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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement.

Shaohua Zhi, Marc KachelrieB, Fei Pan

IEEE Transactions on Medical Imaging

|May 19, 2021

Summary

New deep learning models, N-Net and CycN-Net, significantly enhance four-dimensional cone-beam computed tomography (4D CBCT) image quality. These networks reduce artifacts and noise, improving image clarity for radiation therapy.

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Area of Science:

Medical Imaging
Radiotherapy Technology
Artificial Intelligence in Medicine

Background:

Four-dimensional cone-beam computed tomography (4D CBCT) is crucial for image-guided radiation therapy.
4D CBCT images suffer from artifacts and noise due to sparse-view reconstruction.

Purpose of the Study:

To develop advanced deep learning models for improving 4D CBCT image quality.
To address streaking artifacts and noise in phase-resolved 4D CBCT reconstructions.

Main Methods:

Proposed two Convolutional Neural Network (CNN) models: N-Net and CycN-Net.
N-Net utilizes prior image information from U-Net for quality enhancement.
CycN-Net incorporates temporal correlations between phase-resolved images.

Main Results:

Both N-Net and CycN-Net effectively suppressed artifacts and noise.
Distinct image features were restored simultaneously.
The proposed methods outperformed existing CNN models and iterative algorithms.

Conclusions:

N-Net and CycN-Net demonstrate significant improvements in 4D CBCT image quality.
The models exhibit robust generalization across diverse patient data and imaging devices.
These CNNs offer a promising solution for enhanced image-guided radiation therapy.