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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Characteristics and outcomes of patients with pediatric-onset non-mastocytosis mast cell activation disorders: A CEREMAST study.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology·2026

Same author

The use of bone-modifying agents in early breast cancer: AIOM Guidelines update and perspectives.

Tumori·2025

Same author

Sarcopenic obesity and reduced BMD in young men living with HIV: body composition and sex steroids interplay.

Journal of endocrinological investigation·2024

Same author

Field output correction factors of small static field for IBA razor nanochamber.

Biomedical physics & engineering express·2023

Same author

Long-term trajectories of bone metabolism parameters and bone mineral density (BMD) in obese patients treated with metabolic surgery: a real-world, retrospective study.

Journal of endocrinological investigation·2023

Same author

Novel unconventional radiotherapy techniques: Current status and future perspectives - Report from the 2nd international radiation oncology online seminar.

Clinical and translational radiation oncology·2023

Search research articles

Related Experiment Video

Updated: Jun 18, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

An iterative technique to segment PET lesions using a Monte Carlo based mathematical model.

S A Nehmeh¹, H El-Zeftawy, C Greco

¹Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. nehmehs@mskcc.org

Medical Physics

|November 26, 2009

Summary

This summary is machine-generated.

An iterative algorithm accurately segments 18FDG PET lesions using a mathematical fit from Monte Carlo simulations. This method precisely estimates lesion volumes for therapy response, dosimetry, and radiotherapy planning.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Jun 18, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

Medical Imaging
Nuclear Medicine
Computational Modeling

Background:

Accurate lesion segmentation in 18FDG PET is crucial for therapy response assessment, dosimetry, and radiotherapy planning.
Current methods require precise tumor segmentation thresholds, which can be challenging to determine.

Purpose of the Study:

To develop an iterative method using a mathematical fit derived from Monte Carlo simulations to estimate optimal tumor segmentation thresholds for 18FDG PET.
To improve the accuracy of lesion volume quantification in 18FDG PET imaging.

Main Methods:

Utilized GATE software (GEANT4) to simulate a PET scanner and spheres of varying sizes in different backgrounds.
Developed a mathematical fit correlating lesion volume with optimal threshold values based on simulation analysis.
Implemented an iterative algorithm to determine the threshold, evaluating its performance with phantoms and clinical patient data.

Main Results:

The mathematical fit accurately predicted the relationship between PET lesion size and activity concentration threshold.
Phantom studies showed segmented PET target volumes within 10% of nominal values.
Clinical evaluation demonstrated PET target volumes within 10% of CT-derived volumes.

Conclusions:

The developed iterative algorithm accurately segments 18FDG PET lesions.
The method is independent of lesion contrast and provides precise volume estimations.