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Related Concept Videos

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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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Related Experiment Video

Updated: Feb 1, 2026

Mitochondria and Endoplasmic Reticulum Imaging by Correlative Light and Volume Electron Microscopy
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vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.

Jibin Yang1,2,3, Jie Huo4, Muyu Liu1

  • 1School of Software, Shandong University, Jinan, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

We developed vEMINR, a fast method for 3D electron microscopy reconstruction. This implicit neural representation approach improves image quality and accelerates processing for large datasets.

Keywords:
3D reconstructiondeep learningimplicit neural representationseries section electron microscopyvolume electron microscopy

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Area of Science:

  • Neuroscience
  • Biophysics
  • Computational Biology

Background:

  • Volume electron microscopy (vEM) provides nanoscale 3D visualization of biological structures.
  • vEM suffers from anisotropic resolution, with significantly lower axial than lateral resolution due to sectioning limitations.

Purpose of the Study:

  • To introduce vEMINR, an ultra-fast implicit neural representation (INR) based method for isotropic vEM image reconstruction.
  • To enhance vEM image quality by learning true degradation patterns and accelerate reconstruction using INR's efficient parameterization.

Main Methods:

  • Developed vEMINR, an implicit neural representation (INR) method for vEM image reconstruction.
  • Utilized INR's continuous function representation and efficient parameterization for accelerated processing.
  • Trained the model to learn true degradation patterns from low-resolution vEM images.

Main Results:

  • vEMINR achieved over tenfold faster reconstruction compared to mainstream methods across 11 public datasets.
  • Demonstrated significantly higher accuracy in reconstructing organelles and neurons from vEM data.
  • Maintained reconstruction accuracy while enabling high-throughput processing of terabyte-scale vEM datasets.

Conclusions:

  • vEMINR offers a substantial improvement in reconstruction speed and accuracy for vEM datasets.
  • The method's efficiency facilitates large-scale vEM image analysis and related research.
  • vEMINR is poised to become a significant tool in advanced biological imaging and research.