Imaging technique unlocks ‘super-resolution’ brain scans
A new imaging technique could have the potential to detect neurological conditions like Alzheimer’s disease at their earliest stages, enabling physicians to diagnose and treat patients more quickly.
Termed 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 are often limited by unwanted movements of the patient during scanning. But in this study, researchers utilised the super-resolution technique to harness the usually undesirable head motion of subjects to enhance resolution. An external motion tracking device that continuously measured head movement with extremely high precision was used on moving non-human primates.
For reference, static PET scans were also performed without inducing movement. After data from the imaging devices were combined, researchers recovered PET images with noticeably higher resolution than that achieved in the static reference scans.
“This work shows that one can obtain PET images with a resolution that outperforms the scanner’s resolution by making use, counterintuitively perhaps, of usually undesired patient motion,” said Dr Yanis Chemli from the Gordon Center for Medical Imaging in Boston, Massachusetts. “Our technique not only compensates for the negative effects of head motion on PET image quality, but it also leverages the increased sampling information associated with imaging of moving targets to enhance the effective PET resolution.”
While 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 may find applications in diagnosing brain disorders, specifically Alzheimer’s disease.
“Alzheimer’s disease is characterised by the presence of tangles composed of tau protein. These tangles start accumulating very early on in Alzheimer’s disease—sometimes decades before symptoms—in very small regions of the brain. The better we can image these small structures in the brain, the earlier we may be able to diagnose and, perhaps in the future, treat Alzheimer’s disease,” Chemli said.
In 2017, it was found that a person’s ‘brain age’ can be predicted using MRI images and machine learning to help determine patients who might be at increased risk of poor health or early death.
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