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Synthetic CT generation based on CBCT using respath-cycleGAN.

Liwei Deng¹, Jie Hu², Jing Wang³

¹Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang, China.

Medical Physics

|April 29, 2022

Summary

This summary is machine-generated.

A new respath-cycleGAN method synthesizes high-quality CT (sCT) from cone-beam CT (CBCT) images. This technique improves radiotherapy planning by reducing artifacts in CBCT scans.

Keywords:

CBCT cycleGAN pCT sCT scatter artifacts

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Radiotherapy Technology

Background:

Cone-beam computed tomography (CBCT) is crucial for radiotherapy but suffers from artifacts that limit its clinical utility.
Artifacts in CBCT can compromise the accuracy of radiotherapy planning and treatment delivery.

Purpose of the Study:

To develop a novel deep learning method, respath-cycleGAN, for synthesizing artifact-free CT images (sCT) from CBCT.
To improve the quality of CBCT images for enhanced radiotherapy planning.

Main Methods:

The respath concept was integrated into the cycleGAN architecture, creating respath-cycleGAN for mapping CBCT to planning CT (pCT).
The model was trained on 30 patient datasets and validated on 15 patient datasets.
Image quality was assessed using quantitative metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Spatial Nonuniformity (SNU).

Main Results:

The respath-cycleGAN method significantly reduced artifacts, improving MAE from 197.72 to 140.7 and RMSE from 339.17 to 266.51.
Quantitative metrics showed improvements: PSNR increased from 22.07 to 24.44, and SSIM increased from 0.948 to 0.964.
Visual and quantitative assessments confirmed the superiority of sCT generated with respath-cycleGAN compared to CBCT and sCT without respath, demonstrating robust performance on diverse datasets.

Conclusions:

The developed respath-cycleGAN method effectively synthesizes high-quality CT images from CBCT.
This AI-driven approach holds significant potential for improving radiotherapy planning in clinical practice by providing artifact-reduced CBCT-based CT images.