Journal
NATURE MACHINE INTELLIGENCE
Volume 4, Issue 9, Pages 781-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42256-022-00530-3
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Funding
- NSF [CCF-1813910, CCF-2043134, CCF-1813848, EPMD-1846784]
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Intensity Diffraction Tomography (IDT) is a technique that uses optical microscopy to image the three-dimensional refractive index distribution of a sample from two-dimensional intensity-only measurements. Neural fields is a new deep learning approach that can learn continuous representations of physical fields. DeCAF is a neural-fields-based IDT method that can learn a high-quality continuous representation of a refractive index volume from intensity-only and limited-angle measurements, without ground-truth RI maps. DeCAF can generate high-contrast and artifact-free RI maps and outperforms existing methods in terms of mean squared error reduction.
Intensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the three-dimensional refractive index (RI) distribution of a sample from a set of two-dimensional intensity-only measurements. The reconstruction of artefact-free RI maps is a fundamental challenge in IDT due to the loss of phase information and the missing-cone problem. Neural fields has recently emerged as a new deep learning approach for learning continuous representations of physical fields. The technique uses a coordinate-based neural network to represent the field by mapping the spatial coordinates to the corresponding physical quantities, in our case the complex-valued refractive index values. We present Deep Continuous Artefact-free RI Field (DeCAF) as a neural-fields-based IDT method that can learn a high-quality continuous representation of a RI volume from its intensity-only and limited-angle measurements. The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model without any ground-truth RI maps. We qualitatively and quantitatively evaluate DeCAF on the simulated and experimental biological samples. Our results show that DeCAF can generate high-contrast and artefact-free RI maps and lead to an up to 2.1-fold reduction in the mean squared error over existing methods. Producing high-quality 3D refractive index maps from 2D intensity-only measurements is a long-standing objective in computational microscopy, with many applications involving the visualization of cellular and subcellular structures. A new method can reconstruct high-contrast and artefact-free images by employing the neural fields technique, which can learn a continuous 3D representation using a neural network that maps spatial coordinates to the refractive index values.
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