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

제품, 인재 채용, 투자 관련 또는 기타 문의사항이 있으신 경우 편하신 방법으로 연락주시기 바랍니다

문의하기