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Danish researchers develop new cancer scanning method

By Ryan Maass
Danish researchers develop new cancer scanning method
The Danish research team combined computing tomography with magnetic resonance imaging to develop their new cancer scanning technique. Photo by Aarhus University.

AARHUS, Denmark, Oct. 21 (UPI) -- Danish researchers have created a new cancer imaging technique scientists say can pave the way for the development of more effective treatments.

The researchers developed the method by combining two existing approaches, namely magnetic resonance imaging and computed tomography. Investigators used new techniques to observe the development of tumors in mice.

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By using both MRI and CT scans, the research team was able to view images at a higher resolution, resulting in a more accurate analysis. The findings were published in the journal Microvascular Research.

"We can lay two images of the same cancer tumor on top of each other so to speak, so we get a more geometrically complex understanding of the individual tumor's blood vessels, and thereby an opportunity to very precisely study the way drugs are distributed," author Jens Vinge Nygaard explained in a press release.

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Traditionally, an MRI scan is used to show how a cancer-inhibiting drug is distributed inside of a tumor. However, the images are produced in a raw resolution. CT scanners are able to provide more detailed images of blood vessels, but fail to provide details on how drugs move throughout the body. By using both at once, scientists say they are afforded both advantages.

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"The new images give us an opportunity to follow the way a tracer travels through the blood vessels in the tumour and into the surrounding tissue to the cancer cells," researcher Thomas Rea Wittenborn said. "As scientists, we're interested in mapping the size and branching of the blood vessels, and understanding what goes on between the blood vessels over time."

The researchers plan to continue their experiments using mice, and hope to develop new computer models that can describe drug distribution in addition to generating growth scenarios for tumors.

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