‘We’ve designed complementary metamaterials that will make it easier for medical professionals to use ultrasound for diagnostic or therapeutic applications, such as monitoring blood flow in the brain or to treat brain tumours,’ said Tarry Chen Shen, a Ph.D. student at NC State and lead author of a paper on the work. ‘This has been difficult in the past because the skull distorts the ultrasound’s acoustic field.’
‘These metamaterials could also be used in industrial settings,’ said Dr. Yun Jing, an assistant professor of mechanical and aerospace engineering at NC State and senior author of the paper. ‘For example, it would allow you to use ultrasound to detect cracks in airplane wings under the wing’s metal ‘skin.’’
Ultrasound imaging works by emitting high frequency acoustic waves. When those waves rebound off an object, they return to the ultrasound equipment, which translates the waves into an image.
Some materials such as bone or metal have physical characteristics that block or distort ultrasound’s acoustic waves and these materials are called aberrating layers.
The researchers are said to have addressed this problem by designing customised metamaterial structures that take into account the acoustic properties of the aberrating layer and offsetting them. The metamaterial structure uses a series of membranes and small tubes to achieve the desired acoustic characteristics.
The researchers have tested the technique using computer simulations and are in the process of developing and testing a physical prototype.
In simulations, 28 per cent of ultrasound wave energy makes it past an aberrating layer of bone when the metamaterial structure is not in place. With the metamaterial structure, the simulation shows that 88 per cent of ultrasound wave energy passes through the aberrating layer.
‘In effect, it’s as if the aberrating layer isn’t even there,’ Jing said in a statement.
The paper, ‘An Anisotropic Complementary Acoustic Metamaterial for Cancelling out Aberrating Layers,’ is published online in Physical Review X. The paper was co-authored by Drs. Jun Xu and Nicholas Fang at MIT.