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

Computed Tomography01:10

Computed Tomography

7.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.7K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.7K
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...
2.7K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

167
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...
167
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

641
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
641

You might also read

Related Articles

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

Sort by
Same author

Image reconstruction from incomplete data via approximated pseudo-inverse and dual-domain coupled diffusion posterior sampling.

Quantitative imaging in medicine and surgery·2026
Same author

Detail preservation sparse-view CT image reconstruction via range-null space decomposition based diffusion priors.

Journal of X-ray science and technology·2026
Same author

An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.

Quantitative imaging in medicine and surgery·2026
Same author

Visual language model-assisted CT denoising via text-guided diffusion and fidelity maintenance.

Journal of X-ray science and technology·2026
Same author

Accelerating direct material decomposition via diffusion probabilistic model for Sparse-view spectral computed tomography.

Journal of X-ray science and technology·2025
Same author

Visual language model-assisted spectral CT reconstruction by diffusion and low-rank priors from limited-angle measurements.

Physics in medicine and biology·2025

Related Experiment Video

Updated: Dec 6, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

899

Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization.

Wenkun Zhang1, Ningning Liang1, Zhe Wang2

  • 1Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.

Quantitative Imaging in Medicine and Surgery
|October 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for multi-energy computed tomography (MECT) using photon-counting detectors. The technique significantly reduces image noise while preserving crucial details, enhancing diagnostic accuracy.

Keywords:
Multi-energy CT reconstructionspatial sparsitytensor nonlocal similarity

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.3K
Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles
10:00

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Published on: July 5, 2016

12.2K

Related Experiment Videos

Last Updated: Dec 6, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

899
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.3K
Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles
10:00

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Published on: July 5, 2016

12.2K

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Photon-counting detector-based multi-energy computed tomography (MECT) is an emerging imaging technique.
  • High quantum noise in MECT images limits diagnostic quality due to limited photon counts in narrow energy bins.
  • There is a need to improve MECT image quality by reducing noise while maintaining image details.

Purpose of the Study:

  • To develop and validate a novel reconstruction method for photon-counting detector-based MECT.
  • To enhance MECT image quality by minimizing noise levels.
  • To preserve fine image details during the reconstruction process.

Main Methods:

  • Proposed a novel MECT reconstruction method leveraging nonlocal tensor similarity and spatial sparsity.
  • Utilized intrinsic tensor sparsity regularization (Tucker and CP decomposition) for interchannel images.
  • Incorporated total variation (TV) regularization for spatial sparsity in single-channel images.
  • Developed a reconstruction model combining intrinsic tensor sparsity and TV regularizations, solved via iterative alternating minimization.

Main Results:

  • The proposed method demonstrated significant noise suppression and detail preservation in digital phantom and real mouse data.
  • Achieved superior reconstruction and decomposition results compared to analytic, TV-based, and tensor-based methods.
  • Reduced root mean square error (RMSE) by up to 89.75% for reconstructed images and 97.96% for decomposition results.

Conclusions:

  • A novel MECT reconstruction method using intrinsic tensor sparsity and TV regularizations was successfully developed.
  • The method effectively suppresses noise and preserves image details in photon-counting detector-based MECT.
  • Validated improvements through qualitative and quantitative evaluations, confirming the method's potential for MECT applications.