문의하기 아이콘
문의하기 텍스트
top 아이콘
coreline logo
close icon
Cookie Settings Information

When you visit our website, we store cookies on your browser to collect information. The information collected may relate to you, your device, or your preferences, and is primarily used to ensure the website functions properly and to provide a more personalized web experience.

However, you may choose to disallow certain types of cookies, which could affect your user experience and the services we are able to offer. You can click on each category below to learn more and adjust your default settings.

Please note that Strictly Necessary Cookies are essential for the basic functioning of the website and cannot be disabled (e.g., maintaining login sessions, remembering settings). For more detailed information about cookies, please refer to our [Privacy Policy].

Manage Consent Preferences
+Strictly Necessary CookiesAlways Active
These cookies are essential for the website to function properly and cannot be switched off in our systems. They are usually set only in response to actions you take, such as setting your privacy preferences, logging in, or filling out forms. You can set your browser to block or alert you about these cookies, but some parts of the site may not function properly as a result. These cookies do not store any personally identifiable information.
+Targeting Cookies
These cookies may be set through our site by our advertising partners. They are used to build a profile of your interests and show you relevant advertisements on other sites. These cookies do not directly store personal information but operate based on unique identification of your browser and device. If you do not allow these cookies, you will experience less targeted advertising.
+Performance
These cookies allow us to aggregate the number of visitors and traffic sources in order to measure and improve the performance of our website. They help us understand which pages are the most popular and how visitors navigate through the site. All information collected is aggregated and therefore anonymous. If you do not allow these cookies, we will not be able to monitor the performance of our site or know when you have visited it.
Save My Choices

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

Authors
Sang Min Lee, MD, June-Goo Lee, PhD, Gaeun Lee, BS, Jooae Choe, MD, Kyung-Hyun Do, MD, Namkug Kim, PhD, Joon Beom Seo, MD
Journal
Thoracic Imaging
Related Product

COPD

Date Published
2018.12
Summary

This study aimed to develop and validate a convolutional neural network (CNN) to convert CT images reconstructed with one kernel to those with different reconstruction kernels without using a sinogram. The study included ten chest CT scans reconstructed with B10f, B30f, B50f, and B70f kernels, divided into training, validation, and testing datasets. A CNN with six convolutional layers was constructed. Performance was evaluated using root mean square error (RMSE) values. For clinical validation, 30 additional chest CT scans reconstructed with B30f and B50f kernels were converted and analyzed using Aview software for emphysema quantification. The scheme achieved a rapid conversion rate of 0.065 s/slice and significantly reduced RMSE (mean reduction of 65.7%). Emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4%, 15.3%, 5.9%, and 16.8%, respectively. The CNN-based conversion demonstrated high accuracy and speed, highlighting its potential for clinical use.

Contact

Please leave your inquiry if you have any questions regarding our products, recruitment, investment, or any other matters.

Contact us