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

Modeling and Similitude01:12

Modeling and Similitude

728
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
728
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

536
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
536
Typical Model Studies01:30

Typical Model Studies

687
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
687
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

407
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
407
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
335
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K

You might also read

Related Articles

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

Sort by
Same author

A Conditional Generative Diffusion Model of Trabecular Bone with Tunable Microstructure.

Proceedings of SPIE--the International Society for Optical Engineering·2025
Same author

Robot-Assisted Reduction of the Ankle Joint via Multi-Body 3D-2D Image Registration.

IEEE transactions on medical robotics and bionics·2025
Same author

Effects of non-stationary blur on texture biomarkers of bone using Ultra-High Resolution CT.

Proceedings of SPIE--the International Society for Optical Engineering·2024
Same author

Performance assessment of surgical tracking systems based on statistical process control and longitudinal QA.

Computer assisted surgery (Abingdon, England)·2023
Same author

Multi-Stage Adaptive Spline Autofocus (MASA) with a Learned Metric for Deformable Motion Compensation in Interventional Cone-Beam CT.

Proceedings of SPIE--the International Society for Optical Engineering·2023
Same author

Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopaedic surgery.

Physics in medicine and biology·2023
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.6K

Model-based Reconstruction of Objects with Inexactly Known Components.

J W Stayman1, Y Otake1, S Schafer1

  • 1Dept. of Biomedical Eng., Johns Hopkins University, Baltimore, MD USA 21205.

Proceedings of Spie--The International Society for Optical Engineering
|July 24, 2015
PubMed
Summary
This summary is machine-generated.

Known-Component Reconstruction (KCR) improves medical imaging by incorporating known objects, even when their exact position or shape is unknown. This method enhances image quality, especially with noisy data or metal artifacts in CT scans.

Keywords:
CT reconstructionImplant imagingJoint registration-reconstructionPenalized-likelihood estimation

More Related Videos

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
Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

909

Related Experiment Videos

Last Updated: Apr 6, 2026

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach
08:01

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

9.6K
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
Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

909

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Tomographic reconstructions are often ill-conditioned, leading to poor image quality, especially with noisy or incomplete data.
  • Incorporating prior knowledge about the imaging volume can significantly improve reconstruction quality.
  • Metallic surgical implants in medical imaging cause artifacts due to photon starvation, complicating diagnosis.

Purpose of the Study:

  • To present a novel reconstruction framework, Known-Component Reconstruction (KCR), for improving image quality in tomographic imaging.
  • To develop a deformable KCR (dKCR) approach to handle inexactly known components with potential shape variations.
  • To evaluate the effectiveness of KCR and dKCR in low-dose cone-beam CT with metallic surgical hardware.

Main Methods:

  • Developed a general reconstruction framework (KCR) using a novel parameterization of known components.
  • Utilized a likelihood-based objective function and alternating optimization between registration and image parameters.
  • Introduced a deformable KCR (dKCR) using a control point-based warping operator for component shape variations.
  • Applied KCR and dKCR to low-dose cone-beam CT data with spine fixation hardware.

Main Results:

  • KCR and dKCR demonstrated substantially improved image quality compared to traditional filtered-backprojection and penalized-likelihood methods.
  • The proposed methods effectively handled photon starvation artifacts caused by metallic components.
  • KCR framework provided good visualization of anatomy adjacent to surgical devices, unlike traditional methods.

Conclusions:

  • Known-Component Reconstruction (KCR) offers a robust framework for improving image quality in challenging tomographic reconstructions.
  • The deformable KCR (dKCR) extends the applicability to components with unknown poses and deformations.
  • These methods are particularly beneficial for low-dose CT with metallic implants, enhancing diagnostic accuracy.