An official website of the United States government
Learnable-pattern scanning based deep compressed imaging
We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality.
more »
« less
- Award ID(s):
- 1847141
- PAR ID:
- 10268494
- Editor(s):
- Goda, Keisuke; Tsia, Kevin K.
- Date Published:
- Journal Name:
- High-Speed Biomedical Imaging and Spectroscopy VI
- Volume:
- 11654
- Page Range / eLocation ID:
- 1165413
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract We present a two-photon fluorescence microscope designed for high-speed imaging of neural activity in cellular resolution. Our microscope uses a new adaptive sampling scheme with line illumination. Instead of building images pixel by pixel via scanning a diffraction-limited spot across the sample, our scheme only illuminates the regions of interest (i.e., neuronal cell bodies), and samples a large area of them in a single measurement. Such a scheme significantly increases the imaging speed and reduces the overall laser power on the brain tissue. Using this approach, we performed high-speed imaging of the neural activity of mouse cortexin vivo. Our method provides a new sampling strategy in laser-scanning two-photon microscopy, and will be powerful for high-throughput imaging of neural activity.more » « less
-
We present a two-photon fluorescence microscope designed for high-speed imaging of neural activity at cellular resolution. Our microscope uses an adaptive sampling scheme with line illumination. Instead of building images pixel by pixel via scanning a diffraction-limited spot across the sample, our scheme only illuminates the regions of interest (i.e., neuronal cell bodies) and samples a large area of them in a single measurement. Such a scheme significantly increases the imaging speed and reduces the overall laser power on the brain tissue. Using this approach, we performed high-speed imaging of the neuronal activity in mouse cortexin vivo. Our method provides a sampling strategy in laser-scanning two-photon microscopy and will be powerful for high-throughput imaging of neural activity.more » « less
-
We present a two-photon fluorescence microscope designed for high-speed imaging of neural activity in cellular resolution. Our microscope uses line illumination with an adaptive sampling scheme. Instead of building images pixel by pixel via scanning a diffraction-limited spot across the tissue, our scheme only illuminates the regions of interest (i.e., neuronal cell bodies), and samples a large area of them in a single measurement. This significantly increases the imaging speed and reduces the overall laser power on the sample. We characterized the imaging resolution and verified the concept of adaptive sampling through phantom samples. Our approach holds great promise for high-throughput neural activity imaging.more » « less
-
null (Ed.)Intensity Diffraction Tomography (IDT) is a new computational microscopy technique providing quantitative, volumetric, large field-of-view (FOV) phase imaging of biological samples. This approach uses computationally efficient inverse scattering models to recover 3D phase volumes of weakly scattering objects from intensity measurements taken under diverse illumination at a single focal plane. IDT is easily implemented in a standard microscope equipped with an LED array source and requires no exogenous contrast agents, making the technology widely accessible for biological research.Here, we discuss model and learning-based approaches for complex 3D object recovery with IDT. We present two model-based computational illumination strategies, multiplexed IDT (mIDT) [1] and annular IDT (aIDT) [2], that achieve high-throughput quantitative 3D object phase recovery at hardware-limited 4Hz and 10Hz volume rates, respectively. We illustrate these techniques on living epithelial buccal cells and Caenorhabditis elegans worms. For strong scattering object recovery with IDT, we present an uncertainty quantification framework for assessing the reliability of deep learning-based phase recovery methods [3]. This framework provides per-pixel evaluation of a neural network predictions confidence level, allowing for efficient and reliable complex object recovery. This uncertainty learning framework is widely applicable for reliable deep learning-based biomedical imaging techniques and shows significant potential for IDT.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1117/12.2577998
