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Related Experiment Video

Updated: Mar 8, 2026

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Iterative volume morphing and learning for mobile tumor based on 4DCT.

Songan Mao¹, Huanmei Wu², George Sandison³

¹School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.

Physics in Medicine and Biology

|January 26, 2017

Summary

This summary is machine-generated.

This study introduces an iterative morphing algorithm to predict real-time 3D tumor volume during radiation therapy using pre-treatment 4DCT scans. This method enhances cancer treatment planning and validation by accurately estimating tumor changes without extra radiation exposure.

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Last Updated: Mar 8, 2026

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Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

Area of Science:

Medical Imaging
Radiation Oncology
Computational Biology

Background:

Accurate 3D tumor volume is crucial for image-guided cancer radiation treatment.
Real-time 3D tumor imaging during treatment is limited by radiation dose concerns.

Purpose of the Study:

To develop an iterative morphing algorithm for predicting real-time 3D tumor volume using pre-treatment 4DCT data.
To enable accurate tumor volume estimation during radiation delivery without continuous imaging.

Main Methods:

An offline iterative learning process was used to derive volumetric deformation functions from 4DCT data.
A novel landmark selection method using a minimum bounding box on 3D tumor surfaces was developed.
An iterative morphing algorithm and online prediction strategy were implemented for real-time volume estimation.

Main Results:

The algorithm demonstrated promising performance with maximum morphing deviations of 0.27% for patient data and 1.25% for generated data.
The developed approach accurately predicts 3D tumor volume changes between breathing phases.
The method allows for online tumor volume prediction during radiation treatment delivery.

Conclusions:

The iterative deformable algorithm for tumor volume morphing and prediction based on 4DCT is innovative and effective.
This algorithm has significant potential applications in cancer radiation treatment planning, dose calculation, and validation.
The approach addresses the challenge of real-time 3D tumor volume monitoring in radiation therapy.