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Respiratory Prediction Based on Multi-Scale Temporal Convolutional Network for Tracking Thoracic Tumor Movement.

Lijuan Shi^1,2, Shuai Han^1,2, Jian Zhao^2,3

¹College of Electronic Information Engineering, Changchun University, Changchun, China.

Frontiers in Oncology

|June 13, 2022

Summary

This study introduces a novel respiratory motion prediction model using empirical mode decomposition (EMD) and deep learning for enhanced radiotherapy precision. The model significantly improves prediction accuracy and reduces treatment time for cancer patients.

Keywords:

deep learning network empirical mode decomposition radiotherapy respiratory motion prediction temporal convolutional network

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Published on: October 10, 2018

Area of Science:

Medical Physics
Artificial Intelligence in Medicine
Oncology

Background:

Radiotherapy precision is challenged by respiratory motion in thoracoabdominal tumors.
Real-time motion tracking is crucial for improving radiotherapy efficacy.
Accurate prediction of respiratory motion is essential for adaptive radiotherapy.

Purpose of the Study:

To develop a highly precise and efficient deep learning model for respiratory motion prediction.
To apply empirical mode decomposition (EMD) to enhance prediction accuracy.
To evaluate the model's performance across various input data lengths and delay times.

Main Methods:

Decomposition of respiratory signals into intrinsic mode functions (IMFs) using EMD.
Development of a deep prediction model combining multi-scale enhanced convolution neural networks and temporal convolutional networks.
Training and validation on a respiratory motion dataset from 103 cancer patients.

Main Results:

The proposed model demonstrated superior prediction performance compared to three other models.
The model achieved enhanced accuracy across different input data lengths and delay times.
Network update time was reduced by approximately 60%, improving time efficiency.

Conclusions:

The developed respiratory motion prediction model significantly enhances radiotherapy precision.
The model's efficiency and accuracy offer substantial clinical application value.
This approach promises to shorten radiotherapy duration and improve patient outcomes.