Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

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...

You might also read

Related Articles

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

Sort by

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

Polymeric particle-based antigen delivery system: From immunological engineering to clinical translation.

International journal of pharmaceutics: X·2026

Same author

Human anthrax vaccines: Current status and future prospects.

Human vaccines & immunotherapeutics·2025

Same author

A springback TV algorithm for image reconstruction from sparse view data in CT.

Physics in medicine and biology·2025

Same author

A novel high order directional total variation algorithm of EPR imaging for fast scan.

Journal of X-ray science and technology·2025

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

Search research articles

Related Experiment Video

Updated: Nov 9, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

A simple and fast ASD-POCS algorithm for image reconstruction.

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

Journal of X-Ray Science and Technology

|April 12, 2021

Summary

This summary is machine-generated.

A simplified adaptive steepest descent projection onto convex set (ASD-POCS) algorithm accelerates computed tomography (CT) image reconstruction. This new method maintains reconstruction accuracy while significantly improving processing speed for sparse and noisy data.

Keywords:

ASD-POCS TV gradient image reconstruction matrix-form expression total variation

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

Related Experiment Videos

Last Updated: Nov 9, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

Area of Science:

Medical Imaging
Image Reconstruction
Computational Science

Background:

Adaptive Steepest Descent Projection Onto Convex Set (ASD-POCS) is effective for total variation (TV) norm minimization in computed tomography (CT) image reconstruction.
Existing ASD-POCS implementations face challenges due to complex TV-type norm gradient expressions, leading to slower image reconstruction speeds, especially with sparse or noisy data.

Purpose of the Study:

To develop and validate a simplified and faster ASD-POCS algorithm for CT image reconstruction.
To address the implementation complexity and speed limitations of current ASD-POCS methods.

Main Methods:

Derived simple matrix-form expressions for TV-type norm gradients, including TV, adaptive weighted TV (awTV), total p-variation (TpV), and high order TV (HOTV).
Utilized matrix representations and term combinations for gradient derivation and analysis of hidden relations.
Tested the simplified ASD-POCS (S-ASD-POCS) algorithm using sparse-view CT projections with standard phantoms (Shepp-Logan, FORBILD) and a real CT image.

Main Results:

The S-ASD-POCS algorithm achieved comparable reconstruction accuracy to the original ASD-POCS.
Experimental results demonstrated a significant speed-up of 1.8-2.7 times for the overall ASD process.
The simplified gradient expressions effectively reduced implementation complexity.

Conclusions:

The derived simple matrix expressions for TV-type norm gradients simplify ASD-POCS implementation and accelerate the reconstruction process.
The S-ASD-POCS algorithm offers a faster and more efficient solution for CT image reconstruction with sparse or noisy data.
A general gradient expression was demonstrated, enabling the potential application of ASD-POCS to broader image reconstruction fields with enhanced computational speed.