Chris Yip 0:02 Welcome to Coffee with Chris Yip, the official podcast of the Faculty of Applied Science and Engineering at the University of Toronto. I'm Chris Yip, the Dean here at U of T engineering. In each episode, I'll be sitting down for coffee with someone from our amazing global community to talk about what they're working on, and how it places us at the heart of bold solutions to design a better world. In this first season, I want to zoom in on "the why?" finding out what drives the curiosity and passion of our extraordinary community. Once you understand that, I hope you'll start to see what makes this place so special, and that you'll be inspired to make, innovate and create along with us. Today on coffee with Chris Yip, have you ever had an MRI? Since its invention in the 1970s magnetic resonance imaging or MRI has become a powerful tool for looking inside the human body without the need for invasive surgery. It's used to diagnose and track conditions from heart disease to cancer but there's a lot more that it can do. Professor Hai-Ling Margaret Cheng and her team are designing the next generation of MRI scans. In the future it can be used to catch cancer in its earliest stages, or track stem cell treatments as they repair damaged tissues. A better window into what's happening inside the body could save lives around the world. Morning, Margaret, how are you? Hai-Ling Margaret Cheng 1:25 I'm doing well, Chris. How are you doing? Chris Yip 1:28 Good. It's great to have you here on the inaugural podcast here and get a chance to catch up and hear all about the cool stuff you're doing in your lab. I know we've talked about wanting to do an MRI of my knee so give me the intro. What is MRI? Hai-Ling Margaret Cheng 1:45 MRI is a really cool technology that has become very prevalent in the past two decades. It's a clinical imaging modality very similar to CT, X-ray and ultrasound and that it's able to get images inside your body in a noninvasive way. But unlike CT, or nuclear medicine, it doesn't involve the use of radioactive tracers and when you compare against all the other imaging modalities that are available, MRI is the only one that gives you the best contrast or signal differences amongst different tissue types in your body. Can I image my heart? Can I image my brain? Can I image cartilage in my body? MRI can do all of that. It can penetrate deep into your body without any depth limitations and provide really highly spatially resolved images, which means that the details are superior. When we talk about imaging function, it could mean different things for different MR physicists. For the neuro imaging people, functional imaging always means imaging brain function so certain parts of the brain getting more blood flow because that part of the brain is activated. But functional MR, functional imaging actually means a lot more than just brain function. It means a function in every part of the body. So when I think about functional MRI, I think about things like how fast is blood flowing to my toe? How much blood is actually going through my liver? It includes things like tissue oxygenation, what is the hemoglobin oxyhaemoglobin level in the blood? Those things are what we consider to be functional. Chris Yip 3:35 Cool. Compared to someone who's had an ultrasound or an X-ray, does it take longer? Or is it faster? Is it noisy? Hai-Ling Margaret Cheng 3:46 Unfortunately, and I'll get into this a little bit more later on in this interview, the time that it takes for an MR exam is longer compared to an X-ray, or a CT scan or ultrasound, but it's not that much longer. Typically, people would have 30 minutes in an MRI scanner if they're imaging the brain or other body parts. If they're doing a cardiovascular exam, that's when the time goes up a little bit, up to an hour or maybe even a little bit more. But when you compare the amount of time it takes to do a CT scan, under 10 minutes, obviously it's a big difference. Ultrasound scans on the other hand, they also take time. Half an hour is not uncommon for a cardiovascular exam. So when we really look at the practical aspects of MR, of course I'm biased, but for the amount of information that MRI provides you it's really an excellent modality. Chris Yip 4:50 It's terrific because we hear a lot now, it's become almost, I mean, it's become commonplace, right? Over the past 50 years there's been lots of improvements. Your work is very much focused in "how do I improve it?" And I guess maybe for our listeners, what are the key areas that MRI imaging needs to be improved on? Hai-Ling Margaret Cheng 5:16 When I think about an MR, as with most people, they think about pretty anatomical images. You can get exquisite detail of the cartilage, cartilage tissue against bone with muscle. And this is the reason why sports injury can only be imaged on MR and not with other modalities, because MR is the only modality that gives you the excellent tissue contrast amongst different tissue types. But I think about MR at a deeper level. I think about MR as a high resolution analog of nuclear imaging, because it can provide functional information, physiological information. And really, the two different capabilities of MR that my lab is working on to advance are, first of all, trying to make it go faster, so that we can have shorter scan times. And the second is to be able to probe at molecular events, functional events that are still below the detection limit today. Because currently, it's not the first modality the doctors will prescribe for a lot of different conditions but as it becomes more and more commonplace, it will become cheaper. And it's like a positive feedback loop. So that is where I would like to bring MR closer to. Chris Yip 6:41 I did a little bit digging in the literature, and I found this cool paper called Bright Ferritin that you published. Maybe you could talk a little bit about that. It looks really cool, the ability to kind of track a cell using the MR system. You're really pushing into a completely different realm. Hai-Ling Margaret Cheng 7:00 So bright ferritin is truly a scientific discovery. It's a mechanism for tracking cells that we discovered by sheer luck. But the technology is so cool that we actually filed a PCT patent on it just last month, April 2021. It is a method for us to be able to image stem cells or any type of therapeutic cells, you can even think about tracking cancer, immune cells, for instance, only for the duration that the cells remain alive. And so once the cells die, the signal will go away even if they start reproducing and dividing into daughter cells and more daughter cells and more daughter cells. So long as the cells remain alive, the signal will be maintained at a constant level on a per cell basis. So this is unlike any other existing cell tracking technology where the signal fades away as cells grow and divide. It's also very sensitive, meaning that we can detect the cells with a high SNR, signal-to-noise ratio. We discovered this really just by being good scientists. My former PhD student, Daniel Scholtes, and I, we basically created a grid of possible factors, experimental variables that we could vary, and we tried out every single permutation. And then we saw this anomaly, why are we seeing a signal with this certain combination of genetic modification and MR contrast agent? We repeated it, same thing again and again and again, in vivo, in vitro, we could not get rid of the signal. And so we dug deeper and basically what we were seeing was the ability of cells to assemble on their own. These metal nanoparticles that give off a bright signal on magnetic resonance imaging. They're harmless, cells are perfectly happy, because these particles also get recycled and eliminated from the cell over the course of several days. I'm so happy with this discovery that we are taking it to investigate stem cell therapy. We're going to be looking at both cardiac and neural stem cell regeneration guided by this new technology. The bright-ferritin technology is really meant for enabling surgeons, radiologists to be able to see the therapeutic cells that they implant or transplant into a patient. The cells could be transplanted for the purpose of treating a myocardial infarct. It might be implanted to treat a spinal cord injury. It could also be transplanted to repair damaged areas of the brain that suffered stroke. Could also be used to track liver cells, kidney cells, you name it. Anytime that people are using therapeutic cells and putting them inside the body for growing new tissue, you can use this technology. Technology can also be used for tracking immune cells like cancer, immune cells for cancer immunotherapy, which is also a really hot new topic these days. Chris Yip 10:21 That's a really cool discovery and it sort of points out investigating something which is anomalous in your data, right? Seeing something that was persistent, and then really digging in deep to figure out where it was coming from. Hai-Ling Margaret Cheng 10:36 I always tell my students, data never lies and we should never look at the data with preconceived hypotheses. Really look at the data for what they're trying to tell us. And I think that's where discoveries can come from. It's good to have expectations but don't be married to the expectations and be prepared to be surprised. Chris Yip 10:57 I noticed there's another there's another author with the same last name on that paper. Is that is that someone related to you? Hai-Ling Margaret Cheng 11:03 So that same author Hai-Ying Mary Chang, that's my younger sister. She is a molecular biologist, but now doing mostly neurobiology. Yes, she is also an inventor on this, because she helped us design the genetic modification to introduce into these cells. Chris Yip 11:23 Cool. Was it fun working with your sister on the project? Hai-Ling Margaret Cheng 11:26 It is she challenges me and I challenged her, she would tell me if things aren't good. If they're not correct, she'll tell me to my face, right? She doesn't need to hold back and her heart is all in. She tries to teach me about biology and I try to teach her about the physical side of things. Despite not completely understanding her biology, or her understanding my physics, in some instances, we do connect, we do understand, and it doesn't have to be a deep understanding, all it takes us that we can see "oh, there is something that we can actually work on together". And so I'm looking forward to more collaborations with her. Chris Yip 12:09 That actually leads me to a very nice segue. A little bit about your path into into biomedical engineering now, because I know your original, your undergrad work, and so on was also in signal processing, but not really in the biology space. Maybe you could give us, give the listeners a little bit of a sense of that trajectory. Hai-Ling Margaret Cheng 12:30 So I'll just step a little bit back, rewind even a little bit more to high school. I was interested in many things, all the sciences, biology, chemistry, physics. I love math. I loved history. I loved every subject in school. I don't know if I should be saying that on the podcast. But when it came time to decide on a discipline to go into for undergraduate studies, my father actually was the one who recommended that I go into engineering because it was a practical discipline. I personally wanted to go into either physics or math. But I listened to my dad, I trusted him. And I thoroughly enjoyed my four years of undergrad in electrical and computer engineering. I subsequently got a master's degree also in the same Department of Electrical and Computer Engineering. After my master's, I decided to take a break. I knew I wanted to get a PhD, but I wasn't quite sure what. Even at that time I wasn't quite sure I wanted to get a PhD in electrical and computer engineering so I worked as an engineer for two years. During those two years, it was very interesting. I was working on real time signal processing for synthetic aperture radar. SARS are technologies that the military and the Canadian government they have on these planes, aircrafts, where they're surveying the land, and getting very high resolution mapping of coastlines and such. And so I was working on that, looking at signal processing theory, writing code for the real time processing and generation of these images. After two years, I don't know, I just felt like I wanted to do something different. I knew I still wanted to do engineering, but it wasn't applied to defense or surveillance. I wanted to do something to help people. And so I talked to my sister, she was already in graduate school at the University of Toronto at that time. And I asked her, I said, What can I as an engineer do that could help people? And she said, why don't you go into medical biophysics? And I listened to her. So I ended up in the medical biophysics program imaging stream at Sunnybrook Hospital in Toronto, and that's where I spent the next four and a half years doing my PhD. At the time I interviewed with all the profs, you know, the X-ray profs, the CT, ultrasound, MR, MRI was the only modality that interested me. Because it was new, it was spunky, not a lot of people knew anything about it and I felt that because it was so green, I could make a contribution. I fell in love with the research and I knew I wanted to stay a researcher for the rest of my life. And so I spent the next 11 years working at the Hospital for Sick Children, as an MR physicist. It wasn't until 2014, when I came to U of T, that I was kind of, you know, returning back to my engineering roots, Chris Yip 15:38 It's always great to hear and find out how people went on their career path, right? How you went through kind of that classic stream and then the industry experience or the real world experience and then realize how you could apply that into a different space and then pursuing kind of a passion, which I think is always a terrific opportunity. Of course, it was great to recruit you into U of T back in 2014 into biomedical engineering and also into electrical and computer engineering. Time for a couple of off the wall questions. We were trading emails over Chinese New Year, we were trading this giant thread and and I don't even know what started the conversation but there was a whole bunch of us on that thread. And someone was sharing pictures of their dining table and all the food that was being set up for Chinese New Year. Hai-Ling Margaret Cheng 16:36 That was me. (laughing) Chris Yip 16:38 Yeah. So come on, spill the beans. Tell us what was on that table and what is your favorite food for Chinese New Year? Hai-Ling Margaret Cheng 16:47 So on that table, we had our traditional Chinese New Year dishes. White cut chicken, which is simply boiled chicken and you dip it in soy sauce. There was fresh steamed fish with scallion and ginger. There was also something like a minced pork-filled omelet with mung bean vermicelli and napa cabbage in chicken soup, it's a Shanghainese specialty and we we eat it. It's like eating golden nuggets, right? It's good fortune for the rest of the year. Shanghai bok choy with soybean sprouts, mixed with tofu pockets. It's all lucky according to Chinese, like if you say it in Chinese, it sounds really prosperous and fortuitous. What else did we have? I can't remember. There was a lot of food. So what I remember the picture was that one, there was a lot of food too. Second, you had cooked it all. I think it was all home made, home cooked so I think this is going to start the next kind of U of T Eng Dean kind of Instagram thing, which is going to be a bit of a cooking competition between faculty to see who can pull off the best festive meal. I'm going to make the usual Chris non sequitur connections and ask you, MR for food - have there been applications of MR imaging for food? And could we now start to apply this for when you know your noodles are ideally cooked? I don't know if I'll be passing dishes of Chinese food through the MR scanner but I could tell you that we can maybe sort a bag of apples and find if there are worms in them. And you can toss out the bad ones from the good ones. Chris Yip 18:43 Let me thank you, Margaret. This has been an absolutely phenomenal discussion today to hear all about your research, which is very cool. And also to hear how your career's gone, and how you started in the double E space, and how you've now moved into this really cool interdisciplinary space of signal processing. As we're doing a podcast and I'm thinking about the waveform going across here, and you analyze in a waveform and now applying it right into biology and the healthcare space. So again, thank you so much for taking the time to be here on the first, the pilot podcast and look forward to chatting more, look forward to getting my MR of my knee at some point. Hai-Ling Margaret Cheng 19:25 Okay. Promise you that, Chris. Chris Yip 19:28 Terrific. Thanks, Margaret. Hai-Ling Margaret Cheng 19:30 Thank you very much. Chris Yip 19:33 Thanks again for listening to Coffee with Chris Yip. If you want to catch up on past episodes or make sure that you don't miss the next one, please subscribe. We're on SoundCloud, Apple Podcasts, Spotify, and more. Just look for Coffee with Chris Yip. You can also check out @UofTEngineering on Twitter, Facebook, Instagram and LinkedIn for more stories about how our community is building a better world. And finally, if you've been inspired to join us, we'd love to welcome you. Whether you're thinking of taking a degree or working with us on a research project you can find us online at engineering.utoronto.ca or you can visit our beautiful campus in Toronto, Ontario, Canada. I hope I can join you for coffee soon.