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

9.6K
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...
9.6K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

728
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...
728
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

1.2K
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Single-pass metal artifact reduction using a dual-layer flat panel detector.

Medical physics·2021
Same author

High-resolution model-based material decomposition in dual-layer flat-panel CBCT.

Medical physics·2021
Same author

Projection-domain metal artifact correction using a dual layer detector.

Proceedings of SPIE--the International Society for Optical Engineering·2021
Same author

Comparative Study of Dual Energy Cone-Beam CT using a Dual-Layer Detector and kVp Switching for Material Decomposition.

Proceedings of SPIE--the International Society for Optical Engineering·2021
Same author

Notch signaling: Its essential roles in bone and craniofacial development.

Genes & diseases·2021
Same author

Carrier Effects on the Chemical and Physical Properties of Freeze-Dried Encapsulated Mulberry Leaf Extract Powder.

Acta chimica Slovenica·2021
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
09:56

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

7.5K

Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.

Meng Wu, Sungwon Yoon, Edward G Solomon

    IEEE Transactions on Medical Imaging
    |July 25, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Digital tomosynthesis, a lower-dose 3D imaging method, faces challenges with out-of-plane artifacts. A new convolution-based blur-and-add (BAA) model accurately simulates reconstructions, aiding artifact reduction and system analysis.

    More Related Videos

    Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
    07:16

    Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

    Published on: April 25, 2025

    912
    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
    09:10

    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

    Published on: August 5, 2021

    2.4K

    Related Experiment Videos

    Last Updated: Apr 6, 2026

    Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
    09:56

    Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

    Published on: August 21, 2019

    7.5K
    Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach
    07:16

    Three-Dimensional Imaging of Tumor-Bearing Tissue Using the Iterative Bleaching Extends Multiplexity Approach

    Published on: April 25, 2025

    912
    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
    09:10

    Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

    Published on: August 5, 2021

    2.4K

    Area of Science:

    • Medical Imaging
    • Radiological Physics

    Background:

    • Digital tomosynthesis offers a lower radiation dose alternative to computed tomography (CT) for 3D imaging.
    • Reconstruction algorithms in tomosynthesis struggle to fully remove out-of-plane structures due to missing data.

    Purpose of the Study:

    • To analyze the impulse responses of tomosynthesis systems.
    • To develop a fast and accurate convolution-based blur-and-add (BAA) model for simulating tomosynthesis reconstructions.
    • To generalize the analysis formalism for various gantry types.

    Main Methods:

    • Analysis of tomosynthesis system impulse responses on a plane-to-plane basis.
    • Implementation of a ray tracing forward/backprojection (ray-based) model.
    • Development and implementation of a convolution-based blur-and-add (BAA) model for simulating shift-and-add reconstructions.

    Main Results:

    • The proposed convolution-based BAA model provides reasonably accurate estimates of tomosynthesis reconstructions, especially with geometry distortion correction.
    • Numerical comparison showed less than 6% root-mean-squared error difference between the BAA model and the ray-based model.
    • The impulse response analysis formalism is applicable to both rotating and parallel gantries.

    Conclusions:

    • The convolution-based BAA model is an efficient tool for simulating tomosynthesis.
    • This model can enhance system geometry analysis, reconstruction algorithm design, and out-of-plane artifact suppression.
    • The BAA model facilitates CT-tomosynthesis registration.