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

6.3K
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...
6.3K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

353
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...
353
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.5K
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...
2.5K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

56
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...
56
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

137
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
137

You might also read

Related Articles

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

Sort by
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

A Graph-based Benchmark dataset for Printed Circuit Netlist Partitioning.

Scientific data·2026
Same author

Fine-grained classification of thoracic vertebral compression fractures based on multi-layer feature fusion and attention-guided patch recombination.

Biomedical engineering letters·2025
Same author

Incorporating multi-modal prompt learning into foundation models enhances predictability of visual fMRI responses to dynamic natural stimuli.

Journal of neural engineering·2025
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: Sep 17, 2025

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

8.1K

Sparse-view spectral CT reconstruction via a coupled subspace representation and score-based generative model.

Jie Guo1, Yizhong Wang1, Shaoyu Wang1

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

Quantitative Imaging in Medicine and Surgery
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for spectral computed tomography (CT) reconstruction, enhancing image quality from sparse-view data. The method effectively reduces artifacts and preserves details, offering a significant advancement for clinical applications.

Keywords:
Spectral computed tomography (spectral CT)low-rank subspace representationscore-based generative model (SGM)sparse-view reconstruction

More Related Videos

Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Related Experiment Videos

Last Updated: Sep 17, 2025

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

8.1K
Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

13.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Area of Science:

  • Medical imaging
  • Computed tomography
  • Image reconstruction

Background:

  • Spectral CT offers rich imaging data but suffers from artifacts in sparse-view scanning.
  • Conventional methods struggle with detail and edge preservation during reconstruction.
  • Artifacts in spectral CT can lead to misdiagnosis in clinical settings.

Purpose of the Study:

  • To develop a novel framework for sparse-view spectral CT reconstruction.
  • To enhance image quality by reducing artifacts and preserving fine details.
  • To integrate subspace decomposition with deep generative priors for improved reconstruction.

Main Methods:

  • Proposed an unsupervised reconstruction framework integrating subspace representation and a score-based generative model (SGM).
  • Decomposed spectral CT images into subspace components and eigen-images for dimensionality reduction.
  • Employed an alternating optimization algorithm to update coefficients and enforce consistency between measurements and learned priors.

Main Results:

  • Achieved at least a 3dB increase in PSNR and 2.54% in SSIM compared to Wavelet-SGM in simulations.
  • Demonstrated minimal error and closest results to ground truth in real data experiments.
  • Showcased promising performance in detail preservation and artifact reduction.

Conclusions:

  • The framework synthesizes model-driven low-rank priors and data-driven deep priors for mutual enhancement.
  • Achieved superior spectral CT reconstruction quality with exceptional detail preservation.
  • Introduced a robust and practical sparse-view spectral CT reconstruction technique for clinical use.