Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Spherical Coordinates

Spherical Coordinates

Spherical coordinate systems are preferred over Cartesian, polar, or cylindrical coordinates for systems with spherical symmetry. For example, to describe the surface of a sphere, Cartesian coordinates require all three coordinates. On the other hand, the spherical coordinate system requires only one parameter: the sphere's radius. As a result, the complicated mathematical calculations become simple. Spherical coordinates are used in science and engineering applications like electric and...

Coordinates and Map Projections

Coordinates and Map Projections

Coordinates and map projections are essential tools in accurately representing the Earth's surface for various applications, ranging from navigation to spatial analysis. The latitude and longitude coordinate system is a universally recognized framework for defining locations. Latitude specifies the distance of a point north or south of the equator, measured in degrees from 0° at the equator to 90° at the poles. Longitude indicates a location's position east or west of the prime meridian,...

You might also read

Related Articles

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

Sort by

Same author

Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT.

Medical physics·2026

Same author

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration.

European radiology·2026

Same author

Erratum to: Detection of myeloma-associated osteolytic bone lesions with energy-integrating and photon-counting detector CT.

Radiologie (Heidelberg, Germany)·2026

Same author

Feasibility of opportunistic dental diagnostics in routine photon-counting CT examinations of the cervical spine.

BMC oral health·2026

Same author

Multispectral PCCT and CBCT imaging for high precision radiotherapy through translation of imaging parameters with machine learning validation.

Scientific reports·2026

Same author

Development and characterization of new contrast agents for Photon-Counting CT.

European journal of radiology·2026

Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026

Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026

Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026

Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026

Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026

Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

Model-based sphere localization (MBSL) in x-ray projections.

Stefan Sawall¹, Joscha Maier¹, Carsten Leinweber¹

¹German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany.

Physics in Medicine and Biology

|June 21, 2017

Summary

This summary is machine-generated.

Accurately detecting spherical marker centers in X-ray projections is crucial. A new simulation-based method precisely estimates sphere center projections, improving accuracy for applications like CT calibration.

More Related Videos

Preparation and 3D Tracking of Catalytic Swimming Devices

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

Related Experiment Videos

Last Updated: Feb 28, 2026

Measuring the Complete-arch Distortion of an Optical Dental Impression

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

Preparation and 3D Tracking of Catalytic Swimming Devices

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

Area of Science:

Medical Imaging
Computational Geometry
Image Processing

Background:

Accurate detection of spherical markers in X-ray projections is vital for geometric calibration and detector correction.
Current methods for estimating the sphere center projection often use surrogate positions, limiting downstream algorithm performance.

Purpose of the Study:

To develop a novel simulation-based algorithm for precise estimation of spherical marker center projections in X-ray images.
To address limitations of existing methods by directly estimating the sphere center projection without prior knowledge of acquisition geometry or object properties.

Main Methods:

A simulation-based approach matching artificial projections to actual measurements.
Simultaneous estimation of a polynomial to correct for polychromatic effects.
Robustness testing against variations in sphere material, size, and acquisition parameters.

Main Results:

The proposed algorithm achieves sub-pixel accuracy in estimating the center projection, even with realistic noise levels.
Demonstrated robustness across various sphere materials, sizes, and acquisition parameters.
Outperformed three reference methods by an order of magnitude in both simulations and real measurements.

Conclusions:

The developed method provides a highly accurate algorithm for estimating the center of spherical markers in CT projections.
The algorithm effectively corrects for polychromatic effects and noise, enhancing reliability in medical imaging applications.