Image Postprocessing in Cardiac Computed Tomography

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CHAPTER 12 Image Postprocessing in Cardiac Computed Tomography

The role of CT in the evaluation of cardiac disease is no longer the subject of possible applications, but more in the realm of determining positive and negative predictive values as cardiac CT angiography becomes a central part of cardiac imaging. Whether the application is to determine coronary artery stenosis in native vessels, to determine patency of bypass grafts or stents, or to detect anomalous anatomy, CT has been shown in many cases to be the study of choice. In the emergency department, the power of a 100% negative predictive value promises to lead to a paradigm shift in emergency department triage for patients with chest pain.1,2 Similarly, a gated CT acquisition has become the standard of care to evaluate aortic root pathology, including valvular disease, suspected dissection, Marfan syndrome, and Loeys-Dietz syndrome.35 Other applications, such as preoperative assessment and postoperative evaluation in congenital heart disease, are becoming a well-accepted application for CT angiography.6,7

Despite more than 400 articles on the subject and a body of literature that continues to grow, few published data have focused on the technical aspects of different processing techniques for the evaluation of the heart and coronary arteries.813 The “how-to” of data acquisition (16 vs. 64 vs. 128 detector scanners)1416 is addressed in detail in the literature, as are contrast delivery techniques (test bolus vs. bolus triggering vs. timed injection),1719 but the analysis of the resultant CT data has been the topic of only a few articles of note. This chapter provides a systematic approach to data analysis, with emphasis on how to use each postprocessing tool to its greatest advantage (Figs. 12-1 through 12-4), to interpret most accurately an isotropic multidetector CT volume of the heart.

TECHNICAL REQUIREMENTS

With respect to data analysis, the most comprehensive article has been by Ferencik and colleagues.11 Two important statements from this article are as follows:

The key point made by this article was that the method of data set evaluation (interactive interrogation of the volume by the interpreting physician vs. review of preset images generated by an individual not performing the interpretation) is perhaps even more important than the postprocessing tools used. Interacting with the data set using only axial images and MPRs was more efficacious than review of preset three-dimensional images. When preset images are reviewed, the information seen depends on the skill of the individual (interpreting physician or technologist) who performed the postprocessing. We have found that in nearly every case, errors in postprocessing can result in key mistakes in data interpretation. Interactivity means that the interpreting physician must process the images himself or herself. The following rules are suggested to do this successfully:

Let us assume that the patient has been scanned, the data have been processed into the appropriate slice thickness (0.6 to 0.75 mm) and interscan spacing (0.4 to 0.5 mm), and the data have been sent to the workstation. Now what? That question is guided in part by individual preference and by workflow, and partly dictated by the workstation available to the user. Regardless of the workstation specifics, some general principles can be implemented across almost all current platforms.

TECHNIQUE

Axial Images

Although a cardiac CT scan typically is reviewed at least using multiplanar reconstruction and three-dimensional renderings, the axial images (see Fig. 12-3) remain a crucial part of the study workflow. At some sites with highly experienced interpreting physicians, interpretation is typically limited to the axial images with sparing use of additional views in selected cases. On a workstation with a composite display of axial, coronal, and sagittal images combined with a three-dimensional image, our strategy for axial images is to scroll interactively through the data set in what the scanner selects as the optimal phase (0% to 90% of R–R interval on retrospective gated study) for coronary artery visualization. We then do the following:

The limitation of axial images is that one may need to look at 200 to 350 individual slices to get through a single vessel, such as the left anterior descending coronary artery. Vessels with off-axis courses, such as the right coronary artery, are best defined when one is looking beyond the axial plane. A more global presentation of an entire coronary artery is impossible without a curved planar reconstruction (CPR) (see Fig. 12-4) or three-dimensional display.

Our experience is that referring physicians desire three-dimensional maps to correlate with what they typically see on classic catheter angiography. Axial CT images in this regard are not helpful and do not provide similar information.

Although we typically scroll through the classic axial display interactively, other physicians believe that reading studies can be noninteractive, and that there are no data to prove otherwise. This controversy needs to be addressed in a future study. One issue is the lack of a well-designed, controlled study on the use of different techniques for coronary CT angiography analysis. This is in contrast to many articles published on reading techniques (e.g., two-dimensional vs. three-dimensional) for virtual colonoscopy.

Although most physicians review the axial images as slabs in the plane acquired, one helpful hint is to draw a line through the plane of the aortic valve, and scroll up and down through this plane. This practice often provides a perfect en face display of the proximal coronary arteries.

Multiplanar Reconstruction—Coronal and Sagittal Images

The use of coronal and sagittal reconstructions is common in a range of CT applications, and in cardiac CT its role is also defined. With isotropic data, the individual cardiac chambers, aorta, and pulmonary vasculature can benefit from these displays, and they are routinely used. Whether it is for suspected aortic dissection or suspected pulmonary embolism, these displays are essential. On most workstations, the thickness of the reconstruction can be interactively adjusted (Fig. 12-5), but when slabs are reduced to beyond a few millimeters, the images become fuzzy and are of no value. This is in contrast to MIP images, where often less data may prove to be useful for these large-volume applications. One issue with coronal and sagittal reconstruction and evaluation of the coronary arteries is that because the vessels have a nonlinear pathway, only short segments can be analyzed at a time. When the left anterior descending coronary artery is analyzed with a coronal or axial display, one must constantly change the angle of the reconstruction plane to follow the vessel. Changing the angle can be done interactively, but potentially results in more room for error and less than ideal image displays. For analyzing selected segments of a vessel, this technique might be perfect. If there is a suspect area on the axial views, the coronal or sagittal view may help clarify and quantify this potential narrowing. Key points include the following:

Curved Planar Reconstruction

As discussed, the limitations with coronal and sagittal reconstruction largely involve the inability to track the complex courses of the coronary arteries. CPR addresses the issue directly (Fig. 12-6). CPR is best thought of as a reconstruction plane that perfectly tracks a vessel regardless of the complexity of its course or direction. In the initial versions of CPR, the user would drop multiple points through the path of a vessel, and the computer algorithm would track the points and create a vessel map. Currently, most vendors have designed software that makes the process more robust and easier to use, and requires minimal user interaction.10,12 On some workstations, there is a program that can be used for tracking the coronary vessels. One simply picks a start and an end point, and the computer automatically tracks the vessel. The vessel is laid out like a string or piece of spaghetti, and the user can rotate the plane to look at the vessel and analyze it from multiple perspectives. The vessel path is calculated as a central line through the vessel, which is also crucial in avoiding errors in analysis. The curved planar images can be viewed in classic soft tissue window or altered to be presented in MIP mode. In cases where there is a gap or stenosis in the vessel, dropping additional seed points can be done to help with vessel tracking.

We use CPR for every coronary artery CT angiography (Figs. 12-7 and 12-8). The ability to lay out a vessel without the worry of partial averaging is important not only when trying to define the presence of stenosis, but also for calculating the degree of stenosis. On some systems, the CPR also serves as the basis for the system to calculate automatically the percent stenosis present. A function that is common on all cardiac-specific software packages and many vessel analysis packages has the user pick the area of concern (usually area with greatest stenosis) and select a point of normal vessel proximal and distal to the stenosis. When selected, the system automatically calculates the percent stenosis. It is important to be careful with these programs because there are many potential sources of error, but at least they are a guide, and in the future they should be more robust. Several key points regarding CPR and some highlights and pitfalls are as follows:

Maximum Intensity Projection

The role of three-dimensional imaging, using MIP or VRT, still is controversial today. Although most sites use some type of three-dimensional imaging for selected cardiac applications, experts have yet to reach consensus as to whether this practice is mandatory or necessary in all cases. Our experience is just the opposite, however. Postprocessing with three-dimensional imaging is an essential part of every examination, providing crucial information not revealed by other interpretative methods. One caveat is that MIP is most valuable and accurate in a patient without coronary artery calcification. In the presence of small amounts of calcification, MIP images can easily overestimate the presence and extent of stenosis.

In practice, MIP is usually performed with a sliding slab to look at the coronary arteries. The term sliding MIP is used to emphasize that it is done interactively at the workstation by the interpreting physician to define the best plane for vessel display. There are numerous tricks of the trade and pitfalls with MIP in general.20,22 By adjusting the MIP slab thickness (1 to 30 mm), one can interactively vary the size of the segmented volume as needed (see Fig. 12-5), but one always needs to be careful not to edit out crucial parts of the data set. The potential issues with MIP can be seen through a careful understanding of the technique itself. Some key points from a technical perspective are as follows:

In a patient without calcification, MIP can create agreeable images of long segments of the coronary arteries that superficially appear as a comprehensive display. Soft plaque is overlooked, however, unless it creates significant lumen narrowing. Rybicki and coworkers13 also noticed this issue and issued the following warning:

We use MIP in every case because it does add value, especially in terms of speed and for viewing small-caliber distal portions of smaller vessels. Some key points are as follows:

Volume Rendering Technique

VRT has a crucial role in evaluation of cardiac and coronary CT angiography (Fig. 12-9). The role of VRT in the analysis of these cases is based on numerous factors, including the ability of VRT to display large volumes of data accurately, especially from complex data sets. The best way to understand the crucial role of VRT is to remember several of the basics of VRT.20

VRT can display data without classifying it into rigid all-or-nothing categories as thresholding does. VRT is most often performed with a method of classification termed percentage classification. The key difference between thresholding classification and percentage classification is that, in thresholding, it is assumed that each voxel contains either all or none of a particular tissue type, and no mixtures of tissues. In percentage classification, it is assumed that a voxel can contain one or more tissue types, and the amount of each tissue is a continuum between 0% and 100%. Percentage classification is possible to approximate more closely true voxel content in voxels containing tissue mixtures, or volume averaging. Percentage classification involves examination of each voxel to determine the amounts (percentages) of each tissue type present in the voxel. The resultant classified volume data consist of voxels still representing the percentage of each tissue type initially present.

The most common method used to determine the percentage contents is probabilistic classification involving a trapezoidal approximation. This method for determining tissue-type percentages works well for CT data. For trapezoidal classification, each tissue type is assigned a nominal value range that theoretically represents that tissue type exactly. A voxel with a signal within that nominal value range is considered to contain 100% of that tissue. Around this ideal nominal value range, another range is defined by choosing a high and low point representing attenuation values at which a voxel would contain none of the designated tissue. Voxels with signal intensities that lie between 0% and 100% are assigned a corresponding percentage between 0% and 100%. A voxel with signal intensity precisely halfway between 0% and 100% would be assigned 50% of that tissue. A voxel with signal intensity three fourths of the way toward 100% would be assigned 75% of that tissue. All values between 0% and 100% represent voxels in which volume averaging is present (i.e., more than one tissue is present). This trapezoidal classification models closely the actual volume averaging in CT voxels.

When the data have been assigned percentages, they must be processed further to form a final image. Each tissue is assigned a color and transparency. Each voxel is assigned a color and transparency by taking a weighted sum of the percentage of each tissue present in the voxel and the color and transparency assigned to those tissues. A final image is produced by casting simulated rays of light through the volume containing the classified and colored voxels. As the simulated rays pass through a voxel, the color and transparency of the voxel modulates the color of the ray.

A few words of caution: although MIP images may seem similar from one workstation to the other, this rule does not hold true with VRT. Each vendor has its own “flavor” of volume rendering. The generic term volume rendering simply refers to a method of making three-dimensional images from volume data that allows every voxel in the volume potentially to contribute to the final image. Many different specific methods of volume rendering can produce vastly different results. Which method the vendor uses has a large effect on the resulting images. In addition, for most volume rendering methods, there are many adjustable parameters that change the way the image looks. The simplest parameters are windowing settings (window center and width). VRT has many other parameters, such as color, opacity, and shading. Because there is no standardization in VRT, the parameters from one system do not generally translate well to another system. Image quality also varies among different vendors.

The method of VRT has the biggest effect on image quality. Other factors also come into play, however. Does the system use full 12-bit (−1024 to 3072 HU) input data for rendering? Does it limit the volume size to some maximum so that larger volumes are shrunk when loaded? What is the quality of the video display? All of these factors have an effect on image quality. The interpreting physician must learn the specific capabilities of the available system to be used for image processing.

VRT can help accurately define complex anatomy via what we would describe as global or volume visualization. With this technique, one can view the entire data set or a subset, yet keep the spatial relationships intact. VRT allows for either gray-scale or color mapping; the latter is especially attractive in complex cases. Although techniques such as MIP require careful editing to be able to do routine visualization, VRT provides global viewing of the heart—notably its chambers and coronary arteries, and their relationship to the sternum and chest wall. This global visualization is especially valuable in a patient with complex cardiac anomalies or a patient who has had prior cardiac surgery ranging from bypass grafts to stents to conduits. In contrast to other techniques, VRT tends to create a more global view; several of the conditions in which this is a huge advantage include the following:

4 Cases where anomalies of coronary artery origin are suspected. VRT can help define the course of even the most complex variation. It can provide all of the information in a series of select views or as part of a video or audio-video interleave presentation. As noted by Duran and colleagues,21 “As a conclusion, our study showed that multidetector CT, especially volume rendering and maximum intensity projection techniques, may be useful for assessment of complex variations, when the conventional angiography may not be sufficient.”

Generally, we like to review the VRT images of the heart to get a “lay of the land.” Many unsuspected pathologies are seen on these initial overviews. Although VRT images should not be used to grade stenosis, with experience one can recognize stenosis on the images, which may be helpful in further evaluation of the data set. VRT can introduce substantial errors in the volume display, so there is a definite learning curve with this technique.

CONCLUSION

The interpreting physician reviewing cardiac CT imaging needs to master numerous image interpretation skills for optimal viewing and analysis of three-dimensional image data sets. This chapter addresses key points needed for analysis of CT angiography of the coronary arteries and interpretation to assist the reader in daily practice. The interpreting physician needs to use each visualization tool to its maximum advantage and develop a personal workflow that is comfortable and accurate.

KEY POINTS

REFERENCES

1 Hoffmann U, Nagurney JT, Moselewski F, et al. Coronary multidetector computed tomography in the assessment of patients with acute chest pain. Circulation. 2006;114:2251-2260.

2 Rubinshtein R, Halon DA, Gaspar T, et al. Usefulness of 64-slice cardiac computed tomographic angiography for diagnosing acute coronary syndromes and predicting clinical outcome in emergency department patients with chest pain of uncertain origin. Circulation. 2007;115:1762-1768.

3 Feuchtner GM, Dichti W, Muller S, et al. 64-MDCT for diagnosis of aortic regurgitation in patients referred to CT coronary angiography. AJR Am J Roentgenol. 2008;191:W1-W7.

4 Konen E, Goitein O, Feinberg MS, et al. The role of ECG-gated MDCT in the evaluation of aortic and mitral mechanical valves: initial experience. AJR Am J Roentgenol. 2008;191:26-31.

5 Laissy JP, Messika DZ, Serfaty JM, et al. Comprehensive evaluation of preoperative patients with aortic valve stenosis: usefulness of cardiac multidetector computed tomography. Heart. 2007;93:1121-1125.

6 Leschka S, Oechslin E, Husmann L, et al. Pre- and postoperative evaluation of congenital heart disease in children and adults with 64-section CT. RadioGraphics. 2007;27:829-846.

7 Spevak PJ, Johnson PT, Fishman EK. Surgically corrected congenital heart disease: utility of 64-MDCT. AJR Am J Roentgenol. 2008;191:854-861.

8 Cademartiri F, Mollet N, Alemos PA, et al. Standard versus user-interactive assessment of significant coronary stenoses with multislice computed tomography coronary angiography. Am J Cardiol. 2004;94:1590-1593.

9 Choi JW, Seo JB, Do KH, et al. Comparison of transaxial source images and 3-plane, thin-slab maximal intensity projection images for the diagnosis of coronary artery stenosis with using ECG-gated cardiac CT. Korean J Radiol. 2006;7:20-27.

10 Cordeiro MA, Lardo AC, Brito MS, et al. CT angiography in highly calcified arteries: 2D manual vs. modified automated 3D approach to identify coronary stenoses. Int J Cardiovasc Imaging. 2006;22(3-4):507-516.

11 Ferencik M, Ropers D, Abbara S, et al. Diagnostic accuracy of image postprocessing methods for the detection of coronary artery stenoses by using multidetector CT. Radiology. 2007;243:696-702.

12 Dewey M, Schnapauff D, Laule M, et al. Multislice CT coronary angiography: evaluation of an automatic vessel detection tool. Rofo. 2004;176:478-483.

13 Rybicki FJ, Lu M, Fail P, Daniels M. Utilization of thick (>3 mm) maximum intensity projection images in coronary CTA interpretation. Emerg Radiol. 2006;13:157-159.

14 Dewey M, Hoffmann H, Hamm B. CT coronary angiography using 16 and 64 simultaneous detector rows: intraindividual comparison. Rofo. 2007;179:581-586.

15 Heffernan EJ, Dodd JD, Malone DE. Cardiac multidetector CT: technical and diagnostic evaluation with evidence-based practice techniques. Radiology. 2008;248:366-377.

16 Flohr T, Stierstorfer K, Raupach R, et al. Performance evaluation of a 64-slice CT system with z-flying focal spot. Rofo. 2004;176:1803-1810.

17 Cademartiri F, Luccichenti G, Marano R, et al. Techniques for optimisation of coronary artery opacification in non-invasive angiography with a 16-row multislice computed tomography. Radiol Med (Torino). 2004;107(1-2):24-34.

18 Cademartiri F, Nieman K, van der Lugt A, et al. Intravenous contrast material administration at 16-detector row helical CT coronary angiography: test bolus versus bolus-tracking technique. Radiology. 2004;233:817-823.

19 Bae KT. Test-bolus versus bolus-tracking technique for CT angiographic timing. Radiology. 2005;235:369-370.

20 Fishman EK, Ney DR, Heath DG, et al. Volume rendering versus maximum intensity projection in CT angiography: what works best, when, and why. RadioGraphics. 2006;26:905-922.

21 Duran C, Kantarci M, Durur S, et al. Remarkable anatomic anomalies of coronary arteries and their clinical importance: a MDCT angiographic study. J Comput Assist Tomogr. 2006;30:939-948.

22 Calhoun PS, Kuszyk BS, Heath DG, et al. Three-dimensional volume rendering of spiral CT data: theory and method. RadioGraphics. 1999;19:745-764.