Single Exposure Quantitative Phase Imaging with a Conventional Microscope Using Diffusion Models

Phase imaging is gaining importance due to its applications in fields like biomedical imaging and material characterization. In biomedical applications, it can provide quantitative information missing in label-free microscopy modalities. One of the most prominent methods in phase quantification is the Transport-of-Intensity Equation (TIE). TIE often requires multiple acquisitions at different defocus distances, which is not always feasible in a clinical setting due to hardware constraints. To address this issue, we propose the use of chromatic aberrations to induce the required through-focus images with a single exposure, effectively generating a through-focus stack. Since the defocus distance induced by the aberrations is small, conventional TIE solvers are insufficient to address the resulting artifacts. We propose Zero-Mean Diffusion, a modified version of diffusion models designed for quantitative image prediction, and train it with synthetic data to ensure robust phase retrieval. Our contributions offer an alternative TIE approach that leverages chromatic aberrations, achieving accurate single-exposure phase measurement with white light and thus improving the efficiency of phase imaging. Additionally, we present a new class of diffusion models that are well-suited for quantitative data and have a sound theoretical basis. To validate our approach, we employ a widespread brightfield microscope equipped with a commercially available color camera. We apply our model to clinical microscopy of patients' urine, obtaining accurate phase measurements.

Authors

• Gabriel della Maggiora Center for Advanced Systems Understanding (CASUS), Görlitz, Germany Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany School of Computation, Information and Technology, Technical University of Munich, Germany
• Luis Alberto Croquevielle Department of Computing, Imperial College London, London, United Kingdom
• Harry Horsley UCL Centre for Kidney and Bladder Health, Division of Medicine, University College London, Royal Free Hospital Campus, London, United Kingdom
• Thomas Heinis Department of Computing, Imperial College London, London, United Kingdom
• Artur Yakimovich Institute of Computer Science, University of Wrocław, Wrocław, Poland Center for Advanced Systems Understanding (CASUS), Görlitz, Germany Helmholtz-Zentrum Dresden-Rossendorf e. V. (HZDR), Dresden, Germany

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