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...

You might also read

Related Articles

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

Sort by

Same author

Severe mitral valve regurgitation in a preschool boy following blunt abdominal trauma: a case report.

European heart journal. Case reports·2025

Same author

In Vitro Performance Assessment of a Bioprosthetic Individualized Aortic Valve Prosthesis for Infants and Toddlers.

ASAIO journal (American Society for Artificial Internal Organs : 1992)·2025

Same author

Quality of life in pediatric patients on a paracorporeal ventricular assist device with a novel mobile driving system.

JHLT open·2025

Same author

Neonatal enlargement of the aortic arch roof without cardiopulmonary bypass using ductal patency for lower body perfusion: impact on long-term growth, function and shape of the aortic arch†.

Interdisciplinary cardiovascular and thoracic surgery·2025

Same author

The Introduction of a New Mobile Driving Unit for a Ventricular Assist Device in a Pediatric Patient (EXCOR Active).

Journal of cardiovascular development and disease·2024

Same author

European Society of Organ Transplantation (ESOT) Consensus Statement on Machine Perfusion in Cardiothoracic Transplant.

Transplant international : official journal of the European Society for Organ Transplantation·2024

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 28, 2026

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

GPU-Accelerated Finite Element Method for Modelling Light Transport in Diffuse Optical Tomography.

Martin Schweiger¹

¹Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.

International Journal of Biomedical Imaging

|October 21, 2011

Summary

This summary is machine-generated.

We developed a GPU-accelerated finite element solver to speed up light transport calculations in scattering media. This significantly enhances diffuse optical tomography image reconstruction by improving computational efficiency.

More Related Videos

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Published on: May 26, 2015

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Related Experiment Videos

Last Updated: May 28, 2026

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Published on: May 26, 2015

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Area of Science:

Computational physics
Biomedical optics
Scientific computing

Background:

The forward model is critical for iterative image reconstruction in diffuse optical tomography (DOT).
Optimizing the computationally intensive forward solver is essential for efficient inverse problem solutions.
Scattering media pose significant computational challenges for light transport modeling.

Purpose of the Study:

To introduce a Graphics Processing Unit (GPU)-accelerated finite element forward solver.
To enhance the computational speed of light transport simulations in scattering media.
To improve the efficiency of image reconstruction in DOT.

Main Methods:

Developed a CUDA-based GPU implementation of the finite element forward solver.
Applied the solver to both time-domain and frequency-domain light transport problems.
Utilized sparse linear system evaluation on graphics hardware.

Main Results:

Achieved significant performance gains compared to CPU-based implementations.
Demonstrated speedups of approximately 10x for double-precision and over 20x for single-precision computations.
Observed that performance gains increase with mesh complexity, particularly at high resolutions.

Conclusions:

The GPU-accelerated solver offers substantial computational speedups for light transport in scattering media.
This advancement is crucial for improving the efficiency of diffuse optical tomography.
The solver's performance is highly dependent on mesh resolution, benefiting complex models.