Developing a 15-minute MR for scanning challenging patients

Developing a 15-minute MR for scanning challenging patients

October 01, 2019
MRI
From the October 2019 issue of HealthCare Business News magazine

By: Nan-kuei Chen

It’s difficult for even the healthiest patients to lie perfectly still in an MR machine for an hour.
But for some patient populations, such as children, those with cognitive impairments or individuals suffering from the tremors that come with Parkinson’s disease, it’s practically impossible.

Using more than $2.1 million in funding from the National Institute of Neurological Disorders and Stroke, I’m leading a team of University of Arizona researchers in developing faster MR technologies to accommodate challenging patient populations. Scan times will be reduced from about 40 minutes or an hour to around 15 minutes or shorter, depending on a patient’s condition.

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This challenge is not easily addressed by simply tweaking conventional MR protocols. Our research aims to provide higher-resolution images with richer information, giving health care practitioners more information about the stage of a disease. These methods may even be able to detect brain signal abnormality before a disease like Parkinson’s is diagnosed.

The pre-motor stages of Parkinson’s, which can include rapid eye movement sleep behavior disorder and hyposmia, or a reduced sense of smell, often begin setting in 10 to 15 years before the disease is diagnosed. Dopamine deficiency is the cause of most Parkinson’s symptoms, and by the time of diagnosis, 60 to 70 percent of dopamine-releasing neurons are already damaged, so treatment options are limited. With earlier detection, we may be able to delay the disease’s progress.

Taking MUSE one step further
The technique builds upon the multiplexed sensitivity encoded, or MUSE, MR I invented when working at Duke University from 2007 to 2016. MUSE improved upon traditional diffusion-weighted MR scan methods, which had voxels, or 3D pixels, that were too large to allow us to see certain subregions of the brain. After moving to the University of Arizona College of Engineering in 2016, I expanded my research, including learning how to translate MR technologies to study neurological diseases.

One of the main challenges in translating these technologies to a patient population is that they must address several limitations of existing technology at once, including limited resolution, susceptibility to motion artifact and the length of scan time. Our interdisciplinary work has been facilitated by my membership in the UA BIO5 Institute, a platform uniting researchers working in agriculture, engineering, medicine, pharmacy and science to find solutions to biology-based challenges, including the treatment of diseases.

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