Compressed acquisition of microscopic images
Inventors
LESHEM, Ben • SMALL, Eran • Hayut, Itai • Na'aman, Erez • BEN-BASSAT, Eyal
Assignees
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Publication Number
US-12489872-B2
Publication Date
2025-12-02
Expiration Date
Abstract
A microscope for computational imaging may include an illumination source configured to illuminate a sample with a plurality of wavelengths, an image sensor, an objective lens to image the sample onto the image sensor, and a processor operatively coupled to the illumination assembly and the image sensor. The processor may be configured to acquire a first image dataset from the sample illuminated using a first set of illumination conditions at a first wavelength. The processor may also be configured to acquire a second image dataset from the sample illuminated using a second set of illumination conditions having a second number of illumination conditions at a second wavelength. The second set of illumination conditions comprises fewer illumination conditions than the first set in order to decrease acquisition time. The processor may be configured to combine the first and second image datasets into a computationally reconstructed image of the sample.
Core Innovation
A method is described for generating a computationally reconstructed image by acquiring, with an image sensor, a first image dataset from a sample illuminated using a first set of illumination conditions comprising a first number of illumination conditions, each comprising a first wavelength. The first image dataset includes a plurality of images having a first spatial frequency bandwidth.
The method further acquires, with the image sensor, a second image dataset from the sample illuminated using a second set of illumination conditions comprising a second number of illumination conditions, each comprising a second wavelength, where the second dataset comprises one or more images having a second spatial frequency bandwidth. The first number of illumination conditions is larger than the second number of illumination conditions.
The first image dataset and the second image dataset are combined into a computationally reconstructed image of the sample. The computationally reconstructed image comprises a spatial frequency bandwidth greater than the first spatial frequency bandwidth of the first image dataset and the second spatial frequency bandwidth of the second image dataset, providing an increased spatial frequency bandwidth relative to the first image dataset and the second image dataset.
The approach is supported by microscope system components including an illumination assembly, objective lens, image sensor, and a processor configured to perform the method. The described workflow supports combining channel-wise datasets across wavelengths, optionally producing an RGB color image or a hyperspectral image, and may include correction for optical aberrations and/or increased image contrast.
Claims Coverage
The claim coverage centers on one independent claim with dependent refinements. It covers combining two image datasets acquired under different illumination-condition sets and different wavelengths to produce a computationally reconstructed image with increased spatial frequency bandwidth.
Two-dataset computational reconstruction with greater spatial frequency bandwidth
Acquiring a first image dataset with a first set of illumination conditions using a first wavelength, where the first image dataset comprises a plurality of images having a first spatial frequency bandwidth; acquiring a second image dataset with a second set of illumination conditions using a second wavelength, where the second image dataset comprises one or more images having a second spatial frequency bandwidth, and the first number of illumination conditions is larger than the second number; and combining the first image dataset and the second image dataset into a computationally reconstructed image comprising a spatial frequency bandwidth greater than the first spatial frequency bandwidth of the first image dataset and the second spatial frequency bandwidth of the second image dataset.
Illumination-condition quantity and bandwidth/time constraints
The first number of illumination conditions is larger than the second number by at least a factor of two, the second acquisition time is no more than half of the first acquisition time, and the computationally reconstructed image spatial frequency bandwidth is greater than each image’s spatial frequency bandwidth by at least a factor of 1.5.
Optical aberration correction and/or image contrast increase in the reconstruction
The computationally reconstructed image includes one or more of a correction for optical aberrations or an increase in image contrast.
Optical aberration correction by separating aberration information from sample information
Correcting optical aberrations by separating aberration information from sample information to decrease an effect of optical aberrations on the computationally reconstructed image.
Color computationally reconstructed image with red, green, and blue channels
The computationally reconstructed image is a color image comprising two or more of a red channel, a green channel, or a blue channel.
Microscope configured to perform the reconstruction method
A microscope comprising an illumination assembly, an image sensor, an objective lens, and a processor configured to perform the method.
The claims are directed to generating a computationally reconstructed image by combining two datasets acquired under different illumination-condition sets and wavelengths so that the reconstructed image has greater spatial frequency bandwidth than either dataset. Dependent refinements address illumination counts, acquisition time, bandwidth growth, optical aberration correction, image contrast, color-channel outputs, and a microscope architecture configured to perform the method.
Stated Advantages
Increased spatial frequency bandwidth in the computationally reconstructed image relative to the first image dataset and the second image dataset.
Acquisition-time reduction and reconstruction-time reduction as described in the provided summary context.
Documented Applications
Computational microscopy with compressed acquisition using two datasets acquired at different wavelengths and illumination-condition sets, including workflows producing an RGB color image or a hyperspectral image.
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