Below is a transcript of our conversation with Drs Yeon Kim and Hiruni Pallage:
David Nicholas:
I'm David Nicholas and this is Central Focus, a weekly look at research activity and innovative work from Central Michigan University students and faculty. MRI technology is currently the most effective form of medical imaging and among its many uses, images of the brain are the most complex. Dr Hiruni Pallage is an Assistant Professor of mathematics at Centre College in Danville, KY. She earned her doctorate from CMU, and she worked with Professor Yeon Kim on an applied mathematics study. Could the number of images be increased and the time inside the machine be decreased by using math? Dr Pallage spoke with me via Zoom and Professor Kim joined me in studio…
Yeon Kim:
The brain system has (has) the fantastically complex system, but doctors need to see how these brain fibers are arranged to diagnose tumors or abnormalities, and that's why we need this advanced imaging techniques and mathematics.
DN:
The goal to increase the number of images and yet at the same time try to limit the time that the procedure takes to make a patient more comfortable with that, how then does math come into play in terms of the application or (or) being able to do both of those things?
Hiruni Pallage:
If you think of a ball, you need a lot of signal like 700 ish measured on different directions on that ball. But if you try to do that by keeping your patient in the machine, then you'll literally cook your patient, which is not feasible. So that's when the mathematics come into play where you say, OK, we don't, we are not measuring 700. Instead, let's measure 100 and then we apply mathematical techniques to predict the other signals at other 600 directions and then now I have measured 100 and the predicted 600. Then again there's another mathematical where we use all those signals to stretch the ball. Just think about stretching a bread dough. You are literally stretching your ball to get a specific shape and that specific shape will tell you how your fibers are arranged at a specific location in the brain. You get that stretched picture. Yes, we are using mathematics and to predict the missing signals. Yes, we are using mathematics.
YK:
You know, I would like to add a little bit there. The, this, (this) ball is a is a replica is representing the water molecule and 70% of body our body has this waters. The MRI signals stretched this water molecules in certain direction depend on this how this drivers are arranged.
DN:
Is it something that you see as something that can adapt when (when) the next stage or phase of MRI technology or potentially something that is even slightly different from the MRI procedure? Do you think that this research you're doing now can continue to grow with that?
YK:
If (if) the MRI machine gives us more or other machine gives better or high quality of data, then absolutely our (our) mathematical method can adapt those achievements with the better and more data. The accuracy and efficiency will improve currently without we know we need this MRI data signal. I wish we can get various signals from other techniques if we (if we) have.
HP:
When we get like more advanced data or more accurate data together with our mathematical methods, well, yes, we can improve the precision of the images. And the efficiency, while keeping up with the technological progress and this will make like the scans faster, reducing machine usage times and cost. Making MRI more affordable and accessible in resource limited settings. Then we can expand this research to serve more populations in our society. So that's something that we can look into.
DN:
Doctor Pallage, we appreciate your time. Assistant Professor of Mathematics at Centre College in Danville, KY, and to you, Professor Yeon Kim in the mathematics department here at Central Michigan University. Congratulations on the findings thus far and good luck as the research continues. We do appreciate your time very much.
YK:
Thank you. It is very exciting to see how math and technology can come together, push the boundaries of this medical imaging.
(HP: Thank you.)
HP:
Thank you so much for this opportunity to share our findings and research. Thank you so much!
