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

Minimally Invasive, Fertility-Sparing Surgical Management of Multiple Bilateral Ovarian Dermoid Cysts.

Fertility and sterility·2026

Same author

Multiplexed optoacoustic tracking and magnetic actuation of labeled blood cells in living mice.

Science advances·2026

Same author

Transcranial pulse stimulation modulates spectral signatures of Alzheimer's disease in the 3×Tg-AD mouse model.

Alzheimer's research & therapy·2026

Same author

Data-driven super-resolution optoacoustic imaging via physically encoded signal acquisition.

Research square·2026

Same author

Quantitative in-vivo full-waveform ultrasound tomography workflow integrating reflection imaging and resolution analysis.

Physics in medicine and biology·2026

Same author

Localization-based techniques for super-resolution imaging of vascular dynamics.

Innovation (Cambridge (Mass.))·2026

Same journal

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026

Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026

Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026

Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026

Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026

Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Published on: November 4, 2014

Three-dimensional optoacoustic reconstruction using fast sparse representation.

Yiyong Han, Lu Ding, Xosé Luis Deán Ben

|March 2, 2017

Summary

This summary is machine-generated.

A new fast algorithm for 3D optoacoustic tomography improves image quality. This method, L1-GDBB, reduces artifacts and enhances contrast more efficiently than previous techniques.

More Related Videos

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research

Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research

Published on: June 8, 2022

Related Experiment Videos

Last Updated: Mar 7, 2026

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

Published on: November 4, 2014

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research

Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research

Published on: June 8, 2022

Area of Science:

Medical Imaging
Biomedical Engineering
Computational Imaging

Background:

Optoacoustic tomography (OAT) imaging quality is often degraded by insufficient spatial sampling, leading to artifacts and reduced contrast.
Sparsity-based reconstructions can enhance image quality in OAT but are computationally intensive, limiting their application, especially in 3D.
Existing methods like L2-norm regularization and gradient descent with backtracking line search have limitations in speed and artifact reduction.

Purpose of the Study:

To develop a computationally efficient and effective algorithm for 3D optoacoustic tomography reconstruction.
To improve image contrast and reduce artifacts in 3D OAT reconstructions.
To enable faster and higher-quality 3D optoacoustic imaging.

Main Methods:

Development of a novel sparsity-based reconstruction algorithm for 3D optoacoustic tomography, termed L1-GDBB (gradient descent with Barzilai-Borwein line search).
Utilized gradient descent optimization with Barzilai-Borwein line search for accelerated convergence.
Validated the algorithm through both computational simulations and experimental phantom studies.

Main Results:

The L1-GDBB algorithm demonstrated a fourfold increase in reconstruction speed compared to previous L1-norm regularized methods using gradient descent with backtracking.
L1-GDBB produced images with significantly fewer artifacts and improved contrast compared to standard L2-norm regularization and back-projection methods.
The algorithm successfully reconstructed high-quality 3D optoacoustic tomography images.

Conclusions:

The proposed L1-GDBB algorithm offers a significant advancement in 3D optoacoustic tomography reconstruction, overcoming previous computational limitations.
This faster and more effective reconstruction method has the potential to broaden the clinical and research applications of 3D OAT.
L1-GDBB provides a superior alternative for achieving high-fidelity 3D optoacoustic imaging.