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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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Contextual loss based artifact removal method on CBCT image.

Shipeng Xie¹, Yingjuan Liang¹, Tao Yang¹

¹College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China.

Journal of Applied Clinical Medical Physics

|November 2, 2020

Summary

This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) algorithm to remove scatter artifacts in cone beam computed tomography (CBCT) images, significantly improving image quality for medical applications.

Keywords:

CBCT contextual loss scatter correction

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Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Cone beam computed tomography (CBCT) is valuable in medical imaging but suffers from scatter artifacts.
These artifacts degrade image quality and hinder clinical applications.
Developing effective artifact reduction techniques is crucial for CBCT's utility.

Purpose of the Study:

To propose and evaluate a novel scatter artifact removal algorithm for CBCT images.
To utilize a convolutional neural network (CNN) with contextual loss for artifact correction.
To improve the diagnostic quality of CBCT images, particularly in the pelvic region.

Main Methods:

A CNN-based algorithm incorporating contextual loss was developed.
The network was trained on 627 CBCT-CT image pairs from 11 patients.
Performance was evaluated using metrics like Mean Absolute Error (MAE) and Peak Signal-to-Noise Ratio (PSNR).

Main Results:

The proposed method effectively removed streaking, shadowing, and cupping artifacts from CBCT images.
Internal contours and texture details of the pelvic region were well preserved.
Quantitative analysis showed improved image quality, evidenced by better CT numbers, MAE, and PSNR.

Conclusions:

The CNN algorithm successfully corrects CBCT scatter artifacts, enhancing image quality.
Significant improvements were observed in the average CT number for bones and skin.
The method shows potential for improving dose estimation and segmentation in medical imaging.