Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Encoding

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Molecular Shapes

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Molecular and Ionic Solids

Molecular and Ionic Solids

Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multi-segmented feature coupling for jointly reconstructing initial pressure and speed of sound in photoacoustic computed tomography.

Journal of biomedical optics·2022

Same author

Depth-recognizable time-domain fluorescence molecular tomography in reflective geometry.

Biomedical optics express·2021

Same author

Streak artifact suppression in photoacoustic computed tomography using adaptive back projection.

Biomedical optics express·2019

Same author

Feature coupling photoacoustic computed tomography for joint reconstruction of initial pressure and sound speed <i>in vivo</i>.

Biomedical optics express·2019

Same author

Reconstruction of high-resolution early-photon tomography based on the first derivative of temporal point spread function.

Journal of biomedical optics·2018

Same author

End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging.

Optics letters·2018

Same journal

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026

Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026

Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026

Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026

Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026

Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 26, 2026

Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts

Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts

Published on: April 26, 2018

3D deep encoder-decoder network for fluorescence molecular tomography.

Lin Guo, Fei Liu, Chuangjian Cai

|April 16, 2019

Summary

This summary is machine-generated.

A new deep learning method improves fluorescence molecular tomography (FMT) reconstruction. The 3D-En-Decoder network enhances image quality and reduces processing time for this noninvasive in vivo imaging technique.

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Published on: June 2, 2009

Related Experiment Videos

Last Updated: Jan 26, 2026

Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts

Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts

Published on: April 26, 2018

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Published on: June 2, 2009

Area of Science:

Biomedical imaging
Medical physics
Deep learning applications

Background:

Fluorescence molecular tomography (FMT) is a noninvasive in vivo imaging technique.
Current FMT reconstruction quality is limited by simplified linear models of photon propagation.

Purpose of the Study:

To develop an advanced method for improving FMT image reconstruction quality.
To address the limitations of linear models in FMT.

Main Methods:

An end-to-end three-dimensional deep encoder-decoder (3D-En-Decoder) network was proposed.
The network establishes a direct nonlinear mapping between fluorescent source distribution and boundary signal.
Numerical simulations and phantom experiments were conducted.

Main Results:

The 3D-En-Decoder network significantly improved FMT image quality.
Reconstruction time was substantially reduced compared to conventional methods.
The proposed network fundamentally avoids reconstruction inaccuracies from simplified linear models.

Conclusions:

The 3D-En-Decoder network offers a superior approach for FMT reconstruction.
This deep learning method enhances both accuracy and efficiency in FMT imaging.
The findings suggest a significant advancement for in vivo functional imaging.