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Related Experiment Video

Updated: Jun 30, 2025

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

Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

Yabo Fu¹, Pengpeng Zhang¹, Qiyong Fan¹

¹Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Medical Physics

|March 20, 2024

Summary

This study introduces a novel deep learning method to enhance lung tumor visibility for accurate, real-time markerless tracking during radiotherapy. The technique achieved high precision, improving treatment safety and effectiveness.

Keywords:

markerless tracking x‐ray

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Area of Science:

Medical Imaging
Radiotherapy
Artificial Intelligence

Background:

Real-time tumor tracking in radiotherapy is crucial for accurate targeting and reducing geometric misses.
Markerless kV x-ray image-based tracking is difficult due to low tumor visibility caused by surrounding structures.
Enhanced tumor visibility is essential for effective real-time tumor tracking.

Purpose of the Study:

To present a novel method for markerless kV image-based lung tumor tracking.
The method utilizes deep learning-based target decomposition to improve tumor visibility.

Main Methods:

A conditional Generative Adversarial Network (cGAN), Pix2Pix, was used to create patient-specific models.
Synthetic decomposed target images (sDTI) were generated to enhance tumor visibility on real-time kV images.
Real-time 2D tumor tracking was performed using template matching with generated sDTI.

Main Results:

The sDTI method effectively improved image contrast for lung tumors in kV projection images.
Average tracking errors were 0.8 ± 0.7 mm (SI) and 0.9 ± 0.8 mm (IPLR).
A 92.2% success rate was achieved with tracking errors less than 2 mm in the SI direction.

Conclusions:

The proposed method provides a potential solution for near real-time, markerless lung tumor tracking.
The technique demonstrated high accuracy and an impressive tracking rate.
Further research into 3D lung tumor tracking is recommended.