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

Computed Tomography

Computed Tomography

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...

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

A Covalent Targeted Platform for Protein Dynamic Tracking.

Analytical chemistry·2026

Same author

Caged Ligand-Decorated Near-Infrared Photosensitizer with In Vivo Albumin-Hijacking Capacity for Tumor-Targeted Hypoxia-Tolerant Photoimmunotherapy of Cancer.

Journal of the American Chemical Society·2026

Same author

Functionalized two-dimensional nanochannel membranes to distinguish methylated/unmethylated peptides for sensing cellular G9a protein.

Chemical science·2026

Same author

Construction and Validation of a Model for Predicting Cervical Intraepithelial Neoplasia Grade II+: A Cross-Sectional Population Study via Machine Learning.

International journal of women's health·2025

Same author

Photochemical Generation of Peroxynitrite: Concurrent Inhibition of Glycolysis and Glutamine Metabolism for Enhanced Metabolic Therapy.

Journal of the American Chemical Society·2025

Same author

Combination of red blood cell distribution width and platelet-to-lymphocyte ratio for predicting severity of diabetic retinopathy.

International journal of ophthalmology·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: May 13, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

GPU based iterative cone-beam CT reconstruction using empty space skipping technique.

Xing Zhao¹, Jing-Jing Hu, Tao Yang

¹The CT laboratory, School of Mathematical Sciences, Capital Normal University, Beijing, China. zhaoxing_1999@yahoo.com

Journal of X-Ray Science and Technology

|March 20, 2013

Summary

This summary is machine-generated.

This study introduces an accelerated iterative reconstruction method for high-resolution cone-beam CT (CBCT) using GPU acceleration, empty space skipping, and multi-resolution techniques. The approach significantly enhances reconstruction performance and reduces computational demands while preserving image quality.

More Related Videos

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

Related Experiment Videos

Last Updated: May 13, 2026

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

Published on: December 2, 2013

Area of Science:

Medical Imaging
Computational Science

Background:

Iterative reconstruction of high-resolution cone-beam CT (CBCT) data is computationally intensive.
Existing methods face challenges with high computer cycle and memory demands.

Purpose of the Study:

To improve the performance of iterative algorithms for CBCT reconstruction.
To reduce computational and memory requirements for high-resolution CBCT imaging.

Main Methods:

Proposed an acceleration approach integrating GPU acceleration, empty space skipping, and multi-resolution techniques.
Divided the volume into blocks, identified empty blocks using low-resolution reconstruction and segmentation, and processed non-empty blocks.
Implemented the entire process in parallel on a GPU.

Main Results:

Significantly improved the performance of iterative reconstruction for CBCT.
Achieved substantial savings in computation, memory requirements, and data transfer.
Maintained high image quality compared to conventional GPU-based approaches.

Conclusions:

The proposed method offers an efficient solution for high-resolution CBCT reconstruction.
The integration of GPU acceleration, empty space skipping, and multi-resolution techniques effectively reduces computational load.
This approach enables faster and more memory-efficient CBCT imaging without compromising image quality.