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 for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

166
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
166
Computed Tomography01:10

Computed Tomography

7.7K
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...
7.7K
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

167
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
167
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

253
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
253

You might also read

Related Articles

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

Sort by
Same author

Sex differences in brain frailty measures and outcomes after endovascular thrombectomy: ESCAPE-NA1 analysis.

Journal of the neurological sciences·2026
Same author

Treatment-Related Factors Associated with Hemorrhagic Transformation and Hemorrhagic Subtypes after Mechanical Thrombectomy for Acute Ischemic Stroke.

AJNR. American journal of neuroradiology·2026
Same author

RNS60 RESCUE Trial in Acute Ischemic Stroke: Post Hoc Analysis in Participants Enrolled <12 Hours Since Last Known Well.

Stroke (Hoboken, N.J.)·2026
Same author

MRI measurement of the delayed secondary ischaemic injury following endovascular thrombectomy: results from the REPERFUSE-NA1 study.

European stroke journal·2026
Same author

Safety of endovascular shunting for normal pressure hydrocephalus from a prospective, multicenter, single-arm study.

Journal of neurointerventional surgery·2026
Same author

High-Resolution Vessel Wall Imaging Can Differentiate Between Branch Atheromatous Disease From Small Vessel Ischemic Disease.

Journal of the American Heart Association·2026

Related Experiment Video

Updated: Dec 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.2K

Detecting Large Vessel Occlusion at Multiphase CT Angiography by Using a Deep Convolutional Neural Network.

Matthew T Stib1, Justin Vasquez1, Mary P Dong1

  • 1From the Departments of Diagnostic Imaging (M.T.S., M.J., J.L.B., G.L.B., R.A.M.), Diagnostic Imaging (A.D.Y.), and Neurosurgery (M.J., R.A.M.), Warren Alpert School of Medicine at Brown University, Rhode Island Hospital, 593 Eddy St, APC 701, Providence, RI 02903; Department of Computer Science, Brown University, Providence, RI (J.V., M.P.D., Y.H.K., S.S.S., H.J.T., A.W., H.L.C.W., C.E., U.C.); and the Norman Prince Neuroscience Institute, Rhode Island Hospital, Providence, RI (M.J., R.A.M.).

Radiology
|September 29, 2020
PubMed
Summary

Deep learning models can detect large vessel occlusion (LVO) stroke using CT angiography. Combining multiple phases significantly improves diagnostic performance for this time-sensitive condition.

More Related Videos

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.3K
A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

405

Related Experiment Videos

Last Updated: Dec 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.2K
Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
09:32

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Published on: December 9, 2021

3.3K
A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis
09:36

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

405

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Large vessel occlusion (LVO) stroke is a critical medical emergency requiring rapid diagnosis and treatment.
  • Emergent endovascular therapy is crucial for reducing morbidity and mortality in LVO stroke patients.
  • Deep learning offers potential for faster LVO detection and improved treatment times.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) for detecting LVOs.
  • The CNN was designed to analyze multiphase CT angiography (CTA) examinations.

Main Methods:

  • A multicenter retrospective study included 540 adult patients with suspected acute ischemic stroke.
  • CT angiography data underwent preprocessing, including vasculature segmentation and maximum intensity projection creation.
  • Seven experimental configurations using combinations of three CTA phases (arterial, peak venous, late venous) were tested.

Main Results:

  • A single CTA phase achieved an AUC of 0.74, with 77% sensitivity and 71% specificity.
  • Utilizing all three CTA phases together significantly improved performance, yielding an AUC of 0.89, 100% sensitivity, and 77% specificity (P = .01).
  • Combinations including delayed phases (phases 1&3, phases 2&3) also showed significant improvements over single-phase CTA (P = .03).

Conclusions:

  • A deep learning model effectively detects LVO presence in CTA.
  • Incorporating delayed phases in multiphase CTA enhances the diagnostic performance of the deep learning model.
  • This approach holds promise for accelerating LVO diagnosis and treatment initiation.