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

Computed Tomography01:10

Computed Tomography

6.4K
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...
6.4K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

70
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...
70

You might also read

Related Articles

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

Sort by
Same author

Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis.

Journal of medical imaging (Bellingham, Wash.)·2025
Same author

Expected Trainee Interpretive Volumes and Potential Threats to Neuroradiology Program Viability: A Survey of Neuroradiology Fellowship Program Directors.

AJNR. American journal of neuroradiology·2025
Same author

Simultaneous reduction of radiation dose and scatter-to-primary ratio using a truncated detector and advanced algorithms for dedicated cone-beam breast CT.

Biomedical physics & engineering express·2025
Same author

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

An attenuation field network for dedicated cone beam breast CT with short scan and offset detector geometry.

Scientific reports·2024
Same author

Dedicated Cone-Beam Breast CT: Reproducibility of Volumetric Glandular Fraction with Advanced Image Reconstruction Methods.

Tomography (Ann Arbor, Mich.)·2023

Related Experiment Video

Updated: Sep 29, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.6K

Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm.

Hsin Wu Tseng1, Andrew Karellas1, Srinivasan Vedantham1,2

  • 1Department of Medical Imaging, The University of Arizona, Tucson, AZ, United States of America.

Physics in Medicine and Biology
|March 22, 2022
PubMed
Summary
This summary is machine-generated.

This study optimized cone-beam breast computed tomography (BCT) by evaluating detector offsets and reconstruction algorithms. The FRIST algorithm with a 5 cm detector offset yielded the best image quality for calcified lesions.

Keywords:
breast CTcone-beam CTimage qualityiterative reconstructionoffset detector

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.4K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K

Related Experiment Videos

Last Updated: Sep 29, 2025

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
08:30

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

14.6K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.4K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.0K

Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Imaging

Background:

  • Cone-beam breast computed tomography (BCT) systems are evolving with advanced detector technologies.
  • Offset-detector geometry presents unique challenges for image reconstruction.
  • Optimizing reconstruction algorithms is crucial for maximizing image quality in BCT.

Purpose of the Study:

  • To investigate the impact of varying detector offsets and reconstruction algorithms on BCT image quality.
  • To determine the optimal combination of detector offset and reconstruction algorithm for dedicated breast CT.
  • To evaluate the performance of the FRIST algorithm against the FDK algorithm in offset-detector BCT.

Main Methods:

  • Developed a prototype cone-beam BCT system with a high-resolution, low-noise detector in an offset-detector geometry.
  • Acquired projection datasets of 30 breasts with calcified lesions.
  • Reconstructed datasets using the Feldkamp-Davis-Kress (FDK) algorithm and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST) algorithm.
  • Emulated different lateral detector offsets (5 cm and 7.5 cm) by truncating projection data.
  • Quantitatively assessed image quality using signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications.

Main Results:

  • Spatial resolution, measured by FWHM, was comparable across all tested reconstruction algorithms and detector offsets.
  • The FRIST algorithm demonstrated significantly superior performance over FDK in terms of variance and SDNR (P < 0.0001) for a given detector offset.
  • A 5 cm lateral detector offset consistently yielded better results compared to a 7.5 cm offset, irrespective of the reconstruction method.
  • FRIST reconstructions with a 5 cm lateral offset achieved the highest image quality metrics.

Conclusions:

  • The compressed sensing-based FRIST algorithm is effective for reconstructing sinograms from offset-detector BCT systems.
  • A 30 cm x 30 cm detector with a 5 cm lateral offset, reconstructed using FRIST, offers optimal performance for dedicated breast CT.
  • The findings support the clinical implementation of offset-detector BCT prototypes for improved breast imaging.