Grant could enable higher definition CT scans at lower radiation doses

An image slice from a lung CT scan done using the new "model-based image reconstruction" technique, which could enable images with finer resolution at current X-ray doses, or with today’s resolution, but using one-fourth of the X-ray radiation. Credit: Jeffrey Fessler.
Click above image for higher resolution.

ANN ARBOR, Mich.—Improving the image quality of lower-dose CT scans for diagnosing and monitoring lung disease is the main goal of a $1.9-million grant that the University of Michigan and GE Global Research have received from the National Institutes of Health.

CT stands for computed tomography, an imaging technique that X-rays the body in slices and assembles the data into a three-dimensional picture. The scans are used to find tumors, complex fractures, blood clots, emphysema and clogged coronary arteries, among numerous other conditions.

Modern scanners with multiple detector rows can collect full data sets in seconds. Increased use of CT imaging has led to increased radiation exposure, however.

"You want to expose people to as little radiation as possible," said Jeff Fessler, a professor in the Department of Electrical Engineering and Computer Science. "This is especially true for sick people who need repeated scans."

Fessler is the principal investigator on this project, and will collaborate with radiologists in the U-M Health System and scientists at GE’s Global Research Center in Niskayuna, NY.

The project will focus on sophisticated algorithms that can squeeze more information out of the X-ray data.

A conventional CT scan takes pictures of 64 cross-sections of the body at a time, each less than 1 millimeter thick. A typical scan of the head would produce 6,000 snapshots along one long ribbon, Fessler said.

"It’s an enormous amount of data," he said. "And the raw data is uninterpretable by doctors. We need image reconstruction software under the hood of the scanner, so to speak, to produce meaningful pictures."

CT scans were invented in the 1970s, but the math used now in their image processing algorithms datesback to 1917.

"These algorithms are used because they are relatively simple and fast," Fessler said. "But they don’t extract the most from the data that is theoretically possible."

The collaborating teams have demonstrated that their techniques have the potential to generate comparable image quality with a quarter of the present radiation exposure. In this project, they will conduct broader trials. And they will work on speeding up the processing to make it practical for busy clinics.

This method could also enable higher resolution images, illuminating the fine details of very small lung airways. While this project focuses on lung scans, these techniques could be expanded to all CT scans.

Fessler is also a professor in the departments of Biomedical Engineering and Radiology.

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