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

Harmonic Mean

Harmonic Mean

The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...

You might also read

Related Articles

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

Sort by

Same author

Brain-gut axis imaging, motion correction with [ <sup>11</sup> C]-carfentanil total-body PET.

medRxiv : the preprint server for health sciences·2026

Same author

A multi-modal flow phantom for quantitative PET/Spectral CT.

bioRxiv : the preprint server for biology·2026

Same author

Realization of Long Axial Field-of-View PET Technology in the Clinic and Research Environment.

The British journal of radiology·2026

Same author

A Virtual Clinical Trial to Detect Changes in Tumor Uptake with PET using Lesion Embedding.

IEEE transactions on radiation and plasma medical sciences·2026

Same author

Impact of detector parameters and image resolution modeling on dedicated brain PET imaging.

IEEE transactions on radiation and plasma medical sciences·2026

Same author

Room to Move: Enhancing Head and Neck Radiotherapy With a Six-Degree-of-Freedom Couch.

Cureus·2026

Same journal

Correction to "On the shape of the radiation survival curve in tumor spheroids: The role of oxygen heterogeneity".

Medical physics·2026

Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026

Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026

Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026

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

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 3, 2026

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

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

Published on: October 27, 2023

Validation of phantom-based harmonization for patient harmonization.

Joseph V Panetta¹, Margaret E Daube-Witherspoon¹, Joel S Karp¹

¹Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Medical Physics

|May 3, 2017

Summary

This summary is machine-generated.

Phantom measurements effectively harmonize patient images in multicenter PET trials. Harmonization strategies defined using phantoms accurately predict patient image quantification, improving consistency across scanners.

Keywords:

PET/CT imaging harmonization quantification

More Related Videos

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Related Experiment Videos

Last Updated: Mar 3, 2026

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

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

Published on: October 27, 2023

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Area of Science:

Medical Imaging
Nuclear Medicine
Quantitative Imaging

Background:

Multicenter clinical trials require precise and consistent imaging data.
Scanner variability can introduce significant noise in quantitative PET imaging.
Standardized phantom measurements are being explored for harmonizing PET scanner parameters.

Purpose of the Study:

To assess the correlation between phantom and patient image quantification.
To validate the use of phantoms for harmonizing patient PET images across different scanners.
To evaluate the effectiveness of postfiltering strategies for image harmonization.

Main Methods:

Used a NEMA phantom with embedded lesions and whole-body FDG-PET scans from two scanners.
Embedded list-mode data to create lesions with known uptake in phantom and patient liver/lung regions.
Analyzed images using contrast recovery coefficient (CRC) and applied postreconstruction filtering for harmonization.

Main Results:

CRC values for phantom and patient-embedded lesions agreed within 5%.
Postfiltering strategies significantly reduced the root mean squared percent difference (RMS_pd) in CRC values between scanners.
RMS_pd for CRC_mean reduced from 36% to <8%, and for CRC_max from ~33% to <6% after harmonization.

Conclusions:

Phantom-defined harmonization strategies effectively translate to patient images.
Quantitative agreement between scanners depends on the chosen metric (CRC_mean vs. CRC_max) for harmonization.
Phantoms are valuable tools for validating and implementing PET image harmonization protocols.