Name: identifying coronary artery calcification on non-gated computed tomography scans
Uploaded: 2018-08-28T13:30:00.000+00:00
Duration: 4 min 40 s
Description: Watch this Scientific Journal Video about Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans at JoVE.com

This work was supported by the National Institutes of Health [1TL1TR001997-01, 2016-2017].

This protocol follows guidelines set forth by the Institutional Review Board and human subject research protocol of the University of Kentucky.
1. Opening the Image Viewer
<ol>
	<li>Open the image viewer used at the institution where the research is being conducted. Double-click on the desktop icon to open the viewer.</li>
	<li>Log in using an institutional username and password.</li>
</ol>
2. Identifying the Appropriate Patient
<ol>
	<li>Click on the Study List icon in the toolbar.</li>
	<li>Under the Search Criteria drop-down list, choose the option labeled With Patient ID Equal To.</li>
	<li>Enter the patient&#39;s hospital identification number.</li>
	<li>Under Modalities, click on All Modalities to unselect all the imaging modalities.
	<ol>
		<li>Click on CT to select this modality.</li>
	</ol>
	</li>
	<li>Under Body Regions, leave the default to All Body Regions.</li>
	<li>Then, click with the mouse on Find.</li>
</ol>
3. Identifying the Optimal Study
<ol>
	<li>Click on Performed on to organize the list by the date of study.</li>
	<li>Then, click on the study of interest. 
	NOTE: The optimal study is a CT chest (either with or without contrast). When multiple studies are available, use the CT scan that can visualize the entire coronary tree closest to the index time point (for retrospective data analysis) or the most recent CT scan (for clinical purposes).</li>
</ol>
4. Identifying Optimal Image Series
<ol>
	<li>Click with the mouse on the tile icon in the top-right corner of the screen and highlight a single tile. Click to make the screen a single pane.</li>
	<li>Hover over the series icon on the top row of images to identify the series that has a 3 mm slice thickness (or the closest to 3 mm).</li>
	<li>Click and hold the left mouse button, drag this icon to the center of the viewing screen, and release the left mouse button.</li>
	<li>Use the center mouse scroll bar (or, alternatively, hold the left mouse button and drag to the right) to scroll through images and ensure an adequate visualization of the coronary tree.</li>
</ol>
5. Optimizing the Images to Highlight Calcification
<ol>
	<li>Scroll through the images until an image where one of the coronary arteries is visualized.</li>
	<li>Right-click and select the Window/Level option.</li>
	<li>Click on Interactive W/L.</li>
	<li>As a starting point, type in 500 in the W (window) field.</li>
	<li>As a starting point, type in 150 in the L (level) field. 
	NOTE: The goal of adjusting the window and level settings is to optimize the contrast between epicardial fat [usually the lowest Hounsfield unit (HU) in the cardiac silhouette], cardiac chambers, and calcification or metallic structures (usually the highest HU). CT scans with contrast that use lower kV often require the highest level (often &#62; 250 HU) and the largest window (often &#62; 1,000 HU). For &#34;low-dose&#34; CT scans (low mAs) without contrast, would use a slightly lower level (0 - 150 HU).</li>
	<li>Manually adjust the window by holding down the left mouse button on the horizontal slide bar and moving it right and left (moving the scroll bar to the right increases the window).</li>
	<li>Manually adjust the level by holding down the left mouse button on the vertical slide bar and moving it up and down (moving the scroll bar up increases the level). 
	NOTE: The goal is to adjust the window and level to achieve the following: fat, including epicardial fat, should be dark gray to black; myocardium should be a slightly lighter gray; and calcium and metal should be white.</li>
	<li>Click on Close to close the window and level box and begin viewing the images.</li>
</ol>
6. Identifying Coronary Artery Calcification
<ol>
	<li>Use the center scroll ball on the mouse to scroll up and down the series of images, looking at one coronary at a time.</li>
	<li>Mark (on a separate document, spreadsheet, etc.) whether coronary artery calcification is present or absent in each of the four major epicardial coronary arteries (Figure 1). 
	NOTE: CAC is deemed as present in the left anterior descending artery (LAD), left circumflex artery (LCx), or right coronary artery (RCA) if it is seen in the vessel itself or in its branches.</li>
</ol>
7. Techniques for Identifying Subtle Areas of Calcification
<ol>
	<li>Identify an area of questionable coronary artery calcification.</li>
	<li>Right-click on the screen to bring up the menu.</li>
	<li>Click on Annotate.</li>
	<li>Then, click on Elliptical ROI.</li>
	<li>Click and hold down the left mouse button on the area of calcification and move it down and to the right to create a circle or ellipse large enough to cover the area of calcification. 
	&#8203;NOTE: Make sure the region of interest (ROI) is large enough to cover the entire area of potential calcification and some epicardial fat, but small enough to not include other chambers (especially those with contrast). The software will then provide the minimum, maximum, and average HU in the area without the region of interest.
	<ol>
		<li>Click and hold the left mouse button in the center of the region of interest to move it if necessary.</li>
		<li>Click and hold the left mouse button on the corners of the region of interest to adjust the size if necessary.</li>
	</ol>
	</li>
	<li>Repeat steps 7.5 - 7.5.2 to create another region of interest over the sternum, the bright bony structure at the top of the screen.</li>
	<li>Repeat steps 7.5 - 7.5.2 to create another region of interest over the ascending aorta.</li>
	<li>Compare the maximum HU in the area of potential calcification to the maximum HU in the ascending aorta and the sternum. 
	NOTE: Classify an area as coronary artery calcification if it is more than 2 standard deviations away from the maximum HU in the ascending aorta. Coronary artery calcification should have a maximum HU closer to the maximum HU in the sternum than the maximum HU in the ascending aorta (Figure 2).</li>
</ol>
8. Distinguishing Coronary Artery Calcification from Other Sources of Calcification
<ol>
	<li>To open the post-processing software, left click on Windows&#39; start button and then click on the post-processing software. Now, log in using an institutional username and password.</li>
	<li>To open the study and series, type in either the Patient ID or the Patient Name in the appropriate field in the search options at the top right of the screen. Then, uncheck Date 1.
	<ol>
		<li>Now, click on Update Study List and then perform a single click on the desired study from the results list at the top left of the screen.</li>
		<li>In the Series List below, click on the series that has the 3 mm slice thickness in the label.</li>
	</ol>
	</li>
	<li>Click and hold the center mouse scroll bar on one of the images and move the mouse up to zoom in so the arteries can be visualized well.</li>
	<li>Click and hold the left mouse button on the center of each of the crosshairs to move them over the center of the area of calcification in question.</li>
	<li>Click and hold the left mouse button on the marker on the crosshairs to be able to rotate the other two images. Continue to watch the other two images until the adjacent structure of interest is well visualized. 
	NOTE: The 3 areas that are most often confused for coronary artery calcification include aortic wall calcification as RCA or left main artery (LM) calcification, mitral annular calcification (mistaken for LCx calcification) or tricuspid annular calcification (mistaken for RCA calcification), and pericardial calcification. Coronary arteries are surrounded by epicardial fat, whereas these other adjacent structures are not.</li>
</ol>

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American Journal of Roentgenology. 190, 923-928 (2008).</li><li>Xie, X., 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=Validation+and+prognosis+of+coronary+artery+calcium+scoring+in+non-triggered+thoracic+computed+tomography:+systematic+review+and+meta-analysis.">Validation and prognosis of coronary artery calcium scoring in non-triggered thoracic computed tomography: systematic review and meta-analysis.</a> Circulation: Cardiovascular Imaging. 6, 514-521 (2013).</li><li>Itani, Y., 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=Coronary+artery+calcification+detected+by+a+mobile+helical+computed+tomography+unit+and+future+cardiovascular+death:+4-year+follow-up+of+6120+asymptomatic+Japanese.">Coronary artery calcification detected by a mobile helical computed tomography unit and future cardiovascular death: 4-year follow-up of 6120 asymptomatic Japanese.</a> Heart and Vessels. 19, 161-163 (2004).</li><li>Hughes-Austin, J. M., 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=Relationship+of+coronary+calcium+on+standard+chest+CT+scans+with+mortality.">Relationship of coronary calcium on standard chest CT scans with mortality.</a> JACC: Cardiovascular Imaging. 9, 152-159 (2016).</li><li>Shemesh, J., 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=Ordinal+scoring+of+coronary+artery+calcifications+on+low-dose+CT+scans+of+the+chest+is+predictive+of+death+from+cardiovascular+disease.">Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease.</a> Radiology. 257, 541-548 (2010).</li><li>Sarwar, 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=Diagnostic+and+prognostic+value+of+absence+of+coronary+artery+calcification.">Diagnostic and prognostic value of absence of coronary artery calcification.</a> JACC: Cardiovascular Imaging. 2, 675-688 (2009).</li><li>Gupta, V. 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=Coronary+artery+calcification+predicts+cardiovascular+complications+after+sepsis.">Coronary artery calcification predicts cardiovascular complications after sepsis.</a> Journal of Critical Care. 44, 261-266 (2017).</li><li>Takx, R. 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=Automated+coronary+artery+calcification+scoring+in+non-gated+chest+CT:+agreement+and+reliability.">Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability.</a> PLoS One. 9 (3), 91239 (2014).</li><li>Hecht, H. S., 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=2016+SCCT/STR+guidelines+for+coronary+artery+calcium+scoring+of+noncontrast+noncardiac+chest+CT+scans:+A+report+of+the+Society+of+Cardiovascular+Computed+Tomography+and+Society+of+Thoracic+Radiology.">2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: A report of the Society of Cardiovascular Computed Tomography and Society of Thoracic Radiology.</a> Journal of Cardiovascular Computed Tomography. 11 (1), 74-84 (2016).</li><li>Erbel, R., 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=Progression+of+coronary+artery+calcification+seems+to+be+inevitable,+but+predictable+-+results+of+the+Heinz+Nixdorf+recall+(HNR)+study.">Progression of coronary artery calcification seems to be inevitable, but predictable - results of the Heinz Nixdorf recall (HNR) study.</a> European Heart Journal. 35 (42), 2960-2971 (2014).</li><li>Blaha, M. J., 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=Improving+the+CAC+score+by+addition+of+regional+measures+of+calcium+distribution.">Improving the CAC score by addition of regional measures of calcium distribution.</a> JACC: Cardiovascular Imaging. 9, 1407-1416 (2016).</li></ol>

The authors have nothing to disclose.

The identification of CAC is an extremely powerful prognostic tool with an ever-growing body of literature supporting its use in many different clinical scenarios. The majority of the literature is focused on gated cardiac CT scans for the identification of CAC, but there is robust evidence of both the correlation of CAC on non-gated CT scans, as well as the prognostic ability of this finding. Given the CT scan utilization in the United States, as well as the ever-growing concerns about radiation exposure, the ability to extract CAC information from CT scans already acquired seems to offer additional value (i.e., improved quality at minimal to no additional cost). This will continue to be important in the evolving healthcare environment. To do this meaningfully and reliably, standardized approaches to identifying CAC on non-gated CT scans are necessary, from a research perspective but also for the translation to clinical application.
Optimizing the sequence identification and performing an accurate window/leveling of the grayscale are the most critical steps of the described methodology. Maintaining an optimal slice thickness, radiation exposure (kV and mAs), and post-processing to mimic the well-validated gated cardiac CT scans allows for the best correlation. When possible, studies that maintain a 2 - 3 mm slice thickness and 120 kV are ideal to allow for the optimal identification of CAC17. Given that the goal of the methodology is to identify CAC in many different types of CT protocols, appropriate window and leveling is essential, especially in studies that are not acquired using the above protocols. Lowering kV is important to reduce radiation exposure at the expense of signal-to-noise. The impact of kV on window and leveling is dependent on whether it is a contrasted study or not. The higher the contrast concentration in the coronary arteries, the higher the level and the larger the window will need to be. This effect is augmented when lower kV is administered. Given that body habitus and reconstruction protocols may influence this, subtle adjustments will likely need to be made on a case-by-case basis. As a consistent reference, the optimal window and leveling is one that makes epicardial fat appear dark gray to black, soft tissue gray, and calcium very light gray to white.
After an optimal sequence identification and appropriate window and leveling, the next stage that warrants focus is differentiating CAC from other sources of calcification in the cardiac silhouette. This can be challenging in studies with a significant cardiac and respiratory motion artifact. The use of multi-planar reconstruction can help to identify CAC (usually seen within the epicardial fat) versus annular calcification (seen in the myocardium itself), pericardial calcification (seen outside of the epicardial fat) and aortic root/aortic valve calcification (seen in the aortic wall). On rare occasion, a severe cardiac and respiratory motion artifact degrades the image sufficiently to prevent differentiation, and these studies should be removed from any analysis.
Given the variance in patients, as well as in acquisition techniques, there is always the need for potential troubleshooting. In addition to patient-specific modifications in window and level, there are potential issues with identifying subtle areas of calcification and discriminating between coronary calcification and non-coronary calcification. Subtle areas of calcification can be difficult to identify, especially with contrast-enhanced studies. Using region of interest tools on any image post-processing software can help to compare HU in areas of calcification to HU in areas of contrast, as well as to HU in other areas of calcification (such as bone). Subtle areas of coronary calcification are likely to have similar HU as bone and should generally be higher than the HU of areas of contrast. Multi-planar reconstruction helps to distinguish coronary calcification (seen in the epicardial coronary arteries that sit in the epicardial fat) from other sources of calcification in the cardiac silhouette. Mitral annular calcification, aortic wall calcification, and pericardial calcification can all be seen independent of coronary artery calcification. Given their location in the mitral valve annulus, the aortic wall, and in the pericardium, respectively, the use of multi-planar reconstruction can help to reliably differentiate these from coronary artery calcification.
Given that the negative prognostic value of CAC is its more powerful asset, the simple presence or absence of CAC provides significant value in cardiovascular risk assessment. This proposed methodology does allow for a standardized approach to this. It also does allow for the identification of single-vessel versus multi-vessel CAD, which in gated CT scans has also been shown to have prognostic significance. However, this protocol limits the quantification of CAC, largely due to concerns about inter-and intra-observer reproducibility, especially amongst less experienced readers. Dedicated cardiac CT scans allow for more validated quantification and may help to provide a tiered risk model for cardiovascular events based on the Agatston score. However, this requires dedicated cardiac CT scans, local expertise, and dedicated post-processing software, with its associated costs and radiation exposure. Requiring gated cardiac CT scans also requires a prospective analysis for most conditions, and the application of CAC in certain disease states may not be validated enough to warrant this. Furthermore, in the current healthcare delivery model with its emphasis on value, the ability to identify CAC on CT scans already acquired has significant appeal for clinical translation. Hopefully, this methodology for identifying CAC in non-gated CT scans allows for such reproducible, value-added research and clinical applications. Future applications of this technique include creating semi-automated CAC detection software, as well as well as training modules for clinicians to be able to integrate into their practice4.

Coronary artery disease is a predictor of major adverse cardiovascular events. CAC on CT scans provides objective evidence of coronary artery disease and may identify previously undiagnosed patients. In addition, CAC has a significant prognostic value. Specifically, the absence of CAC on gated cardiac CT scans identifies a patient population that has a low risk for subsequent cardiovascular events in many different subsets of patients, including patients presenting with cardiac symptoms, as well as asymptomatic patients1,2. With ~70 million CT scans performed in the United States and the usage rising, and approximately 11 - 12 million of those scans being CT scans of the chest, the potential for identification of CAC in a large number of patients remains high3. However, the majority of the CT scans of the chest performed in that analysis are not dedicated cardiac CT scans. Dedicated cardiac CT scans have standardized slice thickness, acquisition protocols, electrocardiographic (ECG) gating to minimize cardiac motion, and reconstruction protocols. There is also a standardized quantitation for gated cardiac CT scans using the Agatston score. The Agatston scoring system has been well validated and associated with clinical outcomes1,2.
CAC can be readily identified on these non-gated CT scans but is often overlooked4. Good correlation has been demonstrated between CAC identified on non-gated CT scans and Agatston scores obtained from gated CT scans (&#62; 90% in pooled analysis)5,6,7,8,9,10. In non-gated CT scans, the presence of CAC has been associated with worse clinical outcomes; whereas, the absence is linked to morbidity and mortality benefits10,11,12,13,14,15.
While different studies have looked at the prognosis of CAC on non-gated studies, there has been limited published data on how best to identify CAC. There have been attempts to identify an automated approach to the identification of CAC in low-dose CT chests scans done for lung cancer screening purposes; however, the translation of this to other study protocols is extremely limited16. The introduction of differential CT scanners, protocols, and contrast (both timing and amount) limits the application of this automated approach. Attempts by the Society of Cardiovascular Computed Tomography and the Society of Thoracic Radiology to promote the standard reporting of CAC on all CT chests have been met with mixed results17. While offering a general framework in this guideline document, the specifics of the identification of coronary calcification, especially for providers who do not routinely visualize coronary anatomy, are limited. Also, strategies specific to abdominal CT scans, contrasted studies, and adjudicating challenging cases are not addressed. Many studies publish their own inter- and intra-observer reproducibility for the protocol they used; however, there is not a standard approach used across different studies.
The ability to consistently and reliably identify CAC on these non-gated CT scans allows for the retrospective and prospective observational investigation of CAC in predicting cardiovascular outcomes in many different conditions. However, there needs to be a standard approach taken to identifying CAC on non-gated CT scans to ensure the reproducibility of the results, as well as a consistency in training to help in clinical practice.

<table><tbody><tr><td>Microsoft Windows Server 2012 R2 Standard</td><td>PowerEdge R730</td><td>8F8KFB2</td><td>Server specifications for post-processing software: Intel(R) Xeon(R) CPU E5-2609 v3 @ 1.90GHz Intel(R) Xeon&amp;reg;CPU E5-2609 v3 @ 1.90GHz</td></tr><tr><td>Intuition</td><td>Terarecon</td><td>4.4.12.xxx</td><td>Post-processing software</td></tr><tr><td>McKesson Radiology Viewing Station</td><td>McKesson</td><td>Station Lite Version 1.0.0.182</td><td>IP version 8.0.31.0</td></tr><tr><td>Computer Desktop and Monitor: Optiplex 9030 AIO</td><td>Dell</td><td>Optiplex 9030 AIO</td><td>Processor: Intel&amp;nbsp; Core i5-4590S CPU @ 3.00 GHz, 3001Mhz, 4 Cores, 4 Logical Processors</td></tr></tbody></table>

identifying coronary artery calcification on non-gated computed tomography scans

Coronary artery calcification (CAC) provides an objective measure of coronary artery disease and can readily be identified on non-gated computed tomography (CT) scans with a high correlation with gated cardiac CT scans. This standardized protocol takes a step-wise approach to not only optimizing an image for the identification of calcification but also to distinguishing CAC from other common causes of calcification in the cardiac silhouette. Recognition of CAC on non-gated CT scans helps to identify a very powerful prognostic factor that can influence therapeutic interventions or downstream diagnostic testing without requiring a gated cardiac scan. These non-gated CT scans are often acquired as part of the routine care of the patient, and this data is readily available without another dose of&#160;ionizing radiation. This protocol allows for the precise and accurate extraction of this data for the purposes of retrospective data analysis in clinical research studies, but also in the clinical evaluation and management of patients.

Coronary anatomy is relatively predictable in most patients as described above. The typical locations to evaluate these vessels are also easily identified in most patients (Figure 1). Using the described methodology, the presence or absence of CAC could be reliably identified in 84% of the patients in a single cohort (267 of 317 possible patients)15. The vast majority of patients excluded did not have a CT scan in the designated time frame or had an abdominal CT scan in which the complete coronary vasculature was not seen, and no CAC was identified. In a single patient, a severe respiratory and cardiac motion artifact obscured the discrimination of CAC from mitral annular calcification and was not included in the analysis. The impact of a cardiac motion artifact can be mild or severe (Figure 3). This is one of the main reasons why the correlation between gated and non-gated CT scans is not perfect. However, as scanners become faster, the duration of breath holds and time to acquisition becomes shorter. This minimizes the impact of respiratory and cardiac motion on image quality and improves the temporal resolution of the image.
The degree and distribution of CAC on gated CT scans are independently associated with clinical outcomes but have not been as well assessed in non-gated studies2,19. While it is possible (and recommended, based on guideline documents) to assess the severity of CAC visually, this does require experience. In addition, it is difficult to standardize the visual estimates of severity for research purposes, and while reported inter- and intra-observer reproducibility within the study helps ensure internal validity, it does little to ensure that the correlation between studies is adequate. However, with the validation of some correlative non-gated and gated studies (with quantification) to train the reader and the use of standard protocols across studies, this may be possible to overcome (Figure 4). General considerations for identifying severity include the number of vessels involved, the number of plaques in each vessel, and the density of calcification in each plaque. Single plaques in one or two vessels are usually mild in severity. Multiple calcified plaques involving all 3 epicardial vessels, especially if they are densely calcified, are usually considered as severe CAC.
The distribution of CAC in non-gated studies is more readily identified, although the clinical significance of this in non-gated studies is less clear. Theoretically, multivessel CAC (or diffuse CAC) likely portends worse outcomes beyond the degree of CAC in non-gated studies as it does in gated studies, but this has not been validated. The classification of distribution is usually based on the four epicardial vessel territories (LM, LAD, LCx, and RCA). We have typically classified these as single vessels versus multi-vessel disease (&#62; 1 vessel involved). Proposed quantifications derived from gated studies beyond this (i.e., diffusivity indices) require a reliable CAC score, which is not reliably attainable in non-gated studies.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57918/57918fig1.jpg" /> 
Figure 1: Normal anatomic position of major epicardial coronary arteries. (A) This panel is a more cranial axial slice (maximal intensity projection), close to the origin of the coronary arteries. (B) This panel is a more caudal axial slice, at the mid-ventricular level. The left main artery (LM) originates from the aorta more posteriorly before branching into the left anterior descending artery (LAD) and left circumflex artery (LCx). The LAD runs in the anterior interventricular groove. The LCx runs in the left atrioventricular groove around the mitral valve. The right coronary artery (RCA) originates from the aorta more anteriorly and runs in the right atrioventricular groove around the tricuspid valve. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57918/57918fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57918/57918fig2.jpg" /> 
Figure 2: Identifying subtle areas of coronary artery calcification. This panel shows regions of interest (ROI) over the area of questionable calcification, the ascending aorta, and the sternum, to see the difference in signal intensity as measured by Hounsfield units (HU). The area in question in the RCA is not coronary artery calcification, and the maximal signal intensity is more consistent with the ascending aorta than it is with the sternum (white boxes). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57918/57918fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57918/57918fig3.jpg" /> 
Figure 3: The impact of gating on the visualization of coronary calcium. The upper two panels show (A) a non-gated and (B) a gated CT chest scan on the same patient, where calcification in the right coronary artery (RCA) is still visualized. The lower two panels show (C) a non-gated and (D) a gated CT chest scan on a different patient showing cardiac motion obscuring the mild coronary calcification in the proximal left circumflex&#160;artery (white arrow). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57918/57918fig3large.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/57918/57918fig4.jpg" /> 
Figure 4: Different degrees of coronary calcification. These panels show axial non-contrast CT chest images of different patients showing (A) no calcification, (B) mild calcification, (C) moderate calcification, (D) and severe calcification of the left anterior descending artery. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/57918/57918fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans at JoVE.com

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Here, we present a protocol to reliably and systematically identify coronary artery calcification (CAC) on non-gated computed tomography (CT) scans of the chest or abdomen. CAC provides an objective measure of coronary artery disease for both research and clinical purposes.

Coronary artery calcification (CAC) provides an objective measure of coronary artery disease and can readily be identified on non-gated computed ...

identifying-coronary-artery-calcification-on-non-gated-computed

Universidad San Sebastian

Research

JoVE Journal

Medicine

15.1K Views.  University of Kentucky. Here, we present a protocol to reliably and systematically identify coronary artery calcification (CAC) on non-gated computed tomography (CT) scans of the chest or abdomen. CAC provides an objective measure of coronary artery disease for both research and clinical purposes.

Video: Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

Use of a Hanging Weight System for Coronary Artery Occlusion in Mice

Murine studies of acute injury are an area of intense investigation, as knockout mice for different genes are becoming increasingly available 1-38. Cardioprotection by ischemic preconditioning (IP) remains an area of intense investigation. To further elucidate its molecular basis, the use of knockout mouse studies is particularly important 7, 14, 30, 39. Despite the fact that previous studies have already successfully performed cardiac ischemia and reperfusion in mice, this model is technically very challenging. Particularly, visual identification of the coronary artery, placement of the suture around the vessel and coronary occlusion by tying off the vessel with a supported knot is technically difficult. In addition, re-opening the knot for intermittent reperfusion of the coronary artery during IP without causing surgical trauma adds additional challenge. Moreover, if the knot is not tied down strong enough, inadvertent reperfusion due to imperfect occlusion of the coronary may affect the results. In fact, this can easily occur due to the movement of the beating heart.
Based on potential problems associated with using a knotted coronary occlusion system, we adopted a previously published model of chronic cardiomyopathy based on a hanging weight system for intermittent coronary artery occlusion during IP 39. In fact, coronary artery occlusion can thus be achieved without having to occlude the coronary by a knot. Moreover, reperfusion of the vessel can be easily achieved by supporting the hanging weights which are in a remote localization from cardiac tissues.
We tested this system systematically, including variation of ischemia and reperfusion times, preconditioning regiments, body temperature and genetic backgrounds39. In addition to infarct staining, we tested cardiac troponin I (cTnI)
as a marker of myocardial infarction in this model. In fact, plasma levels of cTnI correlated with infarct sizes (R2=0.8). Finally, we could show in several studies that this technique yields highly reproducible infarct sizes during murine IP and myocardial infarction6, 8, 30, 40, 41. Therefore, this technique may be helpful for researchers who pursue molecular mechanisms involved in cardioprotection by IP using a genetic approach in mice with targeted gene deletion. Further studies on cardiac IP using transgenic mice may consider this technique.

A murine model for myocardial ischemia and ischemic preconditioning is an important tool study cardioprotective mechanisms in vivo. Here, we report an easy applicable in situ model for cardiac IP using a hanging-weight system for coronary artery occlusion.

Murine studies of acute injury are an area of intense investigation, as knockout mice for different genes are becoming increasingly available 1-38. ...

Coronary Artery Ligation and Intramyocardial Injection in a Murine Model of Infarction

Mouse models are a valuable tool for studying acute injury and chronic remodeling of the myocardium in vivo. With the advent of genetic modifications to the whole organism or the myocardium and an array of biological and/or synthetic materials, there is great potential for any combination of these to assuage the extent of acute ischemic injury and impede the onset of heart failure pursuant to myocardial remodeling. 
Here we present the methods and materials used to reliably perform this microsurgery and the modifications involved for temporary (with reperfusion) or permanent coronary artery occlusion studies as well as intramyocardial injections. The effects on the heart that can be seen during the procedure and at the termination of the experiment in addition to histological evaluation will verify efficacy. 
Briefly, surgical preparation involves anesthetizing the mice, removing the fur on the chest, and then disinfecting the surgical area. Intratracheal intubation is achieved by transesophageal illumination using a fiber optic light. The tubing is then connected to a ventilator. An incision made on the chest exposes the pectoral muscles which will be cut to view the ribs. For ischemia/reperfusion studies, a 1 cm piece of PE tubing placed over the heart is used to tie the ligature to so that occlusion/reperfusion can be customized. For intramyocardial injections, a Hamilton syringe with sterile 30gauge beveled needle is used. When the myocardial manipulations are complete, the rib cage, the pectoral muscles, and the skin are closed sequentially. Line block analgesia is effected by 0.25% marcaine in sterile saline which is applied to muscle layer prior to closure of the skin. The mice are given a subcutaneous injection of saline and placed in a warming chamber until they are sternally recumbent. They are then returned to the vivarium and housed under standard conditions until the time of tissue collection. At the time of sacrifice, the mice are anesthetized, the heart is arrested in diastole with KCl or BDM, rinsed with saline, and immersed in fixative. Subsequently, routine procedures for processing, embedding, sectioning, and histological staining are performed. 
Nonsurgical intubation of a mouse and the microsurgical manipulations described make this a technically challenging model to learn and achieve reproducibility. These procedures, combined with the difficulty in performing consistent manipulations of the ligature for timed occlusion(s) and reperfusion or intramyocardial injections, can also affect the survival rate so optimization and consistency are critical.

Numerous genetic manipulations and/or intramyocardial injections of genes, proteins, cells, and/or biomaterials are superimposed upon the dimension of time in studies of acute ischemia/ reperfusion injury and chronic remodeling in mice. This video illustrates the microsurgical procedures for ischemia/reperfusion, permanent coronary artery ligation, and intramyocardial injection studies.

Mouse models are a valuable tool for studying acute injury and chronic remodeling of the myocardium in vivo.  With the advent of genetic modifications to ...

Modified Technique for Coronary Artery Ligation in Mice

Myocardial infarction (MI) is one of the most important causes of mortality in humans1-3. In order to improve morbidity and mortality in patients with MI we need better knowledge about pathophysiology of myocardial ischemia. This knowledge may be valuable to define new therapeutic targets for innovative cardiovascular therapies4. Experimental MI model in mice is an increasingly popular small-animal model in preclinical research in which MI is induced by means of permanent or temporary ligation of left coronary artery (LCA)5. In this video, we describe the step-by-step method of how to induce experimental MI in mice.
The animal is first anesthetized with 2% isoflurane. The unconscious mouse is then intubated and connected to a ventilator for artificial ventilation. The left chest is shaved and 1.5 cm incision along mid-axillary line is made in the skin. The left pectoralis major muscle is bluntly dissociated until the ribs are exposed. The muscle layers are pulled aside and fixed with an eyelid-retractor. After these preparations, left thoracotomy is performed between the third and fourth ribs in order to visualize the anterior surface of the heart and left lung. The proximal segment of LCA artery is then ligated with a 7-0 ethilon suture which typically induces an infarct size ~40% of left ventricle. At the end, the chest is closed and the animals receive postoperative analgesia (Temgesic, 0.3 mg/50 ml, ip). The animals are kept in a warm cage until spontaneous recovery.

The surgical procedure used to induce experimental myocardial infarction in mice begins with left thoracotomy between the third and the fourth ribs in order to visualize the anterior surface of the heart and left lung. The left coronary artery is ligated, the chest is closed and the mouse is allowed to recover spontaneously.

Myocardial infarction (MI) is one of the most important causes of mortality in humans1-3. In order to improve morbidity and mortality in patients with MI ...

Retrograde Perfusion and Filling of Mouse Coronary Vasculature as Preparation for Micro Computed Tomography Imaging

Visualization of the vasculature is becoming increasingly important for understanding many different disease states. While several techniques exist for imaging vasculature, few are able to visualize the vascular network as a whole while extending to a resolution that includes the smaller vessels1,2. Additionally, many vascular casting techniques destroy the surrounding tissue, preventing further analysis of the sample3-5. One method which circumvents these issues is micro-Computed Tomography (&mu;CT). &mu;CT imaging can scan at resolutions &lt;10 microns, is capable of producing 3D reconstructions of the vascular network, and leaves the tissue intact for subsequent analysis (e.g., histology and morphometry)6-11. However, imaging vessels by ex vivo &mu;CT methods requires that the vessels be filled with a radiopaque compound. As such, the accurate representation of vasculature produced by &mu;CT imaging is contingent upon reliable and complete filling of the vessels. In this protocol, we describe a technique for filling mouse coronary vessels in preparation for &mu;CT imaging.
Two predominate techniques exist for filling the coronary vasculature: in vivo via cannulation and retrograde perfusion of the aorta (or a branch off the aortic arch) 12-14, or ex vivo via a Langendorff perfusion system 15-17. Here we describe an in vivo aortic cannulation method which has been specifically designed to ensure filling of all vessels. We use a low viscosity radiopaque compound called Microfil which can perfuse through the smallest vessels to fill all the capillaries, as well as both the arterial and venous sides of the vascular network. Vessels are perfused with buffer using a pressurized perfusion system, and then filled with Microfil. To ensure that Microfil fills the small higher resistance vessels, we ligate the large branches emanating from the aorta, which diverts the Microfil into the coronaries. Once filling is complete, to prevent the elastic nature of cardiac tissue from squeezing Microfil out of some vessels, we ligate accessible major vascular exit points immediately after filling. Therefore, our technique is optimized for complete filling and maximum retention of the filling agent, enabling visualization of the complete coronary vascular network &#x2013; arteries, capillaries, and veins alike.

Visualization of the coronary vessels is critical to advancing our understanding of cardiovascular diseases. Here we describe a method for perfusing murine coronary vasculature with a radiopaque silicone rubber (Microfil), in preparation for micro-Computed Tomography (&mu;CT) imaging.

Visualization of the vasculature is becoming increasingly important for understanding many different disease states. While several techniques exist for ...

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 ...

A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro

Vascular calcification involves a series of degenerative pathologies, including inflammation, changes to cellular phenotype, cell death, and the absence of calcification inhibitors, that concomitantly lead to a loss of vessel elasticity and function. Vascular calcification is an important contributor to morbidity and mortality in many pathologies, including chronic kidney disease, diabetes mellitus, and atherosclerosis. Current research models to study vascular calcification are limited and are only viable at the late stages of calcification development in vivo. In vitro tools for studying vascular calcification use end-point measurements, increasing the demands on biological material and risking the introduction of variability to research studies. We demonstrate the application of a novel fluorescently labeled probe that binds to in vitro calcification development on human vascular smooth muscle cells and determines the real-time development of in vitro calcification. In this protocol, we describe the application of our newly developed calcification assay, a novel tool in disease modeling that has potential translational applications. We envisage this assay to be relevant in a broader spectrum of mineral deposition research, including applications in bone, cartilage, or dental research.

A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro

Cardiovascular disease is the leading cause of death worldwide. Vascular calcification contributes substantially to the burden of cardiovascular morbidity and mortality. This protocol describes a simple method to quantify vascular smooth muscle cell-mediated calcium precipitation in vitro by fluorescent imaging.

Vascular calcification involves a series of degenerative pathologies, including inflammation, changes to cellular phenotype, cell death, and the absence ...

Biochemistry

Improving Coronary Artery Calcification Assessment with the SWCS Tool

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

The current standard for measuring coronary artery calcification to determine the extent of atherosclerosis is by calculating the Agatston score from computed tomography (CT). However, the Agatston score disregards pixel values less than 130 Hounsfield Units (HU) and calcium regions less than 1 mm2. Due to this thresholding, the score is not sensitive to small, weakly attenuating regions of calcium deposition and may not detect nascent micro-calcification. A recently proposed metric called the spatially weighted calcium score (SWCS) also utilizes CT but does not include a threshold for HU and does not require elevated signals in contiguous pixels. Thus, the SWCS is sensitive to weakly attenuating, smaller calcium deposits and may improve the measurement of coronary heart disease risk. Currently, the SWCS is underutilized owing to the added computational complexity. To promote translation of the SWCS into clinical research and reliable, repeatable computation of the score, the aim of this study was to develop a semi-automatic graphical tool that calculates both the SWCS and the Agatston score. The program requires gated cardiac CT scans with a calcium hydroxyapatite phantom in the field of view. The phantom allows for deriving a weighting function, from which each pixel&#39;s weight is adjusted, allowing for the mitigation of signal variations and variability between scans. With all three anatomical views visible simultaneously, the user traces the course of the four main coronary arteries by placing points or regions of interest. Features such as scroll-to-zoom, double-click to delete, and brightness/contrast adjustment, along with written guidance at every step, make the program user-friendly and easy to use. Once tracing the arteries is complete, the program generates reports, which include the scores and snapshots of any visible calcium. The SWCS may reveal the presence of subclinical disease, which may be used for early intervention and lifestyle changes.

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection 

This video demonstrates the use of a novel graphical tool for measuring the spatially weighted calcium score (SWCS), an alternative to the Agatston score, for quantifying coronary artery calcification. The graphical tool computes SWCS based on image data from gated cardiac computed tomography and user-defined paths of the coronary arteries.

The current standard for measuring coronary artery calcification to determine the extent of atherosclerosis is by calculating the Agatston score from ...

Bioengineering

Non-Invasive Point-of-Care Test for Coronary Artery Disease Assessment 

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

A point-of-care non-invasive test for Coronary Artery Disease (CAD) (POC-CAD) has been previously developed and validated. The test requires the simultaneous acquisition of orthogonal voltage gradient (OVG) and photoplethysmogram signals, which is the primary methodology described in this paper. The acquisition of the OVG, a biopotential signal, necessitates the placement of electrodes on the prepared skin of the patient&#39;s thorax (arranged similarly to the Frank lead configuration, comprising six bipolar electrodes and a reference electrode) and a hemodynamic sensor on the finger (using a standard transmission modality). The signal is uploaded to a cloud-based system, where engineered features are extracted from the signal and supplied to a machine-learned algorithm to yield the CAD Score. The physician must then interpret the value of the CAD Score in the context of their patient&#39;s pre-test probability of CAD, resulting in a post-test probability of CAD. This interpretation can be performed at the level of test positivity and test negativity or at a finer level of granularity; methodologies for each are proposed here based on likelihood ratios. Using the post-test probability, the physician must determine the appropriate next step in the treatment of their patient; several scenarios are used to illustrate this process. Test adoption is only feasible if economically viable; a discussion of the integration of the test into the CAD diagnostic flow and the resultant cost savings to the healthcare system is provided. The economic model demonstrates that cost savings to the healthcare system can be achieved by preventing delayed treatment, which, if left unaddressed, results in disease progression requiring more advanced (and expensive) care.

The methodology to acquire the physiological signal for a Coronary Artery Disease (CAD) test is presented. A method is proposed to interpret the CAD score concerning test positivity and negativity, including the granularities of each. The economics of the test are discussed in the context of the current standard of care.

A point-of-care non-invasive test for Coronary Artery Disease (CAD) (POC-CAD) has been previously developed and validated. The test requires the ...

Longitudinal Quantification of Cardiovascular Calcification Using PET/CT Imaging

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images

Micro positron emission tomography (PET) and micro computed tomography (CT) imaging are powerful, ideal research tools for following the progression of cardiovascular calcification. Due to their non-invasive nature, small research animals can be imaged at multiple time points. The challenge lies in the accurate quantification of cardiovascular calcification. Here, we provide a protocol, using images from the later disease stages as a template, to accurately quantify the progression of cardiovascular calcification in longitudinal studies. The protocol involves 1) the alignment of the chest area in multiple images from the same animal during a longitudinal study as the first step, 2) the definition of a region of interest (ROI) situated within the heart and the aorta at the site of larger calcium deposits that become apparent in later images, and 3) simultaneous segmentation and quantification of calcium deposits across all images acquired during the longitudinal study. This streamlined method enhances the accuracy of image analysis in following the progression of cardiovascular calcification by improving the precision of ROI definition and reducing the variability associated with earlier techniques that analyze individual scans independently.

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals

This novel protocol entails the quantification of cardiovascular calcification progression from serial micro positron emission tomography (PET)/micro computed tomography (CT) images in small research animals.

Micro positron emission tomography (PET) and micro computed tomography (CT) imaging are powerful, ideal research tools for following the progression of ...

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

The CrumpCAT is a prototype small-animal X-ray computed tomography (CT) scanner developed at our research institution. The CMOS detector with a maximum frame rate of 29 Hz and similar Tungsten X-ray sources with energies ranging from 50 kVp to 80 kVp are widely used across commercially available preclinical X-ray CT instruments. This makes the described work highly relevant to other institutions, despite the generally perceived wisdom that these detectors are not suitable for gating the high heart rates of mice (~600 beats/min). The scanner features medium- (200 &#181;m) and high- (125 &#181;m) resolution imaging, fluoroscopy, retrospective respiratory gating, and retrospective cardiac gating, with iterative or filtered-back projection image reconstruction. Among these features, cardiac gating is the most useful feature for studying cardiac functions in vivo, as it effectively eliminates image blurring caused by respiratory and cardiac motion. 
Here, we describe our method for preclinical intrinsic retrospective cardiac-gated CT imaging, aimed at advancing research on in vivo cardiac function and structure analysis. The cardiac-gating method acquires a large number of projections at the shortest practical exposure time (~20 ms) and then retrospectively extracts respiratory and cardiac signals from temporal changes in raw projection sequences. These signals are used to reject projections belonging to the high motion rate inspiration phase of the respiratory cycle and to divide the remaining projections into 12 groups, each corresponding to one phase of the cardiac cycle. Each group is reconstructed independently using an iterative method to produce a volumetric image for each cardiac phase, resulting in a four-dimensional (4D) dataset. 
These phase images can be analyzed either collectively or individually, allowing for detailed assessment of cardiac function. We demonstrated the effectiveness of both approaches of the prototype scanner's cardiac-gating feature through representative in vivo imaging results.

We provide a comprehensive description of the intrinsic retrospective cardiac gating method of the CrumpCAT, a prototype small-animal X-ray computed tomography (CT) scanner designed and constructed at our research institution.

The CrumpCAT is a prototype small-animal X-ray computed tomography (CT) scanner developed at our research institution. The CMOS detector with a maximum ...

Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Rozdziały w tym wideo

Use of a Hanging Weight System for Coronary Artery Occlusion in Mice

Coronary Artery Ligation and Intramyocardial Injection in a Murine Model of Infarction

Modified Technique for Coronary Artery Ligation in Mice

Retrograde Perfusion and Filling of Mouse Coronary Vasculature as Preparation for Micro Computed Tomography Imaging

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

A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro

Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph