Chris Wyatt is a Virginia Tech electrical engineer who is attempting to provide the medical community with better, quicker, and more relevant images of the human body. The side effects are not bad either - lower medical costs, new treatments, and earlier disease detection.
Today, doctors and researchers can view the body's hard and soft tissues through X-rays, ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) technology. With MRI and positron emission tomography (PET) scans, viewing cellular activity is also possible.
But these viewing techniques can be enhanced. Wyatt is specifically looking at improving imaging for virtual colonoscopies; developing algorithms to replace extensive manual work in brain imaging; and developing image-guided polypectomy technology.
As an example of the impact that smarter imaging techniques could have, consider the pharmaceutical industry and its drug trials. "Drug studies involve many hundreds of patients; that's a lot of data. If you're evaluating a new drug for cancer and you scan 1,000 patients three times, you have 3,000 sets of data. Can you hire a radiologist to look at all that?
"You can acquire the data, but pulling out the information you want -– such as how the lesion is changing – is a difficult, time-consuming process that right now is done manually in many cases. Trained technicians look at the images and outline the lesions by hand," said Wyatt, a faculty member in the Virginia Tech – Wake Forest University School of Biomedical Engineering and Sciences.
In brain imaging, Wyatt is concentrating on improving the medical understanding of addiction through modeling techniques. Wyatt is working with the Wake Forest University School of Medicine's Center for the Neurobehavorial Study of Alcohol (CNSA) to develop algorithms that increase the medical understanding of neurological structures beyond what is currently provided by state-of-the-art MRIs. Wyatt hopes to produce an MRI brain template for two species of macaque monkeys and verify his results using the animals in ongoing studies of alcohol abuse and alcoholism.
"Monkeys are a unique tool for alcoholism research because several aspects of their alcohol consumption closely mimic those of humans," Wyatt said. "Using monkey models and magnetic resonance imaging, it is possible to design complex studies to understand the neurological mechanisms of alcoholism without the confounding factors problematic in human research. However, current neuro-image analysis tools were designed for use on human data and do not provide the same level of accuracy and robustness when applied to monkey data," he explained. Wyatt's goal is to develop a comprehensive set of tools for the analysis of monkey images.
If Wyatt is able to improve medicine's understanding of the biological mechanisms of addiction, then he will also increase the knowledge about the influence of risk factors and the effects on the body. "As many of these mechanisms are primarily located in the brain, understanding the neurological effects of alcohol and related factors is a key aspect of alcoholism research. This knowledge is critical to diagnosis, treatment, and prevention of alcoholism," Wyatt said.
Computer-aided diagnosis could also improve the quality of evaluations. "The problem with evaluating all these images manually is that you use different people at different skill levels at different times of the day. People inherently introduce inconsistencies, whereas computers are more consistent and reliable if programmed correctly."
Wyatt is also pursing imaging advancements in the detection and treatment of colon cancer. "About 50 percent of today's colon cancer cases could have been prevented with early detection of polyps. Doctors have the screening methods, but compliance is a problem. Colonoscopies are not fun. If we can do the initial screening with more comfortable imaging instead of scoping, we can get higher compliance and detect more cases early," he said.
He is working to extend the virtual colonoscopy technology to image-guided polypectomy. "The polyps still need to be surgically removed, but patients who already know they have polyps are much more amenable to enduring a scope. A well-trained endoscopist, if there is no problem with insertion, is very fast and very good. Sometimes, though, the polyps can hide in a fold and finding them can be difficult. If we can use virtual colonoscopy to help guide the endoscope to the polyps, we can help the endoscopists become even better and faster."
While earning his Ph.D. at the Wake Forest University School of Medicine, Wyatt worked in its Virtual Colonoscopy Laboratory, which uses CT data to image the colon.
"My efforts are in connecting prior information to analyze the data we get from different imaging," he said. The prior information encompasses anatomy, physiology, and imaging experience. "Physicians and radiologists use prior knowledge of the organs and prior experience in reading images," he explained. "When they look at an image, even if it's not a good image, they impose their knowledge to extract usable information. We've been working for some time to develop algorithms to incorporate this kind of knowledge into the operating systems of imaging equipment."