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

Accessory Structures of the Eye01:17

Accessory Structures of the Eye

4.8K
Optical perception, or vision, is an extraordinary sense dependent on converting light signals received via the ocular organs. These organs, known as eyes, are securely positioned within the bony cavities of the skull, called orbits. The orbits serve a dual purpose: a protective shield for the ocular globes and a stable attachment point for the soft ocular tissues. The eye's external protective mechanisms include the eyelids, which are edged with lashes that act as a barrier against foreign...
4.8K
Orthogonal Trajectories01:26

Orthogonal Trajectories

271
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
271
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

12.3K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
12.3K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

828
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...
828

You might also read

Related Articles

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

Sort by
Same author

ACR Appropriateness Criteria® Horner Syndrome.

Journal of the American College of Radiology : JACR·2025
Same author

ACR Appropriateness Criteria® Vision Loss.

Journal of the American College of Radiology : JACR·2025
Same author

Papillary thyroid carcinoma metastasis to the pituitary: A case report.

Clinical imaging·2021
Same author

Subperiosteal Hematoma of the Orbit: A Variety of Presentations.

Journal of radiology case reports·2019
Same author

Case 206: persistent hypertrophic primary vitreous.

Radiology·2014
Same author

Masticator space: imaging anatomy for diagnosis.

Otolaryngologic clinics of North America·2012

Related Experiment Video

Updated: Apr 19, 2026

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

3.7K

Orbital imaging: a pattern-based approach.

Daniel E Meltzer1

  • 1Mount Sinai Hospital, Roosevelt Division, 1000 Tenth Ave, Suite 4B-14, New York, NY 10019, USA.

Radiologic Clinics of North America
|December 6, 2014
PubMed
Summary
This summary is machine-generated.

Interpreting orbital imaging can be challenging due to overlapping disease features. A pattern-based approach using computed tomography (CT) and magnetic resonance imaging (MRI) aids differential diagnosis.

Keywords:
Computed tomographyExtraconalIntraconalIntraocularLymphoproliferative diseaseMagnetic resonance imagingMetastasis

More Related Videos

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

945
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K

Related Experiment Videos

Last Updated: Apr 19, 2026

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

3.7K
Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

945
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.9K

Area of Science:

  • Radiology
  • Ophthalmology
  • Medical Imaging

Background:

  • Differential diagnosis for orbital imaging is complex.
  • Orbital diseases present overlapping clinical and imaging features.
  • Radiologists face challenges in providing concise diagnoses.

Purpose of the Study:

  • To present a pattern-based approach for interpreting orbital cross-sectional imaging.
  • To assist radiologists in generating differential diagnoses for orbital findings.
  • To improve the diagnostic accuracy of orbital CT and MRI.

Main Methods:

  • Categorizing orbital disease entities into distinct patterns.
  • Utilizing a pattern-based interpretation strategy for orbital imaging.
  • Integrating clinical history with imaging findings.

Main Results:

  • A pattern-based approach simplifies the interpretation of orbital imaging.
  • This method facilitates the generation of relevant differential diagnoses.
  • Overlapping features of orbital diseases can be managed effectively.

Conclusions:

  • A pattern-based approach is valuable for interpreting orbital CT and MRI.
  • This strategy aids clinicians by providing a focused differential diagnosis.
  • Effective diagnosis relies on combining imaging patterns with clinical information.