Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

3.1K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
3.1K
Structural Classification of Joints01:20

Structural Classification of Joints

9.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
9.0K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

890
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
890
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

828
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
828

You might also read

Related Articles

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

Sort by
Same author

Fast eikonal phase retrieval for high-throughput beamlines.

Journal of synchrotron radiation·2026
Same author

Evolution of hierarchical phase-contrast tomography on the European Synchrotron beamlines BM05 and BM18: a whole adult human brain imaging case study.

Journal of synchrotron radiation·2026
Same author

The heterogeneous nature of atrioventricular conduction tissues in tetralogy of Fallot demonstrated by hierarchical phase-contrast tomography.

JTCVS structural and endovascular·2026
Same author

The embryonic origins of site-specific arthritis.

Nature immunology·2026
Same author

Bridging the microstructural gap in human connectomics using hierarchical phase-contrast tomography as a reference for diffusion MRI in the human brain.

bioRxiv : the preprint server for biology·2026
Same author

Comparative mechanical characterisation of 13-93 bioactive glass and hybrid scaffolds for bone regeneration.

Scientific reports·2026
Same journal

Lung Nodule Detection via Optimized Convolutional Neural Network: Impact of Improved Moth Flame Algorithm.

Sensing and imaging·2023
Same journal

A Review of Quartz Crystal Microbalance for Chemical and Biological Sensing Applications.

Sensing and imaging·2023
Same journal

Numerical Simulation of Surface Plasmon Resonance Optical Fiber Biosensor Enhanced by Using Alloys for Medical Application.

Sensing and imaging·2023
Same journal

A Proposal for a Novel Surface-Stress Based BioMEMS Sensor Using an Optical Sensing System for Highly Sensitive Diagnoses of Bio-particles.

Sensing and imaging·2021
Same journal

Comparison Study of Regularizations in Spectral Computed Tomography Reconstruction.

Sensing and imaging·2020
Same journal

Reduction of Angularly-Varying-Data Truncation in C-Arm CBCT Imaging.

Sensing and imaging·2018
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K

Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization.

Daniil Kazantsev1, William R B Lionheart2, Philip J Withers1

  • 1The Manchester X-ray Imaging Facility, School of Materials, The University of Manchester, Manchester, M13 9PL UK ; The Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0FA UK.

Sensing and Imaging
|December 9, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an iterative algorithm to enhance image resolution and signal quality by using structural information from a secondary imaging modality. The method improves image reconstruction across diverse applications by leveraging supplementary data within a regularization framework.

Keywords:
Anatomical priorHybrid medical scannersHybrid modalitiesImage fusionPositron emission tomographyStructural prior

More Related Videos

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.7K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

911

Related Experiment Videos

Last Updated: Apr 19, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K
Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

14.7K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

911

Area of Science:

  • Image processing and computational imaging.
  • Medical imaging and multimodal data fusion.

Background:

  • Image reconstruction often faces limitations in resolution and signal-to-noise ratio (SNR).
  • Multimodal imaging provides complementary information (e.g., anatomical and functional) that can potentially improve image quality.

Purpose of the Study:

  • To develop an iterative reconstruction algorithm that enhances the resolution and SNR of a primary image dataset using information from a supplementary dataset.
  • To exploit structural information from supplementary data within a regularization framework for improved image reconstruction.

Main Methods:

  • An iterative reconstruction algorithm is proposed, operating solely on structural information (level set directions).
  • A modified total variation (TV) penalty term is utilized to incorporate supplementary structural information.
  • The method extracts structural cues from a supplementary image to enhance the primary image.

Main Results:

  • The algorithm successfully enhances the resolution and SNR of the primary image.
  • Edges common to both primary and supplementary images are enhanced.
  • Features with high contrast in the primary image alone are preserved.

Conclusions:

  • The proposed iterative method effectively leverages supplementary structural information to improve primary image quality.
  • The technique demonstrates suitability for various applications due to its focus on structural information.
  • Numerical experiments validate the feasibility and advantages of the proposed image reconstruction approach.