decades, improved structural brain imaging methods and technical advances have enabled large-scale in vivo LSM studies with high spatial resolution (i.e., at the level of individual voxels or specific cortical/subcortical regions of interest)1,2. With these methodological advances, LSM has become an increasingly popular method in cognitive neuroscience and continues to offer new insights into the neuroanatomy of cognition and neurological symptoms3. A crucial step in any LSM study is the accurate segmentation of lesions and registration to a brain template. However, a comprehensive tutorial for the preprocessing of brain imaging data for the purpose of LSM is lacking.
Provided here is a complete tutorial for a standardized lesion segmentation and registration method. This method provides researchers with a pipeline for&#160;standardized brain image processing and an overview of potential pitfalls that must be avoided. The presented image processing pipeline was developed through international collaborations4 and is part of the framework of the recently founded Meta VCI map consortium, whose purpose is performing multicenter lesion-symptom mapping studies in vascular cognitive impairment &#60;www.metavcimap.org&#62;5. This method has been designed to process both CT and MRI scans from multiple vendors and heterogeneous scan protocols to allow combined processing of imaging datasets from different sources. The required RegLSM software and all other software needed for this protocol is freely available except for MATLAB, which requires a license. This tutorial focuses on the segmentation and registration of brain infarcts, but this image processing pipeline can also be used for other lesions, such as white matter hyperintensities6.
Prior to initiating an LSM study, a basic understanding of the general concepts and pitfalls is required. Several detailed guidelines and a hitchhiker&#39;s guide are available1,3,6. However, these reviews do not provide a detailed hands-on tutorial for the practical steps involved in gathering and converting brain scans to a proper format, segmenting the brain infarct, and registering the scans to a brain template. The present paper provides such a tutorial. General concepts of LSM are provided in the introduction with references for further reading on the subject.
General aim of lesion-symptom mapping studies
From the perspective of cognitive neuropsychology, brain injury can be used as a model condition to better understand the neuronal underpinnings of certain cognitive processes and to obtain a more complete picture of the cognitive architecture of the brain1. This is a classic approach in neuropsychology that was first applied in post-mortem studies in the nineteenth century by pioneers like Broca and Wernicke2. In the era of functional brain imaging, the lesion approach has remained a crucial tool in neuroscience because it provides proof that lesions in a specific brain region disrupt task performance, while functional imaging studies demonstrate brain regions that are activated during the task performance. As such, these approaches provide complementary information1.
From the perspective of clinical neurology, LSM studies can clarify the relationship between the lesion location and cognitive functioning in patients with acute symptomatic infarcts, white matter hyperintensities, lacunes, or other lesion types (e.g., tumors). Recent studies have shown that such lesions in strategic brain regions are more relevant in explaining cognitive performance than global lesion burden2,5,7,8. This approach has the potential to improve understanding of the pathophysiology of complex disorders (in this example, vascular cognitive impairment) and may provide opportunities for developing new diagnostic and prognostic tools or supporting treatment strategies2.
LSM also has applications beyond the field of cognition. In fact, any variable can be related to lesion location, including clinical symptoms, biomarkers, and functional outcome. For example, a recent study determined infarct locations that were predictive of functional outcome after ischemic stroke10.
Voxel-based versus region of interest-based lesion-symptom mapping
To perform lesion-symptom mapping, lesions need to be segmented and registered to a brain template. During the registration procedure, each patient&#39;s brain is spatially aligned (i.e., normalized or registered to a common template) to correct for differences in brain size, shape, and orientation so that each voxel in the lesion map represents the same anatomical structure for all patients7. In standard space, several types of analyses can be performed, which are briefly summarized here.
A crude lesion-subtraction analysis can be performed to show the difference in lesion distribution in patients with deficits compared to patients without deficits. The resulting subtraction map show regions that are more often damaged in patients with deficits and spared in patients without deficits1. Though a lesion-subtraction analysis can provide some insights into correlates of a specific function, it provides no statistical proof and is now mostly used when the sample size is too low to provide enough statistical power for voxel-based lesion-symptom mapping.
In voxel-based lesion-symptom mapping, an association between the presence of a lesion and cognitive performance is determined at the level of each individual voxel in the brain (Figure 1). The main advantage of this method is the high spatial resolution. Traditionally, these analyses have been performed in a mass-univariate approach, which warrants correction for multiple testing and introduces a spatial bias caused by inter-voxel correlations that are not taken into account1,10,11. Recently developed approaches that do take inter-voxel correlations into account (usually referred to as multivariate lesion-symptom mapping methods, such as Bayesian analysis13, support vector regression4,14, or other machine learning algorithms15) show promising results and appear to improve the sensitivity and specificity of findings from voxel-wise LSM analyses compared to traditional methods. Further improvement and validation of multivariate methods for voxel-wise LSM is an ongoing process. The best method choice for specific lesion-symptom mapping depends on many factors, including the distribution of lesions, outcome variable, and underlying statistical assumptions of the methods.
In the region of interest (ROI)-based lesion-symptom mapping, an association between the lesion burden within a specific brain region and cognitive performance is determined (see Figure 1 in Biesbroek et al.2 for an illustration). The main advantage of this method is that it considers the cumulative lesion burden within an anatomical structure, which in some cases may be more informative than a lesion in a single voxel. On the other hand, ROI-based analyses have limited power for detecting patterns that are only present in a subset of voxels in the region16. Traditionally, ROI-based lesion-symptom mapping is performed using logistic or linear regression. Recently, multivariate methods that deal better with collinearity have been introduced (e.g., Bayesian network analysis17, support vector regression4,18, or other machine learning algorithms19), which may improve the specificity of findings from lesion-symptom mapping studies.
Patient selection
In LSM studies, patients are usually selected based on a specific lesion type (e.g., brain infarcts or white matter hyperintensities) and the time interval between diagnosis and neuropsychological assessment (e.g., acute vs. chronic stroke). The optimal study design depends on the research question. For example, when studying the functional architecture of the human brain, acute stroke patients are ideally included because functional reorganization has not yet occurred in this stage, whereas chronic stroke patients should be included when studying the long-term effects of stroke on cognition. A detailed description of considerations and pitfalls in patient selection is provided elsewhere7.
Brain image preprocessing for the purpose of lesion-symptom mapping
Accurate lesion segmentation and registration to a common brain template are crucial steps in lesion-symptom mapping. Manual segmentation of lesions remains the gold standard for many lesion types, including infarcts7. Provided is a detailed tutorial on criteria for manual infarct segmentation on CT scans, diffusion weighted imaging (DWI), and fluid-attenuated inversion recovery (FLAIR) MRI sequences in both acute and chronic stages. The segmented infarcts (i.e., the 3D binary lesion maps) need to be registered before any across-subject analyses are performed. This protocol uses the registration method RegLSM, which was developed in a multicenter setting4. RegLSM applies linear and non-linear registration algorithms based on elastix20 for both CT and MRI, with an additional CT processing step specifically designed to enhance registration quality of CT scans21. Furthermore, RegLSM allows for using different target brain templates and an (optional) intermediate registration step to an age-specific CT/MRI template22. The possibility of processing both CT and MRI scans and its customizability regarding intermediate and target brain templates makes RegLSM a highly suitable image processing tool for LSM. The entire process of preparing and segmenting CT/MRI scans, registration to a brain template, and manual corrections (if required) are described in the next section.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59653/59653fig1.jpg" /> 
Figure 1: Schematic illustration of the concept of voxel-based lesion-symptom mapping. The upper part shows the brain image pre-processing steps consisting of segmenting the lesion (an acute infarct in this case) followed by registration to a brain template (the MNI-152 template in this case). Below, a part of the registered binary lesion map of the same patient is shown as a 3D grid, where each cube represents a voxel. Taken together with the lesion maps of 99 other patients, a lesion overlay map is generated. For each voxel, a statistical test is performed to determine the association between lesion status and cognitive performance. The chi-squared test shown here is just an example, any statistical test could be used. Typically, hundreds of thousands of voxels are tested throughout the brain, followed by a correction for multiple comparisons. <a href="https://www.jove.com/files/ftp_upload/59653/59653fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="0" cellpadding="0" cellspacing="0" style="width:611px;" width="609">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">dcm2niix</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td style="width:172px;">free download https://github.com/rordenlab/dcm2niix</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">ITK-SNAP</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download www.itksnap.org</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:223px;">MATLAB</td>
			<td style="width:94px;">MathWorks</td>
			<td style="width:123px;">N/A</td>
			<td>Version 2015a or higher</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">MRIcron</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download https://www.nitrc.org/projects/mricron</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">RegLSM</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download www.metavcimap.org/support/software-tools</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">SPM12b</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download https://www.fil.ion.ucl.ac.uk/spm/</td>
		</tr>
	</tbody>
</table>

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.

Exemplar cases of brain infarct segmentations on CT (Figure 3), DWI (Figure 5), and FLAIR (Figure 6) images, and subsequent registration to the MNI-152 template are provided here. The registration results shown in Figure 3B and Figure 5C were not entirely successful, as there was misalignment near the frontal horn of the ventricle. The regis...

Watch this Scientific Journal Video about Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping at JoVE.com

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

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 ...

Lesion-symptom mapping is a powerful tool to localize brain functions by studying patients with brain lesions. An important step involves image processing, including lesion segmentation and registration to standard space. This protocol describes a unified framework that works with all structural imaging modalities, providing a consistent output of lesions in standard space for use in lesion-symptom mapping studies.The lesions must be transformed to standard space to allow for comparisons across subjects. Each patient's brain must be spatially aligned to correct for differences in brain size and shape. While using this method, it helps to see how each step should be performed, rather than following written instructions alone.It also requires a good understanding of brain images, which makes a visual demonstration useful. This protocol describes how to perform the processing steps required for lesion-symptom mapping. Once the lesion maps are registered to standard space, lesion-symptom mapping can be performed on large groups of patients.When all the lesion maps are transformed to standard space, each voxel represents the same brain region across subjects. This allows for statistical analyses to be performed, for example, to test if the presence of a lesion in a particular voxel is associated with a cognitive deficit. Begin by collecting brain CT or MRI scans of patients with ischemic stroke.Most scanners have the scans as DICOM files that can be copied to a hard disk or server. Collect the clinical variables in a data file by making separate rows for each case and columns for each clinical variable. For infarct segmentation, include at least the date of stroke and the date of imaging or a variable that indicates the time interval between stroke and imaging.To convert the DICOM images to uncompressed NIfTI files, using the DICOM-to-NIfTI tool, type the command seen on-screen here in the command prompt, using the folder path to the DICOM files. An example of the command with the folder paths inserted can be seen here. This command will run the executable, convert the DICOM images in the selected folder, and save the NIfTI files in this folder.Finally, organize the NIfTI files in a convenient folder structure, with a subfolder for each case, before beginning infarct segmentation. First, assure that the scan was performed at least 24 hours after stroke symptom onset. Otherwise, the acute infarct will not be visible on CT or will only be partially visible, and the scan cannot be used for segmentation.Open the native CT using ITK-SNAP software by selecting File, then Open Main Image from the dropdown menu. Then click Browse, and select the file to open the scan. Now identify the infarct based on imaging characteristics.First, note that infarcts have a low signal compared to normal brain tissue. In the acute stage, large infarcts can cause mass effect, resulting in displacement of surrounding tissues, compression of ventricles, midline shift, and obliteration of sulci. There can be hemorrhagic transformation, which is visible as regions with high signal within the infarct.In the chronic stage, the infarct will consist of a hypodense, cavitated center, with a similar density as the cerebrospinal fluid, and a less hypodense rim, which represents damaged brain tissue. In case of large infarcts, there can be ex vacuo enlargement of adjacent sulci or ventricles. Segment the infarcted brain tissue using the Paintbrush Mode from the main toolbar, using left-click to draw and right-click to erase.Alternatively, use the Polygon Mode to place anchor points at the borders of the lesion, and hold the left mouse button while moving the mouse over the borders of the lesion. Once all points are connected, click accept to fill the delineated area. After finishing the segmentation, save it as a binary NIfTI file in the same folder as the scan by clicking Segmentation and Save Segmentation Image from the dropdown menu, then save the segmentation by giving it the exact same name as the segmented scan, with the extension of lesion.First, assure that the DWI scan was performed within seven days of stroke onset. Infarcts are visible on DWI within several hours after stroke onset, and their visibility on DWI gradually decreases after approximately seven days. Next, identify and annotate the infarcted brain tissue based on the high signal on DWI and low signal on the ADC map.Do not mistake a high diffusion signal near interfaces between air and either tissue or bone, which are a commonly observed artifact on DWI. For FLAIR imaging, first check that the scan was performed 48 hours after stroke symptom onset. In the hyperacute stage, the infarct is usually not visible, or the exact boundaries are unclear.Next, open the FLAIR image in ITK-SNAP, as well as the T1-weighted scan in a separate window for reference, if available. In the acute stage, the infarct is visible as a somewhat homogeneous hyperintense lesion, with or without swelling and mass effect. If available, use the DWI to differentiate between the acute infarct and chronic lesions, such as white matter hyperintensities.To register images, first download RegLSM, and use this tool to process CT scans or any kind of MRI sequence. The registration procedure can be seen in the figure shown here. Next, open MATLAB Version 2015a or higher, set the current folder to RegLSM, then enable SPM by typing addpath followed by the folder name for SPM.Next, type RegLSM to open the graphic interface. To perform the registration for a single case, select Test Mode in the Registration dropdown menu. Then use the Open Image button to select the segmented scan, the annotation, and optionally the T1, and select the desired registration scheme.Alternatively, Batch Mode can be used to register all cases in the selected folder. Ensure that RegLSM saved the resulting registration parameters, the registered scans, and the registered lesion map in the automatically generated subfolders. Now, review the registration results by selecting Check Results in the RegLSM interface, and browse to the main folder with these results.Scroll through the registered scan, and use the crosshair to check the alignment to the MNI152 template, paying attention to recognizable anatomical landmarks. Be sure to mark all failed registrations in a separate column in the previously created data file for subsequent manual correction. For any lesion maps that do need correction, open the MNI152 T1 template in ITK-SNAP, then select Open Segmentation from the Segmentation menu, and choose the registered lesion map, which will overlay onto the template.Also, open the registration results in a separate window for reference. Correct the registered lesion map in ITK-SNAP for any type of misalignment, using the brush function to add voxels with left-click or remove voxels with right-click. Finally, save the corrected lesion map as a NIfTI file in the same folder as the uncorrected lesion map, then save the segmentation by giving it the exact same name as the uncorrected lesion map, with the extension of corrected.Here, we see CT scans for a single patient. The initial scan cannot be used for segmentation because the infarct is not yet visible, even though the CT perfusion maps show ischemia. The CT on day six shows swelling of the infarcted brain tissue with slight midline shift and hemorrhagic transformation.The scan after four months shows brain tissue loss with ex vacuo enlargement of ventricles and nearby sulci. The result of registration of the CT on day six to the MNI152 template is seen here. The registration algorithm insufficiently compensated for the midline shift and compression of the left ventricle, which required a manual correction.After correction, the registered lesion map is an accurate representation of the infarct in native space and can be used for lesion-symptom mapping. Here, we see comparison of the registered DWI sequence and the corresponding registered infarct with the MNI152 template. Note the slight error at the head of the right caudate nucleus.This required a manual correction of the segmentation in standard space. Finally, this figure shows the results of the registration of the MRI FLAIR sequence on day three, which is adequate and requires no manual correction. This protocol covers the process of preparing imaging data for lesion-symptom mapping, including practical guidelines on how to perform accurate infarct segmentation and registration to a brain template.The lesion maps can be used in lesion-symptom mapping studies to localize a specific cognitive function or to use lesion location to predict cognitive outcome in stroke patients. This protocol accelerates and harmonizes the workflow for lesion-symptom mapping studies, enabling researchers to perform large multicenter studies including hundreds to thousands of subjects.

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