A new imaging technique could have the potential to detect neurological disorders such as Alzheimer’s disease at an early stage, allowing doctors to diagnose and treat patients more quickly.
Called super-resolution, the imaging technique combines position emission tomography (PET) with an external motion tracking device to generate highly detailed images of the brain.
The quality of PET scans is often limited by unwanted patient movements during scanning. But in this study, researchers used the super-resolution technique to exploit subjects’ usually unwanted head movement to improve resolution.
In moving non-human primates, an external motion tracking device was used that continuously measures head movements with extremely high precision.
For reference, static PET scans were also performed without inducing movement. After the data from the imaging equipment was combined, the researchers recovered PET images at a noticeably higher resolution than the static reference scans.
“This work demonstrates that one can obtain PET images at resolution exceeding that of the scanner by using, perhaps counterintuitively, commonly unwanted patient movements,” said Dr. Yanis Chemli of the Gordon Center for Medical Imaging in Boston, Massachusetts.
“Our technique not only compensates for the negative effects of head movements on PET image quality, but also takes advantage of the increased sampling information associated with imaging moving targets to improve effective PET resolution.
” Although this super-resolution technique has only been tested in preclinical studies, researchers are currently working on extending it to human subjects.
The researchers believe that the super-resolution imaging technique could find applications in diagnosing brain disorders, particularly Alzheimer’s disease. “Alzheimer’s disease is characterized by the presence of tangles composed of tau protein.
These tangles start to build up very early in Alzheimer’s disease — sometimes decades before symptoms — in very small areas of the brain.
The better we can imagine these tiny structures in the brain, the sooner we can diagnose Alzheimer’s disease and perhaps treat it in the future,” Chemli said.
In 2017, it was discovered that a person’s ‘brain age’ can be predicted with using MRI images and machine learning to help determine patients who are at increased risk of ill health or premature death.