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

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A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation.

Byeongjoon Kim¹, Hyunjung Shim¹, Jongduk Baek¹

¹School of Integrated Technology, Yonsei University, Incheon, South Korea.

Medical Physics

|July 26, 2022

Summary

This summary is machine-generated.

This study introduces a new method to reduce streak artifacts in sparse-view computed tomography (CT) scans. The technique effectively removes artifacts while preserving fine anatomical details, offering a patient-specific solution without needing extra training data.

Keywords:

computed tomography (CT)inverse problem neural representation sparse-view CT streak artifact

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Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Sparse-view computed tomography (CT) offers reduced radiation dose and scan time.
Analytical CT reconstruction methods struggle with streak artifacts from limited projection views.
Existing deep learning methods often overfit, limiting generalizability to new systems and patients.

Purpose of the Study:

To develop a novel algorithm for streak artifact reduction in sparse-view CT.
To provide a system- and patient-specific solution for artifact correction.
To improve image quality in low-dose CT imaging.

Main Methods:

Regenerating streak artifacts from a prior CT image using a coordinate-based neural representation.
Optimizing the neural representation to sparse-view projection data via self-supervised learning, inspired by neural radiance fields.
Subtracting regenerated artifacts from the analytically reconstructed image for correction.

Main Results:

The proposed method effectively reduced streak artifacts in simulated and experimental data.
Small anatomical features were best preserved compared to existing methods.
Achieved superior scores in visual information fidelity, modulation transfer function, and lung nodule segmentation.

Conclusions:

The novel method significantly reduces streak artifacts while preserving crucial anatomical details.
It offers a practical solution for clinical settings where large datasets are unavailable.
The system- and patient-specific approach enhances the reliability of sparse-view CT imaging.