The authors would like to acknowledge the following funding agencies that supported this work. SLM is funded through The Arthritis Society (STAR-18-0189) and Canadian Institutes of Health Research Planning and Dissemination Grant. JJT holds a CIHR Fellowship award.

All methods in this protocol follow the guidelines set by the Conjoint Health Research Ethics Board at the University of Calgary (REB19-0387).
1. Install 3D Slicer&#160;15 and bone analysis modules
<ol>
	<li>Download the installation file for a stable release of 3D Slicer that is relevant to the operating system used from&#160;https://download.slicer.org/.</li>
	<li>Run the downloaded installation file and follow the instructions provided on the wizard.</li>
	<li>Once the installation is complete, proceed with installing the bone analysis modules.
	<ol>
		<li>Download the bone analysis modules from&#160;https://doi.org/10.5281/zenodo.7943007 as a compressed zip file and extract the compressed folder. Please note the directory where the extracted folder is located.</li>
		<li>Launch 3D Slicer. Load the modules onto 3D Slicer by clicking on Edit on the top left corner of the 3D Slicer window. Click on Edit &#62; Application Settings to open a new window.</li>
		<li>Click on Modules which is a tab located on the left on the recently opened Settings window. Add the paths to the bone analysis modules under Additional module paths: (Figure 2).
		<ol>
			<li>To do this, drag and drop the following list of folders onto the box located under Additional module paths:. These folders are located inside of the downloaded folder in step 1.3.1: AutoMask, CorticalBreakDetection, ErosionComparison, ErosionVolume, FileConverter, ImageRegistration, Training. Settings window should resemble Figure 2.</li>
			<li>Press OK on the bottom right of the Settings window. A restart is required to confirm the installation of the modules. Do this by closing 3D Slicer and relaunching it. 
			&#8203;NOTE: Loading the modules is only done once per installation of 3D Slicer. Upon further updates to the bone analysis GitHub repository, a simple terminal (or command line) git pull command at the directory containing the earlier BAM download will automatically update all the modules. Again, alternatively, the repository can be downloaded, and old modules exchanged with the new ones manually.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65802/65802fig02.jpg" /> 
Figure 2: Example of settings window after adding bone analysis modules to an installation of 3D Slicer. The image shows a screenshot of the settings window with the modules highlighted in the red box. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Training module
<ol>
	<li>Launch the BAM-Training module.
	<ol>
		<li>Click on the Drop-down Menu located on 3D Slicer&#39;s toolbar. Find bone analysis modules and hover the mouse above it (Figure 3). Click on Training.</li>
	</ol>
	</li>
	<li>Load files in the module.
	<ol>
		<li>Upon launching the BAM training module, all the required files (greyscale images, masks, reference erosion segmentations) will be loaded automatically by clicking Proceed, assuming the BAM github repository was downloaded as mentioned above.</li>
	</ol>
	</li>
	<li>Choose a scan type for the files.
	<ol>
		<li>Choose one by hovering over the drop-down menu labeled Input Volume: Select a Volume, this represents the master grey scale image.</li>
		<li>Choose the mask (i.e., the file that identifies the volume within the periosteal surface) in the drop-down menu labeled Input Mask: Select a Volume. Make sure this mask corresponds to the input volume above by verifying the measurement ID and the MCP joint is the same in both selections.</li>
		<li>If this is the first time running the training module on this image with this launch of 3D Slicer, then create a new output segmentation at the drop-down menu labeled Output Erosions: Select a Segmentation. To do this, click the drop-down menu and select Create New Segmentation. This will create a new output segmentation node labeled after the input mask label + _ER. To give the output a different label, select Create New Segmentation As&#8230; instead and input the desired label. 
		NOTE: In 3D Slicer and this document, mask and segmentation identifies the volume inside the bone&#39;s periosteal surface. The mask is visualized as a binary image, while the segmentation refers to the visualization of the binary image overlaid with the grey scale image. These distinctions are made by 3D Slicer. An example is illustrated in Figure 4.</li>
	</ol>
	</li>
	<li>Place seed points as described below.
	<ol>
		<li>To begin, create a new seed point list to add seed points. To do this, click on the drop-down menu labeled Seed Points: None and create a new list by selecting Create new Point List. Again, the default labeling standard is input image label + _SEEDS. To provide your own label, select Create New Point List&#8230;.</li>
		<li>Scroll through the slices and identify erosion sites by placing a seed point in the region of interest. Press the Red Dot-Blue Drop button shown in Figure 5A to add a new seed point.
		<ol>
			<li>Place the seed point as deep (inwards into trabecular bone) into the erosion volume as possible. Make sure the seed point is placed on the darkest region of the volume.</li>
			<li>To change the seed point size, modify the percentage size at the text box labeled Seed Point Size:. Other fields in the seed points table, such as bone and cortical interruption, are for the user&#39;s records and do not affect the erosion computation algorithm.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Obtain feedback as described below.
	<ol>
		<li>Once the seed points have been placed. Press the button labeled Get Erosions, highlighted in Figure 5B, to run the erosion measurement algorithm on the given inputs. Upon completion of the erosion measurements, the module will provide feedback on the seed point placement. Each seed point&#39;s location is compared with the location of reference erosions to match seed points to the erosion it is trying to measure.</li>
		<li>Obtain reference erosions by calculating the erosion volume using seed points placed by rheumatologists with training, extensive publication records and more than 10 years of experience using HR-pQCT imaging and erosion analysis (SF and CF).</li>
	</ol>
	</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65802/65802fig03.jpg" /> 
Figure 3: 3D Slicer&#39;s dropdown menu. The dropdown menu to find the bone analysis modules and select the training module. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65802/65802fig04.jpg" /> 
Figure 4: Identification of the volume inside the bone&#39;s periosteal surface. (A) Example of a mask. The mask is visualized as a binary image. (B) Example of a segmentation. The segmentation refers to the visualization of the binary image overlaid with the grey scale image. These distinctions are made by 3D Slicer. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65802/65802fig05.jpg" /> 
Figure 5: Example screen shot of training module within 3D Slicer. (A) Click to add new seed points. (B) Click to compute erosion volumes. (C) Click to import images. (D) Click to reveal seed points placed by experts. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Image acquisition and export to use in erosion analysis tool
<ol>
	<li>Acquire HR-pQCT images using a first or second-generation HR-pQCT scanner. A commercial scanner was used to acquire the images for this study (see Table of Materials). 
	NOTE: The images used in this study are of the 2nd and 3rd metacarpophalangeal joints and were acquired using the protocol described in Barnabe et al.8, however images of any joints with erosions are compatible with BAM.</li>
	<li>Inspect images for motion artifact16,17. Do not use images with motion scores &#62; 3 for erosion analysis.</li>
	<li>Export AIM (proprietary image format) or digital imaging and communications in medicine (DICOM) files of each joint to a local disk using file transfer protocol (FTP). Files of individual joints may be generated using the joint space width (JSW) analysis pipeline provided by the manufacturer18. Rename files from default number format for easier use.</li>
</ol>
4. File conversion and bone mask generation
NOTE: Depending on the image format, follow step 4.1 for AIM (HR-pQCT proprietary image format), MHA (ITK MetaImage format), nii (NIfTI - Neuroimaging Informatics Technology Initiative), NRRD (Nearly Raw Raster Data) images or step 4.2 for DICOM images.
<ol>
	<li>To import images with any of the following image file formats: MHA, nii, AIM or NRRD follow the steps described below.
	<ol>
		<li>Click on the button labeled DATA located at the top left corner of the 3D Slicer window (Figure 5C).</li>
		<li>To add image files, click on Choose File(s) to Add, locate and add the images.</li>
		<li>To add an entire directory of images, click on Choose Directory to Add, locate and add the directory. This will load all images in that directory.</li>
		<li>If an image mask needs to be imported as a 3D Slicer segmentation for any reason, first convert to an NRRD or nii file. This conversion can be done automatically, see step 4.4.1 for details.</li>
	</ol>
	</li>
	<li>Import DICOM file(s) in 3D Slicer as described below.
	<ol>
		<li>Click on the button labeled DCM located at the top left corner of the 3D Slicer window.</li>
		<li>Click on Import DICOM Files, locate and add the directory containing the DICOM files (extension is .dcm).</li>
		<li>Click on the button labeled Load located at the right side of the window.</li>
	</ol>
	</li>
	<li>Get image mask using Step 2 - Automatic Mask in the BAM - Automatic Mask module.
	<ol>
		<li>Click on the drop-down menu located on 3D Slicer&#39;s toolbar. Find Bone Analysis Modules and hover the mouse cursor above it. Click on Automatic Mask.</li>
		<li>Under the tab Step 2 - Automatic Mask, select an input volume using the drop-down menu labeled Input Volume:. This is the input scan.</li>
		<li>Create a new output at the drop-down menu labeled output segmentation: and select Create New LabelMapVolume. This will create a new output node labeled after the input mask label + _MASK. To give the output a different label, instead select Create New LabelMapVolume as&#8230; and input the desired label.</li>
		<li>Input the number of bones to be masked at the text box with that label. Select Ormir in the drop-down menu labeled Algorithm for the optimal segmentation for this analysis19. 
		NOTE: Other options for generating these masks are available and may be added in the future.</li>
		<li>Click on Get Mask. This will run the algorithm (~2-3 min) and output the result at the same directory of the input image. It will also save a separate mask for each bone if the image had multiple bones.</li>
	</ol>
	</li>
	<li>Perform manual correction of bone mask using Step 3 - Manual Correction in the BAM module. Often the mask generated is not accurate. Perform manual correction to add, delete, or edit specific components of the segmentations.
	<ol>
		<li>To edit a mask that was generated through other means or generated in a previous run of 3D Slicer, use this module to load these masks to 3D Slicer from a file. The file extension can be any of the following, MHA, nii, NRRD, AIM.
		<ol>
			<li>Copy the images to the LOAD_MASKS directory located in the BAM folder downloaded in step 1.3.</li>
			<li>Go back to 3D Slicer and press the button labeled Load in the manual correction stage.</li>
		</ol>
		</li>
		<li>Select the segmentation to be corrected in the drop-down menu labeled Mask To Be Corrected:&#34;.</li>
		<li>Select the original gray-scale image belonging to this erosion segmentation in the drop-down menu labeled Master Volume:. Press Initialize.</li>
		<li>Each segmentation must have its own entry in the table below. Select the segmentation to be corrected based on the color of the segmentation.</li>
		<li>To add to a segmentation, click on the second button on the first row. This uses the paint function. Make additions to the volumes by drawing on the images (hold left mouse button down and move the mouse).</li>
		<li>To remove part of the segmentation, click on the button below the table labeled Erase Between Slices. This is the erase function and works like the paint function but erases instead.</li>
		<li>Draw additions as needed on roughly every 10-25 slices, but make sure to include the first slice and last slice where an addition was needed.</li>
		<li>If the paint function was used, the changes can be interpolated by clicking on the first button of the fifth row labeled Fill in Between Slices function. Click on the buttons Initialize &#62; Apply.</li>
		<li>If the erase function was used, simply click on the button below the table labeled Apply Erase. Do not use both paint and erase functions at the same time. Apply one function first then apply the other.</li>
		<li>After the edits are completed, press Apply.</li>
	</ol>
	</li>
</ol>
5. Identification of erosions
<ol>
	<li>Use Step 4 -&#160;Erosions in the BAM - Erosion Volume&#160;module for identification of erosion. The erosion volume module is the tool responsible for identifying and measuring erosions within a scan. 
	NOTE: This module is the focus of the training tool detailed above and has a nearly identical workflow. The differences are that the computed erosions are not compared against expertly annotated ones, exporting erosion statistics and manual correction of the volumes after their identification is possible here.</li>
	<li>Place seed points and get erosion as done in step 2.4. In the end no feedback will be provided.</li>
	<li>Manual Correction
	<ol>
		<li>If the size and shape of the automatically detected erosion volumes are unsatisfactory, edit these under the tabs labeled Step 5 - Manual Correction and Export Segmentation. Follow steps outlined in step 4.4. However, there is no option to load external erosion volumes. Do not press Apply after finishing the corrections as the changes have already been saved.</li>
	</ol>
	</li>
</ol>
6. Erosion Statistics
<ol>
	<li>Export the computed data to a spreadsheet file (CSV format) using the tab labeled Step 6 - Statistics.</li>
	<li>Provide the erosion volume computed in Step 4 and optionally corrected in Step 5 under the drop-down menu labeled Input Erosions.</li>
	<li>Provide the greyscale image under the drop-down menu labeled Master Volume. Provide the voxel width, in mm, of the image in the text box. Press Get Statistics.</li>
	<li>The spreadsheet file has been generated in the directory called EROSIONS_OUTPUT_DATA located in the BAM folder downloaded in step 1.3. See Table 1 for an example of the output table.</li>
</ol>

Identification of Bone Erosions in HR-pQCT Images in Rheumatoid Arthritis

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L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+links+between+joint+damage+and+disability+in+rheumatoid+arthritis.">The links between joint damage and disability in rheumatoid arthritis.</a> Rheumatology. 39 (2), 122-132 (2000).</li><li>van Nies, J. A. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+processes+underlying+the+predictive+value+of+baseline+erosions+for+future+radiological+damage+in+early+rheumatoid+arthritis.">Evaluating processes underlying the predictive value of baseline erosions for future radiological damage in early rheumatoid arthritis.</a> Annals of Rheumatic Diseases. 74 (5), 883-889 (2015).</li><li>Versteeg, G. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Early+radiological+progression+remains+associated+with+long-term+joint+damage+in+real-world+rheumatoid+arthritis+patients+treated+to+the+target+of+remission.">Early radiological progression remains associated with long-term joint damage in real-world rheumatoid arthritis patients treated to the target of remission.</a> Scandinavian Journal of Rheumatology. , (2021).</li><li>Brunet, S. 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All authors have no conflict of interest to report.

This training tool provides an opportunity to learn to identify erosions using the bone analysis module. Further use of this erosion analysis tool beyond training requires access to good quality images, with little or no motion artifact. The HR-pQCT erosion definition based on the literature describes anatomical features associated with pathological erosions that can be reported with reasonable reproducibility11,20. However, this definition does not account for common anatomical locations of vascular channels, potentially resulting in their misclassification as bone erosions10.
The critical steps in this protocol are the generation of the bone mask, the placement of the seed points, and the generation of the erosion volume. While automated methods to generate the masks and erosion volume are implemented, the masks often require manual correction to ensure satisfactory results. A comprehensive description of the tools available to perform the manual corrections is provided. The placement of seed points is guided by the training examples provided by the BAM-Training module.
Based on the data utilized to date, this protocol provides suggestions for troubleshooting when the erosion analysis module does not produce the expected results. In future work, access to additional training data will be provided. A previous study showed that the erosion volumes assessed with this method are comparable with existing methods14,21,22. Provision of training data will allow comparison to newer erosion analysis tools as they are developed23.
The training tool introduced here primarily aids with erosion identification; however, the method is currently limited by the lack of a consensus on defining the extent of an erosion in the trabecular bone. Nonetheless, the BAM modules are open source, thus, as future definitions of erosion extent change, other researchers have access to modify the modules to meet their needs.
As the use of HR-pQCT in rheumatological research expands, the training tool provides inexperienced users with guidance on identifying pathological cortical interruptions on HR-pQCT images for erosion analysis. This tool will be applicable to researchers regardless of the method selected for erosion analysis. While completely automated erosion identification is desirable to improve reproducibility and speed of analysis, large reference/benchmark datasets with accurate annotations are required to train machine learning models. As an open-source tool, this module provides an opportunity to collectively develop large, annotated datasets for future use in machine learning. The use of this training tool will enable more researchers to include erosion analysis in their HR-pQCT research.

Bone erosions occur when inflammation causes localized bone loss at the cortical bone surface. These erosions extend into the underlying trabecular bone region. They are a pathological feature of several forms of inflammatory arthritis, including rheumatoid arthritis (RA)1. Erosion presence and size are associated with poor outcomes, patient function, and disease progression2,3,4,5. While plain radiography remains the clinical standard for erosion assessment, high-resolution peripheral quantitative computed tomography (HR-pQCT) provides 3D images and superior sensitivity and specificity for erosion detection6,7. For inflammatory arthritis, such as RA, HR-pQCT is commonly performed on the 2nd and 3rd metacarpophalangeal joints - the most affected joints of the hand8. Because HR-pQCT images have high spatial resolution, physiological interruptions in the cortical surface are also observed in healthy individuals without RA9. These cortical interruptions are often associated with vascular channels or nutrient foramen passing through the bone10. Thus, the challenge is to distinguish cortical interruptions associated with a disease process (i.e., pathological erosions) from non-pathological features.
The consensus definition of a pathological bone erosion was published by the Study grouP for xtrEme Computed Tomography in Rheumatoid Arthritis (SPECTRA) as the presence of a definite interruption in the cortical layer of the bone that extends over at least two consecutive slices and is detectable in two or more perpendicular planes11. Further, the interruption must be non-linear in shape and accompanied by a loss in the trabecular region. Visual examples of cortical interruptions that do and do not meet the criteria of erosions are shown in Klose-Jensen et al.12.
However, not all cortical interruptions that meet the above criteria are classified as erosions. Interruptions are sometimes caused by physiological processes such as vascular channels (Figure 1). These can be identified and differentiated from erosions due to their predictable anatomical locations, parallel and straight margins and sub-millimetric size13. Cysts are another form of cortical interruption that is not considered to be an erosion. They often have a rounded trabecular structure with a clear cystic wall 13. In contrast to the sharp edges and open trabecular structure displayed by erosions. However, it is possible for erosions to form within cystic sites, making it ambiguous to delineate the volume of bone loss caused by the erosions and not the cysts. While resolving this ambiguity with further criteria is not the purpose of this study, there is a need to provide comprehensive examples of pathological erosion and physiological cortical interruptions.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65802/65802fig01.jpg" /> 
Figure 1: Example of cortical interruptions that were not caused solely by erosions. (A) A drawing illustrating the common location of vascular channels at the base of the metacarpal head. Examples of vascular channels in (B) coronal, (C) sagittal, (D) and (E) axial planes. (F) Example of a cortical interruption caused by a cyst. (G) Example of a void volume within trabecular region of the bone involving both cysts and erosions. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Despite the challenges in erosion identification, there are currently no training tools in place to provide less experienced users with guidance on interpreting HR-pQCT images for erosion analysis. Recently, an open-source module for erosion analysis called bone analysis module (BAM) - Erosion Volume was developed, implemented as an extension to an open-source image processing software to enable erosion visualization and volumetric analyses14. The protocol presented here describes the use of a training module added to BAM (BAM - Training), which compares a users&#39; erosion identification attempts by comparing the erosion identification with erosions annotated by expert rheumatologists. This training tool provides users with feedback on erosion identification in order to guide improvements in erosion analysis. Software installation instructions are provided in step 1. For new data acquisition, see steps 3 - 5.3. For training module use only, see step 2.

<table><tbody><tr><td>3DSlicer</td><td>Open Source</td><td>N/A</td><td>Download at https://www.slicer.org/</td></tr><tr><td>BAM Erosion Analysis Modules</td><td>Open Source</td><td>N/A</td><td>Version used in manuscript: download at https://doi.org/10.5281/zenodo.7943007</td></tr><tr><td>XtremeCTII</td><td>Scanco Medical&amp;nbsp;</td><td>N/A</td><td></td></tr></tbody></table>

erosion identification in metacarpophalangeal joints in rheumatoid arthritis using high-resolution peripheral quantitative computed tomography

Bone erosions are a pathological feature of several forms of inflammatory arthritis including rheumatoid arthritis (RA). The increased presence and size of erosions are associated with poor outcomes, joint function, and disease progression. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides unparalleled in vivo visualization of bone erosions. However, at this resolution, discontinuities in the cortical shell (cortical breaks) that are associated with normal physiological processes and pathology are also visible. The Study grouP for xtrEme Computed Tomography in Rheumatoid Arthritis previously used a consensus process to develop a definition of pathological erosion in HR-pQCT: a cortical break detected in at least two consecutive slices, in at least two perpendicular planes, non-linear in shape, with underlying trabecular bone loss. However, despite the availability of a consensus definition, erosion identification is a demanding task with challenges in inter-rater variability. The purpose of this work is to introduce a training tool to provide users with guidance on identifying pathological cortical breaks on HR-pQCT images for erosion analysis. The protocol presented here uses a custom-built module (Bone Analysis Module (BAM) - Training), implemented as an extension to an open-source image processing software (3D Slicer). Using this module, users can practice identifying erosions and compare their results to erosions annotated by expert rheumatologists.

Using the training tool, users can practice identifying erosion sites while receiving feedback on their results. This feedback loop can improve the user&#39;s ability to identify erosions and potentially use the BAM modules to identify erosions on their own images. Feedback after seed point placement is based on the following criteria. 1) If the number of seed points placed does not match the number of reference erosions, the user is then prompted to delete or add the appropriate number of seed points. 2) If the seed point location cannot be matched with a reference erosion, feedback stating that no erosion exists at that seed point&#39;s location is displayed. 3) If a seed point is matched with a reference pathological/physiological cortical interruption such as a cyst or a vascular channel, the user is informed about the type of cortical interruption they attempted to identify as an erosion and is asked to remove the seed point. 4) If the location of the seed point overlaps a reference erosion, the algorithm may still not detect the erosion. This may occur when the seed point has not been centered in the erosion. In these cases, the user is prompted to adjust the position of the seed point. 5) If a seed point is placed too far from any erosion, the user is informed of their incorrect placement and encouraged to try again. 6) When a seed point location matches the reference erosion, a prompt is displayed informing the user of their successful attempt to identify the erosion at that specific seed point.
The following section illustrates examples of how the module works based on different inputs. Correct and incorrect inputs will be demonstrated in the following examples. Figure 6A shows the seed point location which is located within the erosion. Only one erosion exists within this image, therefore computing the erosions with the seed point will yield the expected results. Figure 6B shows the prompt displayed to users when their attempt to identify the erosions matches the expertly annotated image. The module also displays the results as segmentations on the grey-scale image (Figure 6C). If the user placed a seed point at a location without an erosion, such as Figure 7A, the module would display an error prompt (Figure 7B) stating that no erosion exists at this location and suggests that the user relocate/remove the seed points.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65802/65802fig06.jpg" /> 
Figure 6: Example of correct erosion identification. (A) Example of a user correctly placing a seed point within the erosion site. (B) Example of feedback prompt when all erosions were identified correctly. (C) Example of displayed erosion segmentation when an erosion is computed correctly. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65802/65802fig07.jpg" /> 
Figure 7:&#160;Example of incorrect erosion identification. (A) Example of a seed point placed where no erosion exists. (B) Example of an error prompt when a seed point is placed at a location that has no erosion. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The locations of all cysts and vascular channels on the training images provided have been identified by an expert. Therefore, it is possible to detect when a user attempts to incorrectly identify a cyst or a vascular channel. Figure 8A illustrates an attempt to identify a cyst by placing a seed point on it. Figure 8B is the subsequent displayed error prompt.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65802/65802fig08.jpg" /> 
Figure 8:&#160;Example of cyst identification. (A) Example of a seed point placed on a cyst. (B) Example of an error prompt when a seed point is placed on a cyst. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The module will also inform the user if they have the right amount of seed points. If the user placed an incorrect number of seed points, the module would inform the user of the exact amount of seed points that are missing or extra to identify all erosions on the image. The module also gives feedback for each placed seed point. Therefore, the user knows what actions to take for each individual seed point. Figure 9 demonstrates an example where a user only placed one seed point when two were expected.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65802/65802fig09.jpg" /> 
Figure 9: Example of erosions computed while missing one seed point. The example demonstrates an example where the user only placed one seed point when two were expected. <a href="https://www.jove.com/files/ftp_upload/65802/65802fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
If a user has trouble finding any or all the erosions, they have an option to reveal the expertly annotated locations by pressing a button labeled Reveal Correct Seed Points (Figure 5D). When pressed, this button will load the correct seed points on to the current 3D Slicer window.
In summary, this shows that the software module can assess the correctness of the user&#39;s attempt to identify erosions in the select images by comparing the computed erosion with expertly annotated erosions. In addition, the module provides feedback based on each user-placed seed point to guide them towards the expected seed point location and input parameters.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Scan ID</td>
			<td>Cortical Interruption</td>
			<td>Bone</td>
			<td>Label</td>
			<td>Centroid Location</td>
			<td>Volume (mm3)</td>
			<td>Surface Area (mm2)</td>
			<td>Roundness</td>
			<td>Number of voxels (voxels)</td>
		</tr>
		<tr>
			<td>3_Training.nii</td>
			<td>Erosion</td>
			<td>Metacarpal</td>
			<td>SEEDS_28-1</td>
			<td>210, 108, 242</td>
			<td>3.321668853</td>
			<td>14.46818378</td>
			<td>0.74411491</td>
			<td>14853</td>
		</tr>
		<tr>
			<td>3_Training.nii</td>
			<td>Erosion</td>
			<td>Metacarpal</td>
			<td>SEEDS_28-3</td>
			<td>179, 100, 241</td>
			<td>1.100739562</td>
			<td>7.121231239</td>
			<td>0.7239659</td>
			<td>4922</td>
		</tr>
	</tbody>
</table>
Table 1: Example of a generated output file (csv format) describing computed erosions and their statistics.

Watch this Scientific Journal Video about Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis at JoVE.com

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis 

Bone erosions are an important pathological feature of rheumatoid arthritis. The purpose of this work is to introduce a training tool to provide users with guidance on identifying pathological cortical breaks on high resolution peripheral quantitative computed tomography images for erosion analysis.

Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography

Bone erosions are a pathological feature of several forms of inflammatory arthritis including rheumatoid arthritis (RA). The increased presence and size ...

erosion-identification-metacarpophalangeal-joints-rheumatoid

Research

JoVE Journal

Medicine

2.0K Views.  University of Calgary. Een van onze doelen als onderdeel van de studiegroep voor extreme computertomografie bij RA of spectra, is het ontwikkelen van betere beeldanalyse-instrumenten om botschade bij reumatoïde artritis te begrijpen. Door perifere kwantitatieve computertomografie met hoge resolutie, of kortweg HRPQCT, toe te passen op reumatoïde artritis, hebben onderzoekers aangetoond dat deze modaliteit veel gevoeliger is dan standaard röntgenfoto's om boterosies te detecteren en hebben ze botveranderingen ged...

Video: Auteur in de schijnwerpers: onderzoek naar reumatoïde artritis verbeteren door middel van HR-pQCT-beeldvormingsanalyse 

Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are associated with obesity, though the precise underlying mechanisms that contribute to the disease are currently being delineated.2,3 Several small animal models of obesity have been developed and are employed in a variety of studies.4 A critical component to these experiments involves the collection of regional and/or total animal fat content data under varied conditions.
Traditional experimental methods available for measuring fat content in small animal models of obesity include invasive (e.g. ex vivo measurement of fat deposits) and non-invasive (e.g. Dual Energy X-ray Absorptiometry (DEXA), or Magnetic Resonance (MR)) protocols, each of which presents relative trade-offs. Current invasive methods for measuring fat content may provide details for organ and region specific fat distribution, but sacrificing the subjects will preclude longitudinal assessments. Conversely, current non-invasive strategies provide limited details for organ and region specific fat distribution, but enable valuable longitudinal assessment. With the advent of dedicated small animal X-ray computed tomography (CT) systems and customized analytical procedures, both organ and region specific analysis of fat distribution and longitudinal profiling may be possible. Recent reports have validated the use of CT for in vivo longitudinal imaging of adiposity in living mice.5,6 Here we provide a modified method that allows for fat/total volume measurement, analysis and visualization utilizing the Carestream Molecular Imaging Albira CT system in conjunction with PMOD and Volview software packages.

Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.

Obesity is associated with increased morbidity and mortality as well as reduced metrics in quality of life.1 Both environmental and genetic factors are ...

Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT)

Conventional non-invasive imaging modalities of atherosclerosis such as coronary artery calcium (CAC)1 and carotid intimal medial thickness (C-IMT)2 provide information about the burden of disease. However, despite multiple validation studies of CAC3-5, and C-IMT2,6, these modalities do not accurately assess plaque characteristics7,8, and the composition and inflammatory state of the plaque determine its stability and, therefore, the risk of clinical events9-13.
[18F]-2-fluoro-2-deoxy-D-glucose (FDG) imaging using positron-emission tomography (PET)/computed tomography (CT) has been extensively studied in oncologic metabolism14,15. Studies using animal models and immunohistochemistry in humans show that FDG-PET/CT is exquisitely sensitive for detecting macrophage activity16, an important source of cellular inflammation in vessel walls. More recently, we17,18 and others have shown that FDG-PET/CT enables highly precise, novel measurements of inflammatory activity of activity of atherosclerotic plaques in large and medium-sized arteries9,16,19,20. FDG-PET/CT studies have many advantages over other imaging modalities: 1) high contrast resolution; 2) quantification of plaque volume and metabolic activity allowing for multi-modal atherosclerotic plaque quantification; 3) dynamic, real-time, in vivo imaging; 4) minimal operator dependence. Finally, vascular inflammation detected by FDG-PET/CT has been shown to predict cardiovascular (CV) events independent of traditional risk factors21,22 and is also highly associated with overall burden of atherosclerosis23. Plaque activity by FDG-PET/CT is modulated by known beneficial CV interventions such as short term (12 week) statin therapy24 as well as longer term therapeutic lifestyle changes (16 months)25.
The current methodology for quantification of FDG uptake in atherosclerotic plaque involves measurement of the standardized uptake value (SUV) of an artery of interest and of the venous blood pool in order to calculate a target to background ratio (TBR), which is calculated by dividing the arterial SUV by the venous blood pool SUV. This method has shown to represent a stable, reproducible phenotype over time, has a high sensitivity for detection of vascular inflammation, and also has high inter-and intra-reader reliability26. Here we present our methodology for patient preparation, image acquisition, and quantification of atherosclerotic plaque activity and vascular inflammation using SUV, TBR, and a global parameter called the metabolic volumetric product (MVP). These approaches may be applied to assess vascular inflammation in various study samples of interest in a consistent fashion as we have shown in several prior publications.9,20,27,28

Quantification of Atherosclerotic Plaque Activity and Vascular Inflammation using [18-F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography FDG-PET/CT

There is great need to identify atherosclerosis non-invasively, and here we demonstrate how FDG-PET/CT can be used to detect and quantify atherosclerotic plaque activity and vascular inflammation.

Conventional non-invasive imaging modalities of atherosclerosis such as coronary artery calcium (CAC)1 and carotid intimal medial thickness (C-IMT)2 ...

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Small animal fluorescence molecular imaging (FMI) can be a powerful tool for preclinical drug discovery and development studies1. However, light absorption by tissue chromophores (e.g., hemoglobin, water, lipids, melanin) typically limits optical signal propagation through thicknesses larger than a few millimeters2. Compared to other visible wavelengths, tissue absorption for red and near-infrared (near-IR) light absorption dramatically decreases and non-elastic scattering becomes the dominant light-tissue interaction mechanism. The relatively recent development of fluorescent agents that absorb and emit light in the near-IR range (600-1000 nm), has driven the development of imaging systems and light propagation models that can achieve whole body three-dimensional imaging in small animals3.
Despite great strides in this area, the ill-posed nature of diffuse fluorescence tomography remains a significant problem for the stability, contrast recovery and spatial resolution of image reconstruction techniques and the optimal approach to FMI in small animals has yet to be agreed on. The majority of research groups have invested in charge-coupled device (CCD)-based systems that provide abundant tissue-sampling but suboptimal sensitivity4-9, while our group and a few others10-13 have pursued systems based on very high sensitivity detectors, that at this time allow dense tissue sampling to be achieved only at the cost of low imaging throughput. Here we demonstrate the methodology for applying single-photon detection technology in a fluorescence tomography system to localize a cancerous brain lesion in a mouse model.
The fluorescence tomography (FT) system employed single photon counting using photomultiplier tubes (PMT) and information-rich time-domain light detection in a non-contact conformation11. This provides a simultaneous collection of transmitted excitation and emission light, and includes automatic fluorescence excitation exposure control14, laser referencing, and co-registration with a small animal computed tomography (microCT) system15. A nude mouse model was used for imaging. The animal was inoculated orthotopically with a human glioma cell line (U251) in the left cerebral hemisphere and imaged 2 weeks later. The tumor was made to fluoresce by injecting a fluorescent tracer, IRDye 800CW-EGF (LI-COR Biosciences, Lincoln, NE) targeted to epidermal growth factor receptor, a cell membrane protein known to be overexpressed in the U251 tumor line and many other cancers18. A second, untargeted fluorescent tracer, Alexa Fluor 647 (Life Technologies, Grand Island, NY) was also injected to account for non-receptor mediated effects on the uptake of the targeted tracers to provide a means of quantifying tracer binding and receptor availability/density27. A CT-guided, time-domain algorithm was used to reconstruct the location of both fluorescent tracers (i.e., the location of the tumor) in the mouse brain and their ability to localize the tumor was verified by contrast-enhanced magnetic resonance imaging.
Though demonstrated for fluorescence imaging in a glioma mouse model, the methodology presented in this video can be extended to different tumor models in various small animal models potentially up to the size of a rat17.

Diffuse fluorescence tomography offers a relatively low-cost and potentially high-throughout approach to preclinical in vivo tumor imaging. The methodology of optical data collection, calibration, and image reconstruction is presented for a computed tomography-guided non-contact time-domain system using fluorescent targeting of the tumor biomarker epidermal growth factor receptor in a mouse glioma model.

Small animal fluorescence molecular imaging (FMI) can be a powerful tool for preclinical drug discovery and development studies1. However, light ...

In vivo Macrophage Imaging Using MR Targeted Contrast Agent for Longitudinal Evaluation of Septic Arthritis

Macrophages are key-cells in the initiation, the development and the regulation of the inflammatory response to bacterial infection. Macrophages are intensively and increasingly recruited in septic joints from the early phases of infection and the infiltration is supposed to regress once efficient removal of the pathogens is obtained. The ability to identify in vivo macrophage activity in an infected joint can therefore provide two main applications: early detection of acute synovitis and monitoring of therapy.
In vivo noninvasive detection of macrophages can be performed with magnetic resonance imaging using iron nanoparticles such as ultrasmall superparamagnetic iron oxide (USPIO). After intravascular or intraarticular administration, USPIO are specifically phagocytized by activated macrophages, and, due to their magnetic properties, induce signal changes in tissues presenting macrophage infiltration. A quantitative evaluation of the infiltrate is feasible, as the area with signal loss (number of dark pixels) observed on gradient echo MR images after particles injection is correlated with the amount of iron within the tissue and therefore reflects the number of USPIO-loaded cells.
We present here a protocol to perform macrophage imaging using USPIO-enhanced MR imaging in an animal model of septic arthritis, allowing an initial and longitudinal in vivo noninvasive evaluation of macrophages infiltration and an assessment of therapy action.

In vivo Macrophage Imaging Using MR Targeted Contrast Agent for Longitudinal Evaluation of Septic Arthritis

We demonstrate how to perform macrophage MR imaging using ultrasmall superparamagnetic contrast agent (USPIO) in septic arthritis, allowing an initial and longitudinal in vivo non-invasive evaluation of macrophages infiltration and an assessment of therapy efficacy.

Macrophages are key-cells in the initiation, the development and the regulation of the inflammatory response to bacterial infection. Macrophages are ...

Application of Atomic Force Microscopy to Detect Early Osteoarthritis

Biomechanical properties of cells and tissues not only regulate their shape and function but are also crucial for maintaining their vitality. Changes in elasticity can propagate or trigger the onset of major diseases like cancer or osteoarthritis (OA). Atomic force microscopy (AFM) has emerged as a strong tool to qualitatively and quantitatively characterize the biomechanical properties of specific biological target structures on a microscopic scale, measuring forces in a range from as small as the piconewton to the micronewton. Biomechanical properties are of special importance in musculoskeletal tissues, which are subjected to high levels of strain. OA as a degenerative disease of the cartilage results in the disruption of the pericellular matrix (PCM) and the spatial rearrangement of the chondrocytes embedded in their extracellular matrix (ECM). Disruption in PCM and ECM has been associated with changes in the biomechanical properties of cartilage. In the present study we used AFM to quantify these changes in relation to the specific spatial pattern changes of the chondrocytes. With each pattern change, significant changes in elasticity were observed for both the PCM and ECM. Measuring the local elasticity thus allows for drawing direct conclusions about the degree of local tissue degeneration in OA.

We present a method to investigate early osteoarthritic changes at the cellular level in articular cartilage by using atomic force microscopy (AFM).

Biomechanical properties of cells and tissues not only regulate their shape and function but are also crucial for maintaining their vitality. Changes in ...

Bioengineering

Biplanar Videoradiography to Study the Wrist and Distal Radioulnar Joints

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, and the effects of treatments. Measurement systems that use skin surface markers to infer skeletal motion have provided important insight into normal and pathological kinematics, however, accurate arthrokinematics cannot be attained using these systems, especially during dynamic activities. In the past two decades, biplanar videoradiography (BVR) systems have enabled many researchers to directly study the skeletal kinematics of the joints during activities of daily living. To implement BVR systems for the distal upper extremity, videoradiographs of the distal radius and the hand are acquired from two calibrated X-ray sources while a subject performs a designated task. Three-dimensional (3D) rigid-body positions are computed from the videoradiographs via a best-fit registrations of 3D model projections onto&#160;to each BVR view. The 3D models are density-based image volumes of the specific bone derived from independently acquired computed-tomography data. Utilizing graphics processor units and high-performance computing systems, this model-based tracking approach is shown to be fast and accurate in evaluating the wrist and distal radioulnar joint biomechanics. In this study, we first summarized the previous studies that have established the submillimeter and subdegree agreement of BVR with an in vitro&#160;optical motion capture system in evaluating the wrist and distal radioulnar joint kinematics. Furthermore, we used BVR to compute the center of rotation behavior of the wrist joint, to evaluate the articulation pattern of the components of the implant upon one another, and to assess the dynamic change of ulnar variance during pronosupination of the forearm. In the future, carpal bones may be captured in greater detail with the addition of flat panel X-ray detectors, more X-ray sources (i.e., multiplanar videoradiography), or advanced computer vision algorithms.

Biplanar videoradiography (BVR) is an advanced imaging technique for understanding the three-dimensional movement of skeletal bones and implants. Combining density-based image volumes and videoradiographs of the distal upper extremity, BVR is used to study the in vivo motion of the wrist and distal radioulnar joint, as well as joint arthroplasties.

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, ...

Addressing Practical Issues in Atomic Force Microscopy-Based Micro-Indentation on Human Articular Cartilage Explants

Without a doubt, atomic force microscopy (AFM) is currently one of the most powerful and useful techniques to assess micro and even nano-cues in the biological field. However, as with any other microscopic approach, methodological challenges can arise. In particular, the characteristics of the sample, sample preparation, type of instrument, and indentation probe can lead to unwanted artifacts. In this protocol, we exemplify these emerging issues on healthy as well as osteoarthritic articular cartilage explants. To this end, we first show via a step-by-step approach how to generate, grade, and visually classify ex vivo articular cartilage discs according to different stages of degeneration by means of large 2D mosaic fluorescence imaging of the whole tissue explants. The major strength of the ex vivo model is that it comprises aged, native, human cartilage that allows the investigation of osteoarthritis-related changes from early onset to progression. In addition, common pitfalls in tissue preparation, as well as the actual AFM procedure together with the subsequent data analysis, are also presented. We show how basic but crucial steps such as sample preparation and processing, topographic sample characteristics caused by advanced degeneration, and sample-tip interaction can impact data acquisition. We also subject to scrutiny the most common problems in AFM and describe, where possible, how to overcome them. Knowledge of these limitations is of the utmost importance for correct data acquisition, interpretation, and, ultimately, the embedding of findings into a broad scientific context.

We present a step-by-step approach to identify and address the most common problems associated with atomic force microscopy micro-indentations. We exemplify the emerging problems on native human articular cartilage explants characterized by various degrees of osteoarthritis-driven degeneration.

Without a doubt, atomic force microscopy (AFM) is currently one of the most powerful and useful techniques to assess micro and even nano-cues in the ...

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

One of the most prevalent joint disorders in the United States, osteoarthritis (OA) is characterized by progressive degeneration of articular cartilage, primarily in the hip and knee joints, which results in significant impacts on patient mobility and quality of life. To date, there are no existing curative therapies for OA able to slow down or inhibit cartilage degeneration. Presently, there is an extensive body of ongoing research to understand OA pathology and discover novel therapeutic approaches or agents that can efficiently slow down, stop, or even reverse OA. Thus, it is crucial to have a quantitative and reproducible approach to accurately evaluate OA-associated pathological changes in the joint cartilage, synovium, and subchondral bone. Currently, OA severity and progression are primarily assessed using the Osteoarthritis Research Society International (OARSI) or Mankin scoring systems. In spite of the importance of these scoring systems, they are semiquantitative and can be influenced by user subjectivity. More importantly, they fail to accurately evaluate subtle, yet important, changes in the cartilage during the early disease states or early treatment phases. The protocol we describe here uses a computerized and semiautomated histomorphometric software system to establish a standardized, rigorous, and reproducible quantitative methodology for the evaluation of joint changes in OA. This protocol presents a powerful addition to the existing systems and allows for more efficient detection of pathological changes in the joint.

The current protocol establishes a rigorous and reproducible method for quantification of morphological joint changes that accompany osteoarthritis. Application of this protocol can be valuable in monitoring disease progression and evaluating therapeutic interventions in osteoarthritis.

One of the most prevalent joint disorders in the United States, osteoarthritis (OA) is characterized by progressive degeneration of articular cartilage, ...

Software-Assisted Quantitative Measurement of Osteoarthritic Subchondral Bone Thickness

Subchondral bone thickening and sclerosis are the major hallmarks of osteoarthritis (OA), both in animal models and in humans. Currently, the severity of the histologic subchondral bone thickening is mostly determined by visual estimation based semi-quantitative grading systems. This article presents a reproducible and easily executed protocol to quantitatively measure subchondral bone thickness in a mouse model of knee OA induced by destabilization of the medial meniscus (DMM). This protocol utilized ImageJ software to quantify subchondral bone thickness on histologic images after defining a region of interest in the medial femoral condyle and the medical tibial plateau where subchondral bone thickening usually occurs in DMM-induced knee OA. Histologic images from knee joints with a sham procedure were used as controls. Statistical analysis indicated that the newly developed quantitative subchondral bone measurement system was highly reproducible with low intra- and inter-observer variabilities. The results suggest that the new protocol is more sensitive to subtle or mild subchondral bone thickening than the widely used visual grading systems. This protocol is suitable for detecting both early and progressing osteoarthritic subchondral bone changes and for assessing in vivo efficacy of OA treatments in concert with OA cartilage grading.

This methodology article presents a software-assisted quantitative measurement protocol to quantify histologic subchondral bone thickness in murine osteoarthritic knee joints and normal knee joints as controls. This protocol is highly sensitive to subtle thickening and is suitable for detecting early osteoarthritic subchondral bone changes.

Subchondral bone thickening and sclerosis are the major hallmarks of osteoarthritis (OA), both in animal models and in humans. Currently, the severity of ...

Destabilization of the Medial Meniscus and Cartilage Scratch Murine Model of Accelerated Osteoarthritis

Osteoarthritis is the most prevalent musculoskeletal disease in people over 45, leading to an increasing economic and societal cost. Animal models are used to mimic many aspects of the disease. The present protocol describes the destabilization and cartilage scratch model (DCS) of post-traumatic osteoarthritis. Based on the widely used destabilization of the medial meniscus (DMM) model, DCS introduces three scratches on the cartilage surface. The current article highlights the steps to destabilize the knee by transecting the medial meniscotibial ligament followed by three intentional superficial scratches on the articular cartilage. The possible analysis methods by dynamic weight-bearing, microcomputed tomography, and histology are also demonstrated. While the DCS model is not recommended for studies that focus on the effect of osteoarthritis on the cartilage, it enables the study of osteoarthritis development in a shorter time window, with special focus on (1) osteophyte formation, (2) osteoarthritic and injury pain, and (3) the effect of cartilage damage in the whole joint.

The present protocol describes the controlled microblade scratches on the surface of the articular cartilage after destabilizing the mouse knee by cutting the medial miniscotibial ligament. This animal model presents an accelerated form of osteoarthritis (OA) suitable for studying osteophyte formation, osteosclerosis, and early-stage pain.

Osteoarthritis is the most prevalent musculoskeletal disease in people over 45, leading to an increasing economic and societal cost. Animal models are ...

Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography

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