Quantum Localization Principles Applied to Reduce Noise in Medical Imaging

Medical imaging tools like ultrasound and MRI are invaluable for clinicians, yet they often suffer from background noise, a fundamental limitation. This interference can blur images, obscure fine anatomical details, and complicate accurate diagnosis.

Traditional denoising techniques have been developed to address this issue, but most face challenges with the complexity of noise patterns in medical scans. These approaches also rely on manually adjusting parameters, adding time and difficulty to the imaging workflow.

A team of researchers from Massachusetts General Hospital, Harvard Medical School, Weill Cornell Medicine, GE HealthCare, and Université de Toulouse has introduced a new way to tackle this problem, inspired by the principles of quantum mechanics. Their findings, published in AIP Advances this week, take the analogy between particle behavior and pixel intensity further than previous studies—directly applying the mathematics of quantum physics to image denoising.

“While quantum localization is a well-established phenomenon in physical materials, our key innovation was conceptualizing it for noisy images — translating the physics literally, not just metaphorically,” explained lead author Amirreza Hashemi. “This foundational analogy didn’t exist before. We’re the first to formalize it.”

At the heart of the approach is the concept of localization, a cornerstone in describing how particles vibrate in physical systems. When vibrations remain confined to one area, they are “localized,” while vibrations that spread outward are considered “diffused.” Applying this logic to imaging, the team viewed pixel intensity in clear images as localized, whereas noisy patterns could be considered diffused.

By mathematically translating this principle, the researchers developed an algorithm that separates the signal of anatomical structures from the noise of stray pixels. “The main aspect was developing an algorithm that automatically separates the localized (signal) and nonlocalized (noise) components of pixels in an image by exploiting their distinct behaviors,” Hashemi said.

One of the most notable advantages of this method is that it eliminates the need for manual parameter tuning, a common obstacle in conventional denoising algorithms. “Our method leverages physics-driven principles, like localization and diffusive dynamics, which inherently separate noise from signal without expensive optimization,”Hashemi added. “The algorithm just works by design, avoiding brute-force computations.”

Beyond medical imaging, the team sees potential applications in other fields, including quantum computing. Because the framework is directly aligned with computational processes used in quantum systems, it could deliver performance benefits as quantum technologies mature. “Our physics-driven framework aligns with the computational primitives of quantum systems, offering a potential performance advantage as quantum computing scales,” Hashemi said.

This novel fusion of physics and medicine not only offers a new path toward sharper, more reliable medical images but also demonstrates the power of interdisciplinary approaches. By borrowing concepts from quantum mechanics, the researchers have created a tool that may one day improve patient outcomes while also advancing computational science.