Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

Interpolated Data for Speedy Cancer Detection

July 8th, 2011 / in Research News / by Erwin Gianchandani

Magnetic resonance imaging is an attractive tool for detecting breast cancer, but its slow speed and poor resolution limit its viability. With hardware and software improvements, those problems are fixable: on the hardware end, multiple coils arranged in an array produce more data with less noise. Combined with an interpolation algorithm, that large amount of data can be rapidly processed to better serve patients:

To create sharp images quickly, Kyung Sung, a researcher working with [Brian Hargreaves, principal investigator and assistant professor of radiology at Stanford], has developed a process that does more with less information. A conventional breast MRI scan takes up to an hour because of the large amount of data collected to create a scan. “We collect little bits of information in a special way that enables us to make images as though we had collected more details at each point,” Hargreaves explains. Using specially designed software, a computer “builds” the final image from a series of sparse signals collected from the body. Each successive signal layer fills in the final image with help from the computer program. The result is a sharp image created with a fraction of the data used for conventional breast MRI scans. The Stanford process is similar to filming a movie with a very fast, but low-pixel camera. The computer program then transforms the movie so that it is indistinguishable from one taken with a slower, high pixel or “HD” camera, but acquired at the much higher speed. “The more we know about enhancement patterns in normal tissue and tumors, the more we can use this information to sharpen the image,” Hargreaves says.

A smart solution to a practical problem.

To learn more, visit the full press release from our colleagues at the NIH.

(Contributed by Max ChoEben Tisdale Fellow, CRA)

Interpolated Data for Speedy Cancer Detection

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