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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

A deep learning method for eliminating head motion artifacts in computed tomography.

Bin Su¹, Yuting Wen², Yanyan Liu¹

¹Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China.

Medical Physics

|November 17, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning algorithm to remove motion artifacts in computed tomography (CT) scans. The novel method effectively restores image quality, improving diagnostic confidence and potentially avoiding repeat scans.

Keywords:

CT image reconstruction machine learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Patient motion during computed tomography (CT) scans causes data discontinuities, severely degrading image quality.
Motion artifacts, particularly in head scans, pose a significant challenge to accurate diagnosis.

Purpose of the Study:

To develop and evaluate a deep learning-based algorithm for eliminating motion artifacts in CT images.
Specifically address artifacts induced by involuntary patient movement during head scans.

Main Methods:

A novel 3D convolutional neural network (CNN) was designed to map artifact-contaminated images to artifact-free images.
A motion simulation algorithm synthesized images with various motion-induced artifacts (rotation, translation, oscillation) for training.
The proposed network was quantitatively evaluated against U-Net and compared using clinical data from two hospitals.

Main Results:

The deep learning algorithm demonstrated effective artifact removal, outperforming existing methods on a validation dataset with simulated random motion.
Quantitative metrics showed the proposed network achieved the lowest normalized root-mean-square error and highest peak signal-to-noise ratio and structure similarity.
Clinical evaluation confirmed the method's effectiveness in processing real-world motion-contaminated CT images.

Conclusions:

A novel deep learning algorithm effectively reduces motion artifacts in CT scans using synthesized image pairs.
The corrected images enhance diagnostic confidence, offering a potential solution to avoid repeat CT scans in certain clinical situations.