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

Computed Tomography01:10

Computed Tomography

9.2K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

511
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...
511

You might also read

Related Articles

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

Sort by
Same author

Time-averaged simulated microgravity ameliorates tau-induced deficit in Drosophila melanogaster.

NPJ microgravity·2026
Same author

A chromatin-associated pool of Aurora A controls kinetochore-microtubule attachments to ensure chromosome biorientation.

Science advances·2026
Same author

Design and Validation of a Hand Interossei Muscle Dynamometer (HIMDNA) for Finger Abduction and Adduction Strength Measurement.

Annals of biomedical engineering·2026
Same author

Multiplexed Detection of Cancer Biomarker Using a Dual-Mode Colorimetric-SERS Lateral Flow Immunoassay Based on Elongated Rod Ag Nanoshell (ERNS) SERS Tags.

Biosensors·2026
Same author

Hardware-independent control for partial gravity simulation using a 2-DOF robotic device.

Scientific reports·2026
Same author

Comparison of clinostat control strategies to achieve simulated microgravity with uniform gravity vector distribution.

NPJ microgravity·2026

Related Experiment Video

Updated: Mar 6, 2026

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography
08:47

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Published on: March 15, 2021

4.7K

Advanced stacked modeling techniques for material porosity estimation via high-resolution computed tomography

Bubryur Kim1, Sri Preethaa K R2, Yuvaraj Natarajan3

  • 1School of Space Engineering Sciences, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, Korea.

Scientific Reports
|March 4, 2026
PubMed
Summary

A novel deep convolutional neural network (DeepCNN) framework automates concrete porosity estimation from 2D CT scans. This advanced method offers accurate, efficient material characterization, significantly improving upon traditional techniques.

Keywords:
Convolution neural networkMaterial characterizationPorosity predictionStacked modelingVacuum pycnometer

More Related Videos

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

13.1K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

10.3K

Related Experiment Videos

Last Updated: Mar 6, 2026

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography
08:47

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Published on: March 15, 2021

4.7K
Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

13.1K
3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
07:01

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

10.3K

Area of Science:

  • Materials Science
  • Civil Engineering
  • Computer Vision

Background:

  • Traditional concrete porosity measurement is manual, time-consuming, and lacks robustness in low-resolution or noisy imaging.
  • Accurate porosity assessment is critical for concrete material characterization and performance prediction.

Purpose of the Study:

  • To develop an automated framework for concrete porosity estimation using deep convolutional neural networks (DeepCNN) and 2D CT scan images.
  • To enhance the accuracy and efficiency of porosity measurement, especially under challenging imaging conditions.

Main Methods:

  • A DeepCNN architecture with a multi-stage feature extractor and SPP-based neck was trained on augmented CT images.
  • Image processing included automated ROI detection, normalization, and class-specific filtering.
  • A rule-based adaptive thresholding (RBAT) strategy was employed for material classification and porosity estimation.

Main Results:

  • The framework accurately estimated porosity across various concrete types (CM, GM, UHPC), with deviations within 1.3-1.5% compared to vacuum pycnometer measurements.
  • The DeepCNN classifier achieved a precision-recall AUC of 1.0.
  • The automated method demonstrated robustness under low resolution and noisy imaging conditions.

Conclusions:

  • The proposed hybrid framework offers an accurate, automated, and computationally efficient solution for concrete porosity assessment.
  • This approach is suitable for practical and industrial CT-based material characterization workflows.
  • The study highlights the potential of DeepCNN for advanced non-destructive material analysis.