Single-Shot Replica-Free Common-Path Quantitative Phase Imaging with Doubled Field of View

To see the invisible world of living cells without staining them with dyes or killing them, scientists rely on a technique called quantitative phase imaging. Instead of looking at how a sample absorbs light, this method measures how the sample slows down light as it passes through. Because living cells are mostly transparent, they do not block light, but they do delay it slightly depending on their thickness and density. By measuring this tiny delay, researchers can create a detailed map of a cell's structure and even calculate its dry mass. One of the most stable ways to do this is using a setup where the light beam travels a single, shared path, making the system resistant to the vibrations and temperature shifts that usually ruin delicate measurements. However, this stability comes with a frustrating trade-off: the standard method creates a "ghost" image, a duplicate of the sample that overlaps with the real one. To avoid this confusion, scientists have traditionally been forced to look only at sparse samples where the ghost does not touch the real object, effectively throwing away more than half of the camera's viewing area and making it impossible to study dense crowds of cells.

A team of researchers at the Warsaw University of Technology and the Nencki Institute of Experimental Biology has found a way to break this limitation. They have developed a new method that removes the ghost image entirely, allowing them to see the full field of view in a single instant, even when the sample is packed with cells. Their approach, described in their recent work, replaces the mechanical moving parts of older systems with a clever use of light color. By shifting the color of the light source, they can change the position of the ghost image without moving any lenses or mirrors. They then use a sophisticated computer algorithm to mathematically separate the real image from the ghost, effectively doubling the usable space on the camera sensor. This allows for the clear, high-speed imaging of dense, moving biological samples, such as yeast cultures and neural networks, which were previously too crowded to study with this level of clarity.

The core of the problem the team solved lies in how these microscopes work. In a common-path setup, the light that passes through the sample interferes with a copy of itself that has been shifted slightly to the side. This interference creates a pattern that holds the information needed to reconstruct the image. However, because the copy is shifted, it often lands right on top of the original sample in the final picture. If the sample is sparse, with plenty of empty space between cells, the copy lands in the empty area and causes no trouble. But if the sample is dense, like a crowded petri dish of yeast, the copy overlaps with the real cells, destroying the information and making the image unreadable. Previous solutions involved moving the optical components to shift the copy to different positions over time, but this required mechanical scanning. This process was slow, prone to mechanical wear, and made it impossible to capture fast-moving objects because the system needed to take many pictures over several seconds to build a complete image.

The researchers replaced this mechanical movement with a change in the color of the light. They used a special grating that bends light based on its wavelength, or color. When they changed the color of the light, the angle at which the light bent changed, which in turn moved the position of the ghost image. They tested two ways to do this. In the first, they scanned through a range of colors one by one, taking a picture at each step. In the second, which is the more powerful innovation, they illuminated the sample with three distinct colors of light—blue, green, and red—all at the same time. A standard color camera captured a single image containing all three versions of the scene. Because each color created a ghost image at a slightly different location, the camera recorded three overlapping patterns in a single frame.

To make sense of this complex single snapshot, the team created a new computer algorithm based on a method called gradient descent. This algorithm acts like a powerful solver that takes the messy, overlapping data and figures out exactly how to separate the real image from the three different ghost images. It works by iteratively adjusting its guess of what the two separate images look like until the math perfectly matches the recorded data. Crucially, this new algorithm does not need to know beforehand whether the cells are thicker or thinner than their surroundings, a requirement that limited older methods. It also handles the fact that different colors of light focus at slightly different depths and magnify the image by different amounts, correcting these natural optical quirks automatically.

The results of this new method are striking. When the researchers tested it on a standard test target with etched structures, they found that the quality of the image was just as good as the older, slower mechanical methods, but with the added benefit of being able to handle dense samples. They showed that the new algorithm could successfully separate the images even when the shift between the ghost and the real object was very small or very large, a range where previous methods often failed. Most importantly, they demonstrated the power of the single-shot approach by imaging a culture of moving yeast cells. In a dense culture, it is nearly impossible to find a spot where the cells do not overlap with their own ghost image. Using their new single-shot technique, the team captured the motion of the yeast cells in real time, tracking them as they moved across the slide. The algorithm successfully separated the two views, revealing the true structure of the cells without the blur or artifacts that usually plague such dense samples.

This work represents a significant step forward for biological imaging. By eliminating the need for mechanical scanning and removing the restriction on sample density, the researchers have created a tool that is both faster and more versatile. The system is robust against environmental vibrations because it uses a shared light path, and it is fast enough to freeze the motion of living cells. While the use of a color camera does mean a slight reduction in the finest possible detail compared to a specialized black-and-white sensor, the ability to see the whole picture at once in a crowded environment is a major gain. The team validated their method on fixed neural networks and live yeast, showing that it can handle both static, detailed structures and dynamic, moving systems. This approach offers a practical way to study complex biological processes in real time, opening the door to observing crowded cellular environments that were previously too difficult to image clearly.