Researchers at the Advanced Research Center for Nanolithography (ARCNL) and Vrije Universiteit Amsterdam in the Netherlands have developed a compact device that can be fast and ultra-high with ultra-thin fiber Resolution microscopy. Using intelligent signal processing, they broke through the theoretical limits of resolution and speed. Since this method does not require any special fluorescent labeling, it is promising for medical applications and the characterization of three-dimensional 3D structures in nanolithography. The results of this study were published in a recent issue of Nature: Light: Science and Applications.
The lead author of the paper, Amitonova, the first author and professor of the School of Biophotonics and Medical Imaging, has established a nanoimaging and metrology research group to engage in ultrafine fiber endoscopy.
She said: "Nano-scale imaging is limited by the wavelength of the light used. There are many ways to overcome this diffraction limit, but they usually require large microscopes and difficult processing procedures." These systems are not suitable for deep tissue or biological tissue Imaging in other hard-to-reach places. "
To this end, the research team developed a method to overcome the diffraction limit in small systems to achieve super-resolution deep tissue imaging
Inverse data compression
The key to this research method is that creating a meaningful image does not require all the information in the data sample. She said: "Think about digital photography, which uses the JPEG compression format to limit the amount of data in the picture. Compression can delete up to 90% of the image, but we can hardly see the difference between them." "This is feasible , Because all conventional images in real life are sparse, which means that most image points do not contain any information. In our measurement, we use this in the opposite way by obtaining only 10% of the information Available data for sparse information, and the entire image is reconstructed through mathematical calculations."
Speckled beam
In traditional microscopy, the sample is usually illuminated point by point to produce an image of the entire sample. This takes a lot of time because high-resolution images require many data points. The method developed by Amitonova uses an optical fiber that generates a spot laser beam that can simultaneously illuminate many areas in the sample in a random manner. Then the multi-faceted light reflected by the sample is collected into a single data point, and related information is extracted from it through calculation. She said: "In the case of point-by-point lighting, acquiring 256 data points will result in a 256-pixel image. Using our method, the same number of measurements can produce an image of approximately 20 times the pixel," "So compression imaging is faster Much more, but we also proved that it can resolve details that are more than twice smaller than traditional diffraction-limited imaging."
Tagless induction
The development of this method takes into account minimally invasive biological imaging. But this is also very promising for sensing applications in nanolithography because it does not require fluorescent labels, which are necessary for other super-resolution imaging methods. She said: "The compactness of the fiber makes it very convenient for the development of nanolithography metrology tools. Fiber-based probes provide a unique combination of high resolution and large field of view, which can be easily used in difficult-to-develop places. This method is further developed It is expected to bring higher resolution and speed. Metrology tools and medical diagnosis are the areas most likely to benefit from the research findings."
As shown in the schematic diagram of super-resolution fiber imaging. A random spot beam (green) from the fiber illuminates the entire sample multiple times (right). Compressed sensing reconstruction can provide high-resolution images of samples without fluorescent labels, thus providing nanoscale detection applications in biological imaging and nanolithography.
