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

You might also read

Related Articles

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

Sort by

Same author

Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score.

American journal of preventive cardiology·2026

Same author

A call for action: The need to quantify the "-itis" in primary sclerosing cholangitis.

Journal of hepatology·2026

Same author

Applications of Artificial Intelligence in Urodynamic Data Interpretation: A Narrative Review.

Neurourology and urodynamics·2026

Same author

MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma.

Cancer research communications·2026

Same author

EchoAtlas: A Conversational, Multi-View Vision-Language Foundation Model for Echocardiography Interpretation and Clinical Reasoning.

medRxiv : the preprint server for health sciences·2026

Same author

BWS-Net: An Optimal Deep Learning Architecture for the Anterior Bladder Wall Segmentation using Ultrasound Imaging.

IEEE journal of biomedical and health informatics·2026

Search research articles

Related Experiment Video

Updated: Mar 17, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Fully Automated and Robust Tracking of Transient Waves in Structured Anatomies Using Dynamic Programming.

Zeynettin Akkus¹, Mahdi Bayat², Mathew Cheong²

¹Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, Minnesota, USA; Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.

Ultrasound in Medicine & Biology

|July 19, 2016

Summary

A new dynamic programming method accurately measures tissue stiffness by calculating transient transverse wave speed (TWS). This robust technique outperforms traditional time-of-flight methods, especially with in vivo data.

Keywords:

Bladder Dynamic programming Time-of-flight Ultrasound vibrometery Wall stiffness Wave tracking

More Related Videos

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Related Experiment Videos

Last Updated: Mar 17, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Area of Science:

Biomedical Engineering
Medical Imaging
Acoustics

Background:

Tissue stiffness is a key indicator of pathology.
Quantifying mechanical properties like transient transverse wave speed (TWS) is crucial for diagnosis.
Existing time-of-flight methods struggle with in vivo data due to noise and artifacts.

Purpose of the Study:

To develop a robust and automated method for estimating TWS in tissues.
To overcome limitations of current time-of-flight techniques for in vivo applications.

Main Methods:

A novel dynamic programming approach was developed to estimate TWS.
The method requires known tissue geometries.
Validation was performed using ultrasound bladder vibrometry data from an in vivo study.

Main Results:

The dynamic programming method demonstrated robust TWS estimation.
Performance was compared against traditional time-of-flight techniques.
The new method showed superior accuracy and reliability.

Conclusions:

A robust and accurate TWS detection method has been presented.
This dynamic programming approach effectively addresses the challenges faced by time-of-flight methods.
The technique shows promise for in vivo tissue characterization.