New MR to perform hi-res brain scans in 15 minutes or less

A research team is developing an MR scanner that can perform exams in around 15 minutes and produce higher- resolution images

January 07, 2019
by John R. Fischer , Staff Reporter
Lying still during MR procedures may soon require less of a time commitment.

Researchers from the College of Engineering at the University of Arizona have set to work developing a machine that cuts scanning time to around 15 minutes or less for patients with conditions that make it difficult to remain still, such as Parkinson’s disease. The machine will also produce higher-resolution images that provide more details on the stage of a disease and may pave the way to earlier diagnoses.

“If patients are unable to remain still for an extended period of time to complete the required MR exam, then they might be sedated or anesthetized so that high-quality MR data can be acquired,” Nan-kuei Chen, principal investigator and associate professor in the UA Department of Biomedical Engineering, told HCB News. “However, the sedation and anesthesia are not risk free; the potential adverse effects include oxygen desaturation, respiratory distress, motor imbalance, hypotension, injury to arteries, veins or nerves, and post-anesthetic delirium among many others.”

In the case of Parkinson’s, pre-motor stages, such as rapid eye movement sleep behavior disorder or a reduced sense of smell, commonly begin 10-15 years before a formal diagnosis is made, at which time 60-70 percent of dopamine-releasing neurons have already been damaged, limiting available treatment options.

The team plans to address this issue with the integration of multiplexed sensitivity encoded MR (MUSE), which will reduce motion artifacts and inconsistencies created when the subject moves, as well as incorporate multiple types of MR data such as iron levels, gray matter volume and white matter volume. It also will include a “denoising” process to reduce noise-related errors in long-exposure images by up to 90 percent.

Another addition will be diffusion tensor imaging technology developed by Chen, who, along with his team, has alternated between scans that move from the top down and ones that move from the bottom up to cancel out the distortions, the presence of which results in geometrically warped images due to the asymmetric scanning pattern used by the equipment.

In addition, the researchers are currently working to develop new MR data acquisition and reconstruction strategies for high-speed and high-resolution MR, including more efficient MR acquisition pulse sequence and improved reconstruction algorithms.

The project is expected to be completed over a five-year period with the first phase utilizing transcranial magnetic stimulation to validate the efficiency of the methods on new patients and validating the accuracy of findings. During this time, Chen and his team will evaluate and compare data collected from patients at high risk of developing Parkinson’s to those in the early and late stages of the disease.

A longitudinal study will then take place in the second and fifth year of the project, in which researchers will evaluate the progression of the disease in high-risk patients and assess the accuracy of images obtained from patients with Parkinson’s to images of healthy subjects.

“If the tests show that our fast MR strategies can successfully produce high-quality data, then we plan to further optimize the parameters to answer important research questions from challenging subject populations,” said Chen. “For example, we would like to detect subtle brain signal abnormalities in Parkinson's patients, in the early stages of the disease, using high-speed and high-resolution MR. We plan to acquire high-resolution functional MR data, reflecting neurological development, in children with and without developmental disorders.”

The techniques of the machine can also be applied to assess other neurological conditions, from Alzheimer’s disease to cerebral palsy, offering support in their diagnosis, treatment and evaluation of treatment.

The project is funded through a $2.1 million grant from the National Institute of Neurological Disorders and Stroke.

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