Computed tomography –or CT–scans have become a powerful imaging tool for diagnosing disease. Health-care providers performed more than 70 million CT scans in the United States in 2007.

A December 2009 study in the Archives of Internal Medicine calculated that those 70 million scans could lead to 29,000 cancers. That figure is a statistical calculation and “there is no direct evidence linking the radiation dose from CT scans to cancer,” says Cynthia McCollough, a radiological physicist at the Mayo Clinic. “Doses delivered in a CT scan are of the same magnitude that we get every year from background radiation.” (A typical CT scan might result in a dose of one to 14 millisieverts. )

Nevertheless, the CT community is looking for ways to reduce the radiation dose from scanners. This is because CT scans are becoming more common, and because multiple scans are often required for some patients such as those suffering from head or spine trauma . Some promising techniques for reducing CT scan radiation were recently presented at the meeting of the American Association of Physicists in Medicine in Philadelphia.

Researchers at GE Healthcare in Waukesha, WI, presented a technique that needs roughly an eighth of the radiation dose of today’s scanners to create an image just as sharp and with the same high resolution. “Dose reduction depends on a case-by-case basis and the application,” lead scientist Girijesh Yadava says. “You could go lower or higher [than an eighth] depending on the part of body.”

A CT scanner puts together multiple cross-sectional images to create a detailed picture of body structures. An x-ray tube rotates around the patient and directs beams into the body from different angles. After the rays pass through the body, their intensity is measured by an array of detectors on the other side. A computer algorithm then reconstructs images from the intensity data. Just as each light detector in a digital camera corresponds to an image pixel, each detector gives a voxel, or volume element, of the image.

Conventional reconstructive algorithms assume that the x-ray source and detector are points and that the x-ray beam is a line. Last year, GE Healthcare introduced scanners that use an algorithm called adaptive statistical iterative reconstruction (ASIR). The technique compares neighboring voxels; if one looks too different, it is assumed to be noise and is removed. So the scanner can use less intense x-rays, which can result in more noisy images. The technique can cut the radiation dose of a CT scan by half.

The new algorithm by Yadava and his colleagues goes one step further. It uses a more realistic physics model of the x-ray source, the detectors, and the x-ray beam. Each of these three is assumed to have specific diameters instead of being considered a point or a line, Yadava says. Depending on the type of scan, the technique is better than ASIR at cutting image noise, and thus the x-rays can be even less intense. The researchers got high-quality abdomen scans of a human model using an eighth of the radiation dose of a conventional scan.

Software techniques are one promising way to reduce radiation, McCollough says. Over the past decade, other techniques have reduced the radiation dose from CT scans–abdomen scans now deliver a third of the dose that they delivered in the 1980s. One major advance was adding multiple detectors to the scanners, she says. The other was to adjust the x-ray intensity depending on the patient’s size and the organ being imaged. This has cut radiation doses by up to 40 percent.

McCollough has developed another dose-reduction technique that involves automatically adjusting the energy spectrum of the x-rays. She says the method could give better quality images for children and thinner adults and use 20 to 40 percent less radiation. Many hospitals do x-ray energy adjustment manually right now, she says, but a major scanner manufacturer is incorporating the automatic technique into its machines.