With applications in quantitative metabolomics and label-free digital pathology, Quantitative Phase Microscopy (QPM) measures refractive index maps of thin transparent specimens like live cells or tissue sections. In QPM, refractive index maps are usually reconstructed from interference measurements of the object’s light field with respect to a reference field. To this end, many previous works focused on designing stable full-field interferometers from the bottom up. In this work, we present an alternative strategy to design a QPM system top-down, starting from the desired measurement outcomes, with no explicit knowledge about interferometry. We call our inverse design strategy Differentiable Microcopy. To this end, our Differentiable Microcopy approach designed a range of Fourier-filter-based QPM systems that do not require computational post-reconstruction. Our designs are superior to existing similar designs in numerical benchmarks. We also experimentally validated one design using a spatial light modulator. Finally, to fabricate these custom designs in the future, we also propose a new fabrication-aware Differentiable Microcopy pipeline.
With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. However, the speed of current QPM systems is limited by electronic hardware. To improve throughput further, here we propose differentiable optical-electronic quantitative phase microscopy (∂μ) that acquires images in a compressed form such that more information can be transferred beyond the electronic hardware bottleneck. The proposed microscopy uses optical feature extractors as image compressors. The resultant intensity distribution is then decompressed into QPM images by a reconstruction network. By optimizing optical-electronic parameters in an end-to-end manner, our method can improve the QPI throughput from Hours to Seconds (more than an order of magnitude).
Since its introduction, diffractive deep neural networks (D2NN) have been an emerging technology with many useful applications, such as 3D object recognition, saliency segmentation, and quantitative phase microscopy. However, fabricating D2NNs operating in the visible range has not been performed due to the complexities of fabricating nano-scale elements. Recent advancements such as Implosion Fabrication have made it possible to fabricate such networks using a discrete number of phase weights. We propose a quantization-aware training approach for D2NNs through modeling the quantization process in a differentiable manner using a sigmoid-based quantization function, facilitating the fabrication process. We also propose an efficient training schedule to guide the optimization process to converge to a better minima despite the limited number of quantization levels. Our method is simulated and validated for an all-optical quantitative phase microscopy task based on the phase D2NN.
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