A $500,000 National Science Foundation (NSF) grant will help Chunqi Qian, PhD, at Michigan State University College of Osteopathic Medicine’s (MSUCOM) Radiology Department, to develop a compact wireless multi-modal detector array for remote sensing and imaging. While the development would have many applications, one of the potential outcomes will be improved technologies for Magnetic Resonance Imaging (MRI).
The project is focused on developing a compact implantable Self-Oscillation Encoding Telemeter (SET) sensor “capable of simultaneously encoding both neuronal voltages and MRI images for sensitivity-enhanced neuronal recording and imaging,” Qian said.
Application of the NSF grant-funded research has potential benefits for healthcare, which could improve the ability for diagnosis. Qian’s work will “concatenate multiple SETs into a planar array, enabling high-resolution mapping of micro-vessel distribution along the brain cortex, while also aligning multiple miniaturized SETs along an insertion catheter in order to reveal the underlying correlation between MRI and neuronal signals with depth-resolved specificity.” Finally, to extend SET sensors’ capabilities even further, he will “multiplex individual sensors with multiple sensing modalities for environmental monitoring.”
Today, conventional imaging techniques can detect imaging signal changes during brain function, but they cannot locate the specific point of the brain doing the neuronal firing, Qian explained. “If we can combine electrophysiology into imaging then we can probe where the neurons are firing.”
In simple terms, patients can get MRI images or electrophysiology signals separately, but Qian wants to provide a convenient way to get them simultaneously inside an MRI scanner without complicated added-on apparatus. For example, when the brain is functioning, signal dynamic changes can be seen through MRI images, but there isn’t a way to tell if the signal dynamics comes from real brain function, he explained. Electrophysiology is needed for validation.
For patients who have a neurological disease, like Parkinson’s disease or Epilepsy, the MRI could be used to locate normal brain function. “Conventional function of brain imaging is based primarily on blood oxygenation,” Qian explained. “If we can add an additional dimension like electrophysiology recording inside a scanner, we can more precisely locate, for example, epilepsy foci.”
However, due to the interfering environment inside the imaging scanner, the performance of conventional electrophysiology is poor. The integrated wireless sensing device Qian is developing would avoid such interference.
Today, wired connections to electrodes are needed in conventional electrophysiology, but wired connections inside the MRI scanner can be problematic due to the electromagnetic interference. While there are techniques to synchronize the electrophysiology recording with MRI imaging, these techniques are complicated and have not been utilized clinically due to device complexity and safety concerns, such as the potential problem of overheating if wires are inside the MRI scanner.
“With my technology, we propose to use an integrated sensor that can transmit a signal wirelessly and doesn’t need additional wires for signal transduction,” Qian said. “We just need to use the sensor that can encode the electrophysiology signal onto its wireless carrier and this wireless carrier is then received by the same MRI receiver.”
Qian’s research is not limited only to integrated imaging and sensing as used in healthcare. The fundamental question his work will answer is how to combine detection of frequencies of a broad range from kilohertz range to megahertz range and how they can be integrated and coded into a safe carrier grade for wireless remote sensing transmission.
“I am proposing that my technology will not only benefit the imaging field, but also more industrial applications in general,” Qian said.
This NSF grant will help Qian continue his research to develop advanced detection technologies for MRI. Work on the NSF project will begin July 1, with a completion date of June 30, 2027.
[Source: Michigan State University]