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

9.3K
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...
9.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

565
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...
565
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.5K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.5K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

1.1K
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...
1.1K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.9K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.9K
Positron Emission Tomography01:29

Positron Emission Tomography

7.9K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
7.9K

You might also read

Related Articles

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

Sort by
Same author

MCEPANet: A connectivity-edge guided attention network for robust medical image segmentation with multi-scale boundary preservation.

Biomedical physics & engineering express·2026
Same author

Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.

IEEE transactions on medical imaging·2026
Same author

Clinical Metadata Guided Limited-Angle CT Image Reconstruction.

IEEE transactions on medical imaging·2026
Same author

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

Quantitative imaging in medicine and surgery·2026
Same author

Machine learning-driven nanoparticle-enhanced paper chromogenic array sensor approach for detecting sub-lethally injured Salmonella in low moisture food.

Food research international (Ottawa, Ont.)·2026
Same author

ISTNet: a multi-scale transformer-based architecture for malaria cell classification.

Medical & biological engineering & computing·2026
Same journal

Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting.

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

Attention based multi-scale edge-aware segmentation and convolutional transformer framework for automated glaucoma detection from fundus images.

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

Improving the robustness of radiomic features to patient size variations in CBCT imaging for radiotherapy.

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

DH-OOD: A decoupled hybrid framework for robust skin lesion classification via semantic-structural fusion.

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

Development and evaluation of deep learning models for automatic coronary stenosis segmentation in X-ray angiography.

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

Projection-domain reconstruction of patient-specific panoramic images from CBCT projection data.

Journal of X-ray science and technology·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

7.0K

Interior tomography with curvelet-based regularization.

Baodong Liu1,2, Alexander Katsevich3, Hengyong Yu4

  • 1Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.

Journal of X-Ray Science and Technology
|September 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new compressive sensing (CS) method for interior computed tomography (CT) using curvelet transforms. The novel approach enhances image reconstruction stability and uniqueness in challenging CT scenarios.

Keywords:
Computed tomography (CT)curvelet transforminterior tomographylocal reconstructionradon transformregularization method

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

8.6K
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

14.6K

Related Experiment Videos

Last Updated: Mar 15, 2026

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools
07:58

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

7.0K
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

8.6K
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

14.6K

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • The interior problem in computed tomography (CT) involves reconstructing images from limited, truncated projection data, which is common in practical applications.
  • Unconstrained solutions to the interior problem are often non-unique, necessitating advanced methods for stable and accurate reconstruction.
  • Prior knowledge- and compressive sensing (CS)-based methods, including interior tomography, have emerged to address these limitations.

Purpose of the Study:

  • To propose a novel compressive sensing (CS)-based method for solving the interior tomography problem.
  • To leverage the unique properties of the curvelet transform for regularizing interior CT reconstruction.
  • To enhance the uniqueness and stability of image reconstruction from local truncated projections.

Main Methods:

  • A new CS-based method employing the curvelet transform for interior tomography is presented.
  • The curvelet transform is utilized to represent 2D images, with coefficients used for regularization.
  • A curvelet frame-based regularization method (CFRM) is developed, splitting coefficients based on data visibility and applying differential regularization parameters.

Main Results:

  • Numerical experiments demonstrate the feasibility of the proposed curvelet frame-based regularization method (CFRM).
  • The method shows promise in addressing the non-uniqueness and instability issues inherent in interior CT problems.
  • The approach effectively utilizes curvelet transform coefficients for improved image reconstruction.

Conclusions:

  • The proposed curvelet frame-based regularization method (CFRM) offers a viable solution for the interior tomography problem.
  • This CS-based approach enhances the stability and uniqueness of reconstructions from local truncated projections.
  • The findings support the application of curvelet transforms in advanced medical imaging reconstruction techniques.