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

Instrument Calibration01:12

Instrument Calibration

916
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
916
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.7K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.7K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.1K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.1K
Glassware Calibration01:11

Glassware Calibration

1.6K
Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
1.6K
Distance Corrections01:15

Distance Corrections

332
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
332

You might also read

Related Articles

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

Sort by
Same author

Clinical and subclinical mastitis incidence in pasture-based dairy cows.

New Zealand veterinary journal·2025
Same author

Novel Measurement of the Neutron Magnetic Form Factor from A=3 Mirror Nuclei.

Physical review letters·2024
Same author

Experiences and intentions of patients undergoing medically indicated oocyte or embryo cryopreservation: a qualitative study.

Human reproduction (Oxford, England)·2023
Same author

Wapello County Medical Society.

Iowa medical journal·2023
Same author

Two Cases of Puerperal Convulsions.

Iowa medical journal·2023
Same author

Revealing the short-range structure of the mirror nuclei <sup>3</sup>H and <sup>3</sup>He.

Nature·2022
Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026
Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026
Same journal

A novel optical respiratory gating system with a hybrid phase-amplitude algorithm for spot-scanning proton therapy.

Medical physics·2026
Same journal

Gamma Knife treatment planning using knowledge-based reinforcement learning.

Medical physics·2026
Same journal

Development and characterization of a novel, small animal external beam irradiator using a clinical high dose rate brachytherapy source.

Medical physics·2026
Same journal

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

Medical physics·2026
See all related articles

Related Experiment Video

Updated: Mar 2, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

740

WE-G-217BCD-09: Calibration of a DRR Algorithm.

D Staub1, A Sampson1, J Williamson1

  • 1Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA.

Medical Physics
|May 19, 2017
PubMed
Summary
This summary is machine-generated.

This study calibrated a digitally reconstructed radiograph (DRR) algorithm to accurately match fan-beam CT (FBCT) and cone-beam CT (CBCT) projections. The optimized DRR algorithm improves image matching for enhanced medical imaging applications.

Keywords:
CalibrationCone beam computed tomographyDigital radiographyParticle beam detectorsPhotonsRadiography

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Related Experiment Videos

Last Updated: Mar 2, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

740
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Medical Physics
  • Radiological Imaging
  • Computational Imaging

Background:

  • Accurate image reconstruction is crucial for diagnostic and therapeutic applications in medical imaging.
  • Fan-beam CT (FBCT) and cone-beam CT (CBCT) are widely used imaging modalities, but direct comparison of projections can be challenging.
  • Digitally reconstructed radiographs (DRRs) offer a method to simulate projections from existing CT data.

Purpose of the Study:

  • To calibrate a digitally reconstructed radiograph (DRR) algorithm for fan-beam CT (FBCT) data.
  • To ensure that synthetic projections from the DRR algorithm precisely match cone-beam CT (CBCT) projections of the same object.
  • To optimize the DRR algorithm for specific FBCT and CBCT systems.

Main Methods:

  • Developed a ray-tracing algorithm to model primary photon transmission through CT data.
  • Calibrated a CT number to linear attenuation coefficient (LAC) conversion function using known materials.
  • Post-processed DRRs to account for system-specific factors including tube output, fluence, detector response, scatter, beam hardening, and veiling glare.
  • Determined optimal CBCT geometry parameters using a specialized phantom.

Main Results:

  • The calibrated DRR algorithm closely reproduced actual CBCT projections when corrections for scatter, beam hardening, and veiling glare were applied.
  • Intensity profiles of DRRs and CBCT projections showed significantly improved agreement after algorithm optimization.
  • Simulations recommended a cylindrical phantom (150 mm diameter) for geometry determination.
  • Achieved computational times under one second per DRR using GPU acceleration.

Conclusions:

  • The developed DRR algorithm effectively reproduces CBCT projections for test objects.
  • The algorithm is optimized for specific FBCT and CBCT imaging systems.
  • This calibration enhances the utility of DRRs in comparing and integrating data from different CT modalities.