In particular, the algorithm uses the first scan to predict the likely position of the boundaries between different types of tissue in the subsequent contrast scans. "Given the data from one contrast, it gives you a certain likelihood that a particular edge, say the periphery of the brain or the edges that confine different compartments inside the brain, will be in the same place," Adalsteinsson says.
However, the algorithm cannot impose too much information from the first scan onto the subsequent ones, Goyal says, as this would risk losing the unique tissue features revealed by the different contrasts. "You don't want to presuppose too much," he says. "So you don't assume, for example, that the bright-and-dark pattern from one image will be replicated in the next image, because in fact those kinds of dark and light patterns are often reversed, and can reveal completely different tissue properties."
So for each pixel, the algorithm calculates what new information it needs to construct the image, and what information such as the edges of different types of tissue it can take from the previous scans, says graduate student and first author Berkin Bilgic.
The result is an MRI scan that is three times quicker to complete, cutting the time patients spend in the machine from 45 to 15 minutes. This faster scan time does have a slight impact on image quality, Bilgic admits, but it is much better than competing algorithms.
The team is now working to further improve the algorithm by speeding up the time it takes to process the raw image data into a final scan that can be analyzed by clinicians, once the patient has
|Contact: Caroline McCall|
Massachusetts Institute of Technology