Low dosage 3D volume fluorescence microscopy imaging using compressive sensing

• URL: http://arxiv.org/abs/2201.00820v1
• Date: Mon, 3 Jan 2022 18:44:50 GMT
• Title: Low dosage 3D volume fluorescence microscopy imaging using compressive sensing
• Authors: Varun Mannam, Jacob Brandt, Cody J. Smith, and Scott Howard
• Abstract summary: We present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage. We demonstrate our technique by capturing a 3D volume of the RFP labeled neurons in the zebrafish embryo spinal cord with the axial sampling of 0.1um using a confocal microscope. The developed CS-based methodology in this work can be easily applied to other deep imaging modalities such as two-photon and light-sheet microscopy, where reducing sample photo-toxicity is a critical challenge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fluorescence microscopy has been a significant tool to observe long-term imaging of embryos (in vivo) growth over time. However, cumulative exposure is phototoxic to such sensitive live samples. While techniques like light-sheet fluorescence microscopy (LSFM) allow for reduced exposure, it is not well suited for deep imaging models. Other computational techniques are computationally expensive and often lack restoration quality. To address this challenge, one can use various low-dosage imaging techniques that are developed to achieve the 3D volume reconstruction using a few slices in the axial direction (z-axis); however, they often lack restoration quality. Also, acquiring dense images (with small steps) in the axial direction is computationally expensive. To address this challenge, we present a compressive sensing (CS) based approach to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage. We present the theory and experimentally validate the approach. To demonstrate our technique, we capture a 3D volume of the RFP labeled neurons in the zebrafish embryo spinal cord (30um thickness) with the axial sampling of 0.1um using a confocal microscope. From the results, we observe the CS-based approach achieves accurate 3D volume reconstruction from less than 20% of the entire stack optical sections. The developed CS-based methodology in this work can be easily applied to other deep imaging modalities such as two-photon and light-sheet microscopy, where reducing sample photo-toxicity is a critical challenge.

Related papers

• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• Fast and robust single particle reconstruction in 3D fluorescence microscopy [1.0625549557437526]
  Single particle reconstruction is a powerful technique to improve the axial resolution and the degree of fluorescent labeling. We propose a single particle reconstruction method dedicated to convolutional models in 3D fluorescence microscopy. We demonstrate on synthetic approaches in terms of resolution and reconstruction error, while achieving a low computational cost.
  arXiv  Detail & Related papers  (2023-01-23T14:20:01Z)
• Estimation of Optical Aberrations in 3D Microscopic Bioimages [1.588193964339148]
  We describe an extension of PhaseNet enabling its use on 3D images of biological samples. We add a Python-based restoration of images via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with the predicted PSF can not only remove the simulated aberrations but also improve the quality of the real raw microscopic images with unknown residual PSF.
  arXiv  Detail & Related papers  (2022-09-16T13:22:25Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Axial-to-lateral super-resolution for 3D fluorescence microscopy using unsupervised deep learning [19.515134844947717]
  We present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in fluorescence microscopy. Our method greatly reduces the effort to put into practice as the training of a network requires as little as a single 3D image stack. We demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit, but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
  arXiv  Detail & Related papers  (2021-04-19T16:31:12Z)
• Cryo-ZSSR: multiple-image super-resolution based on deep internal learning [14.818511430476589]
  Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging modality capable of visualizing proteins and macro-molecular complexes at near-atomic resolution. We present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our results indicate that the combination of low magnification imaging with image SR has the potential to accelerate cryo-EM data collection without sacrificing resolution.
  arXiv  Detail & Related papers  (2020-11-22T14:04:54Z)
• Recurrent neural network-based volumetric fluorescence microscopy [0.30586855806896046]
  We report a deep learning-based image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume. Recurrent-MZ is demonstrated to increase the depth-of-field of a 63xNA objective lens by approximately 50-fold, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume.
  arXiv  Detail & Related papers  (2020-10-21T06:17:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.