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

Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

310
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
310
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

149
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...
149
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

313
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
313
Instantaneous Acceleration01:16

Instantaneous Acceleration

7.5K
Acceleration is in the direction of the change in velocity, but it is not always in the direction of motion. When an object slows down, its acceleration is opposite to the direction of its motion. Although commonly referred to as deceleration, this causes confusion in our analysis as deceleration is not a vector, and does not point to a specific direction with respect to a coordinate system. Therefore, the term deceleration is not used. For example, when a subway train slows down, it...
7.5K
Rotation with Constant Angular Acceleration - I01:37

Rotation with Constant Angular Acceleration - I

6.5K
If angular acceleration is constant, then we can simplify equations of rotational kinematics, similar to the equations of linear kinematics. This simplified set of equations can be used to describe many applications in physics and engineering where the angular acceleration of a system is constant.
Using our intuition, we can begin to see how rotational quantities such as angular displacement, angular velocity, angular acceleration, and time are related to one another. For example, if a flywheel...
6.5K
Angular Velocity and Acceleration01:11

Angular Velocity and Acceleration

8.7K
We previously discussed angular velocity for uniform circular motion, however not all motion is uniform. Envision an ice skater spinning with their arms outstretched; when they pull their arms inward, their angular velocity increases. Additionally, think about a computer's hard disk slowing to a halt as the angular velocity decreases. The faster the change in angular velocity, the greater the angular acceleration. The instantaneous angular acceleration is defined as the derivative of...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Text-Guided Refinement Network for Multimodal Sentiment Analysis.

Entropy (Basel, Switzerland)·2025
Same author

An iterative-FBP dual-spectral CT reconstruction algorithm based on scatter modeling.

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

Fourier-enhanced high-order total variation (FeHOT) iterative network for interior tomography.

Physics in medicine and biology·2025
Same author

A model-based direct inversion network (MDIN) for dual spectral computed tomography.

Physics in medicine and biology·2024
Same author

Multi-scale dilated dense reconstruction network for limited-angle computed tomography.

Physics in medicine and biology·2023
Same author

Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography.

Physics in medicine and biology·2021
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: May 13, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

Basic acceleration technique with theoretical analysis on iterative algorithms for image reconstruction.

Shuhua Ji1, Boyan Ren1, Xing Zhao1,2

  • 1School of Mathematical Sciences, Capital Normal University, Beijing, China.

Journal of X-Ray Science and Technology
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

Enforcing nonnegativity in computed tomography (CT) image reconstruction accelerates convergence. New methods, like using absolute values for negative pixels, significantly improve image quality and reduce iteration time compared to setting them to zero.

Keywords:
CT reconstructionacceleration methodsimage processingterative reconstruction algorithmsthe non-negativity of pixel values

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

382
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.6K

Related Experiment Videos

Last Updated: May 13, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

382
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.6K

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Computational Imaging

Background:

  • Nonnegativity is a crucial prior in image reconstruction, especially for computed tomography (CT).
  • Current CT iterative algorithms (ART, SART, SIRT) handle negative pixel values inconsistently, impacting reconstruction quality and convergence.
  • Existing methods include zeroing negative values, regularization, or ignoring them, with varying effectiveness.

Purpose of the Study:

  • To establish a general framework demonstrating how enforcing nonnegativity accelerates image reconstruction convergence.
  • To propose and evaluate two novel, efficient acceleration techniques for CT iterative reconstruction.

Main Methods:

  • Developed a general framework for nonnegativity enforcement in iterative reconstruction.
  • Proposed two acceleration techniques: setting negative pixels to their absolute values and updating to previous estimates.
  • Integrated these techniques with Algebraic Reconstruction Technique (ART), Simultaneous ART (SART), and Simultaneous Iterative Reconstruction Technique (SIRT).
  • Tested on simulated (full-angle, limited-angle, noisy) and real CT data.

Main Results:

  • The proposed nonnegativity enforcement framework accelerates convergence towards the true solution.
  • Both proposed acceleration techniques demonstrated effectiveness in improving image quality.
  • The absolute value method significantly outperformed the zeroing method in both image quality (PSNR, SSIM) and iteration time.
  • Experiments confirmed results across ART, SART, and SIRT algorithms and various data types.

Conclusions:

  • Enforcing nonnegativity is a powerful strategy to accelerate CT image reconstruction.
  • The proposed absolute value technique offers a superior alternative to zeroing negative pixels, enhancing both image fidelity and computational efficiency.
  • These findings provide practical improvements for CT image reconstruction algorithms.