Showing posts with label biomedical. Show all posts
Showing posts with label biomedical. Show all posts

Thursday, September 22, 2011

Biomedical Innovation: Ultrasound used for better Brain Computer Interface



Ultrasound is good for more than monitoring fetuses and identifying heart defects. According to engineers in Canada, it can help tell what people are thinking as well. Their research suggests that ultrasound-based devices could lead to a new kind of brain-computer interface.
Brain-computer interface technology allows users to control devices with brain activity alone. Researchers have focused primarily on clinical applications for people with severe disabilities who would otherwise have difficulty interacting with the outside world.
In addition to brain-computer interfaces that involve electronics inserted directly into a patient’s head, researchers are also developing a number of noninvasive methods. For instance, electroencephalography (EEG) relies on electrodes attached to a person’s head; functional magnetic resonance imaging (fMRI) uses powerful magnetic fields to measure blood flow in the brain that telegraphs brain activity;magnetoencephalography (MEG) detects the magnetic fields generated by clusters of thousands of neurons; and near-infrared spectroscopy (NIRS) uses light to scan for changes in blood hemoglobin concentrations.
Yet practical use of these methods has so far been limited due to a number of drawbacks. For instance, EEG faces “noise” from electrical signals sent by the muscles and eyes; fMRI and MEG are very expensive and require large equipment; and NIRS, while still early in development as a brain-computer interface technology, has a low data-transmission rate.
Now biomedical engineer Tom Chau and his colleagues at the University of Toronto reveal that ultrasound can also monitor brain activity, suggesting that it could be used for brain-computer interfaces.
The researchers used lightweight ultrasound headgear to measure blood flow in the brains of nine able-bodied adults as they alternated between relaxing and performing two mental tasks. One task required them to think of words that began with a letter displayed on a video screen, and the other asked them to compare two objects rotated to different angles and determine whether they were the same object or mirror images of each other.
Using this new technique, researchers could see with 82.9 percent accuracy whether people were performing the word-generation task and they could tell with 85.7 percent accuracy if they were doing the mental-rotation tasks or just relaxing. Word generation usually led to an increase in blood flow in the left middle cerebral artery, while mental rotation caused increases in both the left and right middle cerebral arteries.
Myrden, Chau, and their colleagues reported their findings online on 7 September in the journal PLoS ONE.
With some adjustments, results could be even more accurate, Chau says. For instance, if subjects had time to train in these exercises, or if they were given instantaneous visual or auditory feedback during the tasks, they could better adjust their efforts toward their goals.
“The results reported are encouraging,” says Rajesh Rao, a computational neuroscientist at the University of Washington, in Seattle, who did not take part in this study. “The approach offers a potentially cheaper alternative to more traditional EEG- and fMRI-based noninvasive brain-computer interfacing.”
With this approach, it can take as long as 45 seconds before researchers can tell what participants are concentrating on. However, Chau notes that they could speed this up by improving the algorithms used to recognize each task in the brain.
There are also other problems to solve. There are only a few spots on the skull thin enough for the ultrasound to scan through, which limits the amount of brain activity it can monitor. Also, Chau says, “our measurements reflect changes in blood-flow velocity secondary to neural activity in the brain—it will thus always be slower than, say, the detection of electrical activity due to neuronal firing.”
Still, even if it takes 10 seconds for an ultrasound scanner to register what a patient is thinking, it may be worthwhile for someone who is severely disabled, Chau says. The researchers are now looking into combining ultrasound with NIRS to achieve greater speed, accuracy, and detail in monitoring brain activity than either approach could provide alone.
“We hope to soon apply this brain-control interface to the target population—individuals with severe and multiple disabilities,” says Andrew Myrden, a graduate student at the University of Toronto who led the research.
courtesy : biomedikal.in

Saturday, September 10, 2011

Imaging algorithm monitors bone growth



A new image-processing algorithm could improve the treatment of babies born with craniosynostosis, a condition that causes the bone plates in the skull to fuse too soon after birth. However, Chris Hermann, a PhD student in the Coulter Department of Biomedical Engineering at Georgia Tech (Atlanta, GA, USA), has developed imaging software that can monitor this bone growth by analyzing computed tomography (CT) images of the skull.
Craniosynostosis affects approximately one in every 2500 babies in the United States. The condition is caused by the premature closure of sutures -- a type of fibrous joint that only occurs in the skull -- with bone. While sutures provide an important means for the skull to increase in size, if the sutures close too soon after birth and get replaced with bony tissue, the restricted size of the skull could limit the expansion of the brain.
Treating this condition typically requires surgery to remove portions of the fused skull bones, break them up, reposition them, and hold them in place with plates and screws. This procedure usually slows bone growth between the bone pieces, allowing room for expansion of the brain. In some cases, though, the bones grow together again too quickly and additional surgery is required.
Hermann's software identifies bone in the CT images, then quantifies the distance between the bones, the mass of bone in the gap, and the area and volume of the gap.
The software algorithm has already been used to analyze images of developing skulls in mice, where it was able to monitor different types and speeds of bone growth on a daily basis for many weeks in the biomedical research experiment. Now, it is planned to be developed further to help children suffering from craniosynostosis.
-- Courtesy Vision Systems Design