Brain imaging technique allows researchers to achieve more with less data
A Johns Hopkins team has developed a new algorithm that can create ‘super-scans’ of the brain
Magnetic resonance imaging (MRI) uses magnetic fields to create images of the body that allow doctors to diagnose injury or illness more accurately. Susceptibility tensor imaging (STI), a specialized MRI technique, measures the magnetic susceptibility of different tissues in the brain by quantifying how they become magnetized when exposed to the MRI scanner’s magnetic field. Researchers and physicians can use such information to better understand, diagnose, and monitor neurological diseases such as multiple sclerosis (MS) and Alzheimer’s disease.
Johns Hopkins researchers have recently published papers describing a new algorithm, DeepSTI, that takes data from multiple individual scans and provides a “super-scan” of the brain that includes precise brain tissue susceptibility information. Their method requires fewer images taken in fewer positions compared to traditional STI, making the process faster and more pleasant for patients.
“Usually, STI imaging requires at least six different scans at different head orientations to achieve a good reconstruction, and that’s mainly why it’s not currently broadly used despite its potential to understand the human brain,” said senior author Jeremias Sulam, an assistant professor of biomedical engineering. “Our AI-assisted reconstructions greatly expand the amount of useful information that can be gleaned while requiring much less data, and we hope that will help move this imaging technique from lab to clinic.”
The team’s results appeared in Medical Image Analysis, and in the proceedings of the 2023 International Workshop on Machine Learning in Clinical Neuroimaging.
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