Clear imaging deep inside the living brain has traditionally required advanced optical systems and precise correction techniques. A research team from our department has developed a physics-based AI computational algorithm that can restore blurred biological microscopy images more clearly without additional wavefront measurement hardware.
Professor Iksung Kang (School of Electrical Engineering), in collaboration with Professor Na Ji’s research team at UC Berkeley, has developed a technology that accurately corrects image aberrations in microscopes used for live biological imaging. The experimental design and algorithm development – the core components of this work – were led by Professor Kang during his postdoctoral fellowship in Professor Na Ji’s group. This method uses neural fields, a neural network-based approach that represents 3D spatial structures continuously to reconstruct clearer images and volumetric information.
The research team utilized two-photon fluorescence microscopy, a key technique for observing deep inside living biological tissue. This method generates fluorescence when two photons are absorbed nearly simultaneously, enabling localized imaging within biological samples. However, as light passes through thick tissue, differences in refractive index distort the optical wavefront, causing the image to become blurred, much like how objects appear distorted underwater. This phenomenon is known as optical aberration, in which wavefront distortions degrade the focus and clarity of an image.
Previously, correcting these distortions required additional complex and costly hardware, such as wavefront sensors, which measure how much the optical wavefront is distorted.
In contrast, the research team developed an algorithm that inversely calculates how light was distorted using only the acquired image data and corrects the distortion computationally. In other words, rather than simply sharpening a blurred image, the method incorporates the physical process of image formation to restore clearer images without additional wavefront measurement hardware.
The core of this technology is a machine learning algorithm based on neural fields. This algorithm models the distortion process that occurs as light propagates through biological tissue and the microscope system, enabling an integrated framework that simultaneously corrects optical aberrations caused by biological tissue, subtle motion of the living specimen, and mechanical alignment errors in the microscope.
As a result, the team demonstrated that clearer, higher-contrast images can be obtained from deep biological tissues without separate optical wavefront measurement or correction devices.
This research is particularly significant because it moves beyond the conventional approach that better imaging often requires more complex and expensive hardware. Instead, it shows that software-based computational algorithms can improve microscopy image quality. This approach is expected to help reduce the burden of research equipment and experimental procedures, and to support more precise biological imaging for a broader range of researchers.
Professor Iksung Kang stated, “This research shows the potential of combining optics and artificial intelligence to more accurately observe the inside of living biological systems. Moving forward, we plan to develop this into an intelligent optical imaging system where the microscope can identify the optimal imaging conditions.”
This study was published on April 13th in Nature Methods, a leading methodology journal in the life sciences.
※ Paper Title: Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields
※ Authors: Iksung Kang (KAIST, Co-corresponding & First Author), Hyeonggeon Kim, Ryan Natan, Qinrong Zhang, Stella X. Yu, & Na Ji (UC Berkeley, Co-corresponding Author)
