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<p>High numerical aperture fluorescence microscopy provides subcellular resolution, but its depth of field is extremely limited, so thick specimens quickly fall out of focus and typically require axial scanning. Multispectral imaging further compounds this problem because chromatic aberrations shift the best focus plane and distort registration across emission channels, especially in thick samples. In this work, we present MANTIS (Multispectral All-Depth meta-opTic Imaging System), a co-designed computational microscopy imaging system that achieves extended depth-of-field from a single acquisition without axial scanning. MANTIS combines a learned meta-optic with a physics-guided neural network trained end-to-end to reconstruct sharp multispectral images from depth, and wavelength-dependent blurred sensor measurements. We experimentally demonstrate a 50 <i>μ</i>m extended depth of field at NA 1.1, corresponding to an 82-fold increase over a conventional wide-field microscope. In addition, simulations show that MANTIS can target different depth-of-field ranges, with the expected trade-off that larger depth-of-field ranges come at a cost in reconstruction fidelity. We validate the approach on biologically relevant fluorescence specimens, including 50 <i>μ</i>m thick three-dimensional cultured MDCK II spheroids, and show that, compared with conventional wide-field fluorescence microscopy, reconstructions maintain contrast and lateral detail across depth, with reduced defocus blur and consistent performance across spectral channels.</p>

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Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Ipek Anil Atalay Appak,
  • Haobijam Johnson Singh,
  • Sanna Korpela,
  • Teemu O. Ihalainen,
  • Erdem Sahin,
  • Christine Guillemot,
  • Humeyra Caglayan

摘要

High numerical aperture fluorescence microscopy provides subcellular resolution, but its depth of field is extremely limited, so thick specimens quickly fall out of focus and typically require axial scanning. Multispectral imaging further compounds this problem because chromatic aberrations shift the best focus plane and distort registration across emission channels, especially in thick samples. In this work, we present MANTIS (Multispectral All-Depth meta-opTic Imaging System), a co-designed computational microscopy imaging system that achieves extended depth-of-field from a single acquisition without axial scanning. MANTIS combines a learned meta-optic with a physics-guided neural network trained end-to-end to reconstruct sharp multispectral images from depth, and wavelength-dependent blurred sensor measurements. We experimentally demonstrate a 50 μm extended depth of field at NA 1.1, corresponding to an 82-fold increase over a conventional wide-field microscope. In addition, simulations show that MANTIS can target different depth-of-field ranges, with the expected trade-off that larger depth-of-field ranges come at a cost in reconstruction fidelity. We validate the approach on biologically relevant fluorescence specimens, including 50 μm thick three-dimensional cultured MDCK II spheroids, and show that, compared with conventional wide-field fluorescence microscopy, reconstructions maintain contrast and lateral detail across depth, with reduced defocus blur and consistent performance across spectral channels.