State of the Technology: Geoffrey Rubin, MD, on 3D Visualization

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As CT technology continues to advance and the number of slices in a given exam grows exponentially, how is the role of enterprise visualization software evolving to suit the needs of the modern radiology department? speaks with Geoffrey Rubin, MD, professor of radiology and vice chief of staff at Stanford University Hospitals and Clinics, Stanford, California, on the state of the technology.

imageGeoffrey Rubin, MD Which enterprise visualization tools have proved most useful in enhancing 3D interpretations of cardiac imaging? How can techniques like bone removal and artery isolation be leveraged for more rapid speed of interpretation, and what are the hazards involved?

Rubin: The tools most useful for cardiac imaging would be multiplanar reformation, curved planar reformation, and volume rendering. There are hazards in using bone removal and arterial-tree isolation; whenever you perform any segmentation tasks, you run the risk of removing structures that you didn’t intend to remove. It’s important to be careful when using these tools.

For the heart, in particular, bone removal isn’t so important. Vessel-tree extraction tends to be used to create volume renderings or maximum-intensity projections of the coronary arteries, but oftentimes, it’s most desirable to use volume rendering to see coronary arteries within the context of adjacent anatomy, such as the surface of the myocardium. I don’t find myself using these types of segmentation tools for cardiac imaging specifically.

In fact, when assessing the coronary arteries, I stick with multiplanar and curved reformations. For other applications of workstations—for extracardiac CT angiography, for instance—bone removal can be very useful, such as with a lower-extremity CT runoff. There again, though, it can be risky, and you have to be diligent to make sure you haven’t removed adjacent vessels along with the bone. Which tools have proved most useful in enhancing 3D interpretations of pulmonary imaging?

Rubin: Routine application of 3D processing for pulmonary imaging is still controversial. From the standpoint of most accurately assessing and following lung lesions, some type of volumetric measurement of their size is the most logical approach, but there isn’t yet enough consistency among techniques. You’ve done a great deal of research on computer-aided detection for lung nodules. How does the use of computer-aided detection enhance lung-cancer diagnosis? Does it adversely affect workflow?

Rubin: There again, computer-aided detection in the lungs has been used mostly in a research setting; there are few implementations in commercial products. One of the real challenges with computer-aided detection is that it detects areas in lung CT images that are suspected of being lung nodules—structures that are defined as focal lung opacities that range from 3 cm in size on down; beyond that definition, the likelihood that any of those nodules represents a lung cancer is highly dependent on the size of the lesion and who the patient is.

We know computer-aided detection will detect more, or at least different, lung nodules than experienced CT readers. We find more nodules, and there’s more equivalency of performance across readers when they use computer-aided detection. What we don’t know is whether or not those detected nodules will ultimately become cancer. That will require more investigation and more understanding.

It’s complicated further by the fact that we don’t know whether detecting cancers at an early stage means that we can act on those cancers and allow people to live longer than they would have if the cancer were allowed to grow until it became symptomatic. That’s the subject of the National Lung Screening Trial, looking at CT as a screening tool, which we hope will give us the answer.

The ultimate utility of computer-aided detection in detecting lung cancer, however, is dependent on many other characteristics that are independent of the computer algorithm. At one end of the spectrum, computer-aided detection could find early cancers in a setting where their detection is valuable and important. At the other end, computer-aided detection could be finding a bunch of things in the CT scan that either correspond to a real structure in the lung or do not, resulting in additional patient anxiety, work-up, and expense, and in further imaging that isn’t necessary. How does