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

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

893
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...
893
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

853
Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
853
Computed Tomography01:10

Computed Tomography

7.6K
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...
7.6K
Positron Emission Tomography01:29

Positron Emission Tomography

6.2K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Community-based tuberculosis screening with computer-aided detection technology alone and in combination with point-of-care C-reactive protein testing: a paired screen-positive trial.

The Lancet. Infectious diseases·2026
Same author

Assessment of modifications to a blind-sweep ultrasound protocol for improved lower-uterus imaging by novice operators.

Scientific reports·2026
Same author

AI-based histopathology analysis predicts checkpoint inhibitor response in advanced melanoma and identifies patterns associated with response.

European journal of cancer (Oxford, England : 1990)·2026
Same author

Large Language Model Automated Extraction of Clinical Signs and Symptoms From Emergency Department Reports for Machine Learning Prediction Models: Development and Validation Study.

JMIR medical informatics·2026
Same author

DeepMultiConnectome: Deep multi-task prediction of structural connectomes directly from diffusion MRI tractography.

NeuroImage·2026
Same author

Deep learning motion correction of quantitative stress perfusion cardiovascular magnetic resonance.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026

Related Experiment Video

Updated: May 2, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.2K

DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration.

Sascha E A Muenzing1, Bram van Ginneken2, Max A Viergever1

  • 1Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht, The Netherlands.

Medical Image Analysis
|February 22, 2014
PubMed
Summary

DIRBoost enhances deformable image registration (DIR) accuracy by detecting and correcting errors. This machine learning-inspired boosting algorithm significantly improves registration results across multiple methods and datasets.

Keywords:
BoostingDeformable image registrationMachine learningPattern recognition

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.3K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.6K

Related Experiment Videos

Last Updated: May 2, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.2K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.3K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.6K

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computer Vision

Background:

  • Deformable image registration (DIR) is crucial for aligning medical images, but existing methods often struggle with accuracy.
  • Identifying and correcting local registration errors remains a challenge in improving DIR performance.

Purpose of the Study:

  • To introduce DIRBoost, a novel boosting algorithm designed to enhance the accuracy of deformable image registration.
  • To evaluate the effectiveness of DIRBoost in reducing registration errors across various DIR methods and datasets.

Main Methods:

  • DIRBoost employs automatic registration error detection to identify inaccurately aligned regions.
  • Iterative registrations are performed on erroneous areas using boosting masks applied to both fixed and moving images.
  • The algorithm was validated on three established DIR methods (ANTS gSyn, NiftyReg, DROP) using three independent pulmonary image datasets.

Main Results:

  • DIRBoost significantly reduced registration errors across all tested DIR algorithms and datasets.
  • Improvements in registration accuracy ranged from 5% to 34%, demonstrating consistent performance enhancement.
  • The boosting approach effectively addressed local registration inaccuracies.

Conclusions:

  • DIRBoost offers a robust and effective method for improving deformable image registration accuracy.
  • The algorithm shows broad applicability, enhancing performance of diverse DIR techniques.
  • DIRBoost represents a significant advancement in medical image alignment technologies.