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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

171
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...
171
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

62
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
62
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85

You might also read

Related Articles

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

Sort by
Same author

Leveraging CT-based online adaptive radiotherapy for dose escalation in hypofractionated radiotherapy for locally advanced unresectable lung cancer.

Frontiers in oncology·2026
Same author

Multigene germline testing for epithelial ovarian cancer in China.

Cancer·2026
Same author

Preventive effect of PC-SOD on oxaliplatin-induced peripheral neuropathy in rats.

BMC neuroscience·2026
Same author

Evaluating a CBCT Correction Algorithm for Adaptive Radiotherapy.

Technology in cancer research & treatment·2026
Same author

An analytic, moment-based method to estimate orthopositronium lifetimes in positron annihilation lifetime spectroscopy measurements.

Bio-algorithms and med-systems·2026
Same author

A Novel Approach to In Vivo EPR Spectroscopy for Repeatable Assessments of Oxygenation Levels in Tumors at Any Depth: Preliminary Feasibility Studies Utilizing a Multisite Oxygen Sensor Inside HDR Brachytherapy Needles.

Advances in experimental medicine and biology·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
Same journal

CLID: A contrastive learning approach for multi-class classification of breast cancer histopathological images with imbalanced distribution.

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

Related Experiment Video

Updated: Jun 4, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

A fully linearized ADMM algorithm for optimization based image reconstruction.

Zhiwei Qiao1, Gage Redler2, Boris Epel3

  • 1School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China.

Journal of X-Ray Science and Technology
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

We developed a fully linearized alternating direction method of multipliers (FL-ADMM) algorithm for optimization-based image reconstruction. This new method offers a faster and more universal solution compared to traditional algorithms, avoiding complex step-size calculations.

Keywords:
Fully linearized ADMMcomputed tomographyimage reconstructionoptimizationtotal variation

More Related Videos

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.1K
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

Related Experiment Videos

Last Updated: Jun 4, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.1K
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

Area of Science:

  • Medical Imaging
  • Computational Science
  • Optimization Algorithms

Background:

  • Optimization-based image reconstruction is crucial in medical imaging but faces challenges with large-scale, non-smooth models.
  • Existing solvers like ADMM often require complex sub-problem solutions or specific matrix structures.

Purpose of the Study:

  • To develop a simple, convergent, and universally applicable solver for optimization models in image reconstruction.
  • To address the limitations of existing methods, particularly the time-consuming line search for step-size determination.

Main Methods:

  • Proposed a fully linearized alternating direction method of multipliers (FL-ADMM) algorithm.
  • Developed FL-ADMM instances for total variation (TV) models in 2D computed tomography (CT).
  • Validated the FL-ADMM algorithm's performance and convergence factors.

Main Results:

  • The FL-ADMM algorithm accurately solves optimization models in image reconstruction.
  • Demonstrated the algorithm's effectiveness on 2D CT total variation models.
  • Identified key factors influencing the convergence rate of the FL-ADMM algorithm.

Conclusions:

  • FL-ADMM is a simple, effective, convergent, and universal solver for optimization-based image reconstruction.
  • It eliminates the need for time-consuming step-size line searches and special sparse transform requirements.
  • FL-ADMM serves as a rapid prototyping tool for advanced image reconstruction.