Lara Pinar

When someone walks into an emergency room with symptoms of a stroke, every second matters. But today, diagnosing the type of stroke, the life-or-death distinction between a clot and a bleed, requires large, stationary machines like CT scanners that may not be available everywhere. In ambulances, rural clinics, and many hospitals worldwide, doctors often have no way to make this determination in time.

For years, scientists have imagined a different world, one in which a lightweight microwave imaging device, no bigger than a bike helmet, could allow clinicians to look inside the head without radiation, without a shielded room, and without waiting. That idea isn’t far-fetched. Microwave imaging technology already exists and can detect changes in the electrical properties of tissues — changes that happen when stroke, swelling, or tumors disrupt the brain’s normal structure.

The real obstacle has always been speed. “The hardware can be portable,” said Stephen Kim, a Research Professor in the Department of Biomedical Engineering at NYU Tandon. “But the computations needed to turn the raw microwave data into an actual image have been far too slow. You can’t wait up to an hour to know if someone is having a hemorrhagic stroke.”

Kim, along with BME Ph.D. student Lara Pinar and Department Chair Andreas Hielscher, believes that barrier may now be disappearing. In a new study published in IEEE Transactions on Computational Imaging, the team describes an innovative algorithm that reconstructs microwave images 10 to 30 times faster than the best existing methods, a leap that could bring real-time microwave imaging from theory into practice.

It’s a breakthrough that didn’t come from building new devices or designing faster hardware, but from rethinking the mathematics behind the imaging itself. Kim recalls spending long nights in the lab watching microwave reconstructions crawl along frame by frame. “You could almost hear the computer groan,” he said. “It was like trying to push a boulder uphill. We knew there had to be a better way.”

At the heart of the problem is how traditional algorithms work. They repeatedly try to “guess” the electrical properties of the tissue, check whether that guess explains the measured microwave signals, and adjust the guess again. This is a tedious process that can require solving large electromagnetic equations hundreds of times.

The team’s new method takes a different path. Instead of demanding a perfectly accurate intermediate solution at every iteration, their algorithm allows quick, imperfect approximations early on and tightens the accuracy only as needed. This shift, which is simple in concept, but powerful in practice, dramatically reduces the number of heavy computations.

To make the process even more efficient, the team incorporated several clever tricks: using a compact mathematical representation to shrink the size of the problem, streamlining how updates are computed, and using a modeling approach that remains stable even for complex head shapes.

The results are striking. Reconstructions that once took nearly an hour now appear in under 40 seconds. In tests with real experimental data, including cylindrical targets imaged using a microwave scanner from the University of Manitoba, the method consistently delivered high-quality results in seconds instead of minutes.

For Kim and Hielscher, who have worked collaboratively for decades on optical and microwave imaging techniques, the speed improvement feels like a long-awaited turning point. “We always knew microwave imaging had the potential to be portable and affordable. But without rapid reconstruction, the technology couldn’t make the leap into real clinical settings,” Hielscher said. “Now we’re finally closing that gap.”

The promise extends far beyond stroke detection. Portable microwave devices could one day provide an accessible alternative to mammography in low-resource settings, monitor brain swelling in intensive care units without repeated CT scans, or track tumor responses to therapy by observing subtle changes in tissue composition.

The team is now focused on extending the algorithm to full 3D imaging, a step that would bring microwave tomography even closer to practical deployment. But the momentum is palpable. “We’re taking a technology that has been stuck in the lab for years and giving it the speed it needs to matter clinically,” Kim said. “That’s the part that excites us: imagining how many patients someday might benefit from this.”

Accelerated Microwave Tomographic Imaging with a PDE-Constrained Optimization Method, IEEE Transactions on Computational Imaging, VOL. 11, 1614 – 1629 (2025), Authors: Stephen H. Kim, Lara Pinar, and Andreas H. Hielscher.