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

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by

Same author

Interaction in vivo and in vitro of the metastasis-inducing S100 protein, S100A4 (p9Ka) with S100A1.

The Journal of biological chemistry·2001

Same author

Hyperactive tendon reflexes in spastic multiple sclerosis: measures and mechanisms of action.

Archives of physical medicine and rehabilitation·2000

Same author

Synthesis of unsaturated carboacyclic nucleoside analogues via Mitsunobu reactions.

Nucleosides, nucleotides & nucleic acids·2000

Same author

Synthesis and cytokine modulation properties of pyrrolo[2, 3-d]-4-pyrimidone nucleosides.

Journal of medicinal chemistry·2000

Same author

Reciprocal information flow between prefrontal cortex and ventral tegmental area in an animal model of schizophrenia.

Neuroreport·2000

Same author

The "waiting period" of sensory and motor axons in early chick hindlimb: its role in axon pathfinding and neuronal maturation.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2000

Same journal

Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

International journal of biomedical imaging·2026

Same journal

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026

Same journal

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

International journal of biomedical imaging·2026

Same journal

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.

International journal of biomedical imaging·2026

Same journal

Personalized PET Imaging in Gastric Cancer: An Umbrella Review of Meta-Analyses to Guide Radiopharmaceutical Selection and Clinical Indication.

International journal of biomedical imaging·2026

Same journal

Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

International journal of biomedical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Elasto-mammography: Theory, Algorithm, and Phantom Study.

Z G Wang¹, Y Liu, L Z Sun

¹Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA 52242, USA.

International Journal of Biomedical Imaging

|November 21, 2012

Summary

This summary is machine-generated.

Elasto-mammography generates breast elastograms using X-ray mammography. This novel imaging method accurately characterizes tissue elastic moduli, offering a robust approach for breast tissue analysis.

More Related Videos

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: May 16, 2026

Clinical Imaging of Microwave Mammography

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Area of Science:

Medical Imaging
Biophysics
Biomaterials

Background:

Conventional X-ray mammography is a standard breast imaging technique.
Elastography provides valuable information about tissue mechanical properties.
Integrating elastography with mammography could enhance breast tissue characterization.

Purpose of the Study:

To propose and validate a novel imaging modality framework, elasto-mammography.
To generate elastograms of breast tissues using conventional X-ray mammography.
To estimate the elastic moduli of heterogeneous breast tissues.

Main Methods:

Displacement information extracted from mammography projections before and after compression.
Development of a specific elastography reconstruction algorithm.
Validation using numerical breast phantoms with varying noise, geometric mismatch, and elastic contrast ratios.

Main Results:

Elasto-mammography successfully generates elastograms of breast tissues.
The developed algorithm accurately estimates elastic moduli of heterogeneous tissues.
The methodology demonstrated stability and robustness in numerical simulations.

Conclusions:

Elasto-mammography is a feasible and promising imaging modality.
The technique enables robust characterization of breast tissue elastic properties.
This framework has the potential to improve breast tissue analysis and diagnosis.