TY - GEN
T1 - CT Image Harmonization for Enhancing Radiomics Studies
AU - Selim, Md
AU - Zhang, Jie
AU - Fei, Baowei
AU - Zhang, Guo Qiang
AU - Chen, Jin
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported by NIH NCI (grant no. 1R21CA231911) and Kentucky Lung Cancer Research (grant no. KLCR-3048113817).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to normalize CT images acquired using non-standard protocols is vital for decision-making in cross-center large-scale radiomics studies but remains the boundary to explore. We develop a novel GAN-based image standardization algorithm called RadiomicGAN to mitigate the discrepancy caused by using non-standard acquisition protocols. In RadiomicGAN, a pre-trained U-Net has been adopted as part of the generator to learn radiomic feature distributions efficiently, and a novel training approach, called Window Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. In the experiments, we compared RadiomicGAN with four state-of-the-art CT image standardization approaches on both patient and phantom CT images acquired using three different reconstruction kernels. We objectively evaluated model performance based on more than 1,000 radiomic features. The results show that RadiomicGAN clearly outperforms the compared models. The source code, manual, and sample data are available at https://github.con selim-iitdu/radiomicGAN.
AB - While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to normalize CT images acquired using non-standard protocols is vital for decision-making in cross-center large-scale radiomics studies but remains the boundary to explore. We develop a novel GAN-based image standardization algorithm called RadiomicGAN to mitigate the discrepancy caused by using non-standard acquisition protocols. In RadiomicGAN, a pre-trained U-Net has been adopted as part of the generator to learn radiomic feature distributions efficiently, and a novel training approach, called Window Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. In the experiments, we compared RadiomicGAN with four state-of-the-art CT image standardization approaches on both patient and phantom CT images acquired using three different reconstruction kernels. We objectively evaluated model performance based on more than 1,000 radiomic features. The results show that RadiomicGAN clearly outperforms the compared models. The source code, manual, and sample data are available at https://github.con selim-iitdu/radiomicGAN.
KW - Computed Tomography
KW - Generative Adversarial Network
KW - Image Synthesis
KW - Radiomics
KW - Standardization
UR - http://www.scopus.com/inward/record.url?scp=85125194113&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125194113&partnerID=8YFLogxK
U2 - 10.1109/BIBM52615.2021.9669448
DO - 10.1109/BIBM52615.2021.9669448
M3 - Conference contribution
AN - SCOPUS:85125194113
T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
SP - 1057
EP - 1062
BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
A2 - Huang, Yufei
A2 - Kurgan, Lukasz
A2 - Luo, Feng
A2 - Hu, Xiaohua Tony
A2 - Chen, Yidong
A2 - Dougherty, Edward
A2 - Kloczkowski, Andrzej
A2 - Li, Yaohang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Y2 - 9 December 2021 through 12 December 2021
ER -