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Quantitative phase imaging (QPI) is a microscopy technique widely used to investigate cells. Even though first biomedical applications based on QPI have been developed, both acquisition speed and image quality need to improve to guarantee a widespread reception. Scientists from the Görlitz-based Center for Advanced Systems Understanding (CASUS) at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) as well as Imperial College London and University College London suggest leveraging an optical phenomenon called chromatic aberration – that usually degrades image quality – to produce suitable images with standard microscopes.
By employing a generative AI model, just one single exposure is needed to obtain the image quality necessary to make QPI attractive for applications in biomedicine. The team presented the work in late February at the 39th Annual Conference on AI by the Association for the Advancement of AI (AAAI) organized this year in Philadelphia (USA). The corresponding peer-reviewed conference paper will be available later in March.
Labelling biological samples with dyes or other agents reveals valuable insights. But this approach has some disadvantages that hinder its widespread usage in clinical diagnostics: It is time-consuming and expensive equipment as well as reagents are needed. Research in the past years has therefore centered on certain label-free microscopy methods like QPI. Here, not only the magnitude of light absorbed from or scattered by the sample is of interest. Using the scattering information, QPI also captures how the sample shifts the phase of light passing through it – a change that is directly related to its thickness, refractive index, and other structural properties. While also QPI requires quite expensive equipment, computational QPI does not.
One of the most prominent computational QPI approaches is solving the Transport-of-Intensity Equation (TIE). This differential equation allows calculating an image of the sample based on the recorded phase changes. The approach is easy to integrate into an existing optical microscope set-up and results in good-quality images. On the downside, the TIE method often requires multiple acquisitions with different focus distances to get rid of artefacts. Dealing with through-focus stacks can be time-consuming and technically demanding so this type of TIE-based QPI is often not feasible in a clinical setting.
Making use of chromatic aberration
“Our approach relies on the similar principles as TIE but only needs one image because of a clever combination of physics and generative AI”, says Prof. Artur Yakimovich, Leader of a CASUS Young Investigator Group and corresponding author of the work presented at the AAAI Conference. The information about the phase shift induced by biological specimen does not come from additional exposures taken with other focus distances. A through-focus stack can also be generated from one single exposure thanks to a phenomenon called chromatic aberration. Most lens systems of the microscope cannot bring all wavelengths of (polychromatic) white light to a single converging point perfectly – a handicap that only highly specialized lenses can correct. This means e.g. red, green and blue (RGB) light have slightly different focus distances. “By recording the phase shifts of those three wavelengths separately using a conventional RGB detector, one can build a through-focus stack that facilitates computational QPI turning the handicap into an asset”, Yakimovich explains.
“Using chromatic aberrations to realize QPI poses one challenge: The distance between the red light focus and the blue light focus is very small”, says Gabriel della Maggiora, PhD student at CASUS and one of the two lead authors of the publication. Solving the TIE the standard way just does not give meaningful results. “We then reasoned that we could use artificial intelligence. As it turned out, this idea proved to be decisive”, della Maggiora adds. “After training a generative AI model with an open-access data set consisting of 1.2 million images, the model was able to retrieve phase information even though just relying on the very limited data input from the recording.”
Method validated on real-world clinical specimen
The team drew on a generative AI model for image quality improvement presented last spring: the Conditional Variational Diffusion Model (CVDM). It belongs to a particular family of generative AI models named diffusion models. The developers emphasize that training a CVDM needs significantly less computational effort than training other diffusion models while the results are the same or even better. Harnessing a CVDM strategy, della Maggiora and colleagues developed a novel diffusion model that is applicable for quantitative data. With this model, they were now able to finally realize computational QPI based on chromatic aberrations. They validated their generative AI-based approach using for example a common brightfield microscope equipped with a commercially available color camera to make microscopic images from real-world clinical specimen: Analyzing red blood cells in a human urine sample, the method was able to unveil the donut-like shape of these cells whereas another, established computational TIE-based approach was not. An additional advantage was the virtual absence of cloud artifacts in the images calculated with the new generative AI-based quantitative phase imaging variant.
The Yakimovich group “Machine Learning for Infection and Disease” develops novel computational techniques for microscopy that could be immediately applied in clinical settings. The potential e.g. in diagnostics is huge. Among the techniques used is generative AI. As generative AI is prone to produce hallucinations, a main focus of the group is to reduce them. Incorporating physics-based elements is the key approach here. As the AI-based quantitative phase imaging example shows, this approach is very promising.
Prof. Artur Yakimovich | Young Investigator Group Leader
Center for Advanced Systems Understanding (CASUS) at HZDR
Email: a.yakimovich@hzdr.de
G. della Maggiora, L. A. Croquevielle, H. Horsley, T. Heinis, A. Yakimovich, Single Exposure Quantitative Phase Imaging with a Conventional Microscope using Diffusion Models, presented at the 39th Annual Conference on Artificial Intelligence by the Association for the Advancement of Artificial Intelligence (AAAI) and accepted for publication in the Proceedings of the 39th AAAI Conference on Artificial Intelligence, preprint available: https://arxiv.org/abs/2406.04388
Physical foundations of the new generative AI-based quantitative phase imaging variant showing the l ...
Blaurock/CASUS
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