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

Four-dimensional on-beam computed tomography reconstruction using projection-difference images.

Physics in medicine and biology·2026

Same author

TREATMENT EFFECT HETEROGENEITY AND IMPORTANCE MEASURES FOR MULTIVARIATE CONTINUOUS TREATMENTS.

The annals of applied statistics·2026

Same author

In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI.

NPJ Parkinson's disease·2026

Same author

Source-free domain adaptation for multi-institutional chest X-ray images.

Journal of applied clinical medical physics·2026

Same author

Diffusion prior-guided implicit neural representation for metal artifact reduction in sparse-view CT reconstruction.

Physics in medicine and biology·2026

Same author

X-ray Source Motion Deblurring Framework for Fast Scan in Digital Breast Tomosynthesis.

IEEE transactions on bio-medical engineering·2026

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026

Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026

Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization.

Heejun Shin¹, Taehee Kim¹, Jongho Lee²

¹Artificial Intelligence Engineering Division, Radisen Co. Ltd., Seoul, Republic of Korea.

Computers in Biology and Medicine

|March 2, 2025

Summary

This summary is machine-generated.

A new method called FACT improves cone beam computed tomography (CBCT) reconstruction using fewer images. This technique enhances image quality and speeds up the process, reducing radiation exposure for patients.

Keywords:

Cone-beam CT Image reconstruction Implicit neural representation

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

Related Experiment Videos

Last Updated: Apr 30, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

Area of Science:

Medical Imaging
Computational Imaging
Radiology

Background:

Cone beam computed tomography (CBCT) reconstructs images from multiple projections.
Reducing projection views in CBCT is challenging due to ill-posed inverse problems.
Existing methods like Neural Attenuation Field (NAF) show promise but can be improved.

Purpose of the Study:

To develop a faster and more accurate sparse-view CBCT reconstruction method.
To minimize radiation exposure by reducing the number of projection views.
To enhance reconstruction quality and optimization speed.

Main Methods:

Proposed the Fast and Accurate Sparse-view CBCT Reconstruction (FACT) method.
Utilized meta-training of a neural network and hash-encoder with limited scans (15 views).
Implemented a novel regularization technique for detailed anatomical structure reconstruction.

Main Results:

FACT achieved superior reconstruction quality compared to conventional algorithms.
The method demonstrated significantly faster optimization speeds.
Effective reconstruction was validated across various body parts (chest, head, abdomen) and CT vendors (Siemens, Phillips, GE).

Conclusions:

The FACT method offers improved performance in sparse-view CBCT reconstruction.
It enables reduced radiation exposure without compromising image quality.
FACT represents a significant advancement for efficient and accurate CBCT imaging.