TY - GEN
T1 - Multi-institutional Investigation of Model Generalizability for Virtual Contrast-Enhanced MRI Synthesis
AU - Li, Wen
AU - Lam, Saikit
AU - Li, Tian
AU - Cheung, Andy Lai Yin
AU - Xiao, Haonan
AU - Liu, Chenyang
AU - Zhang, Jiang
AU - Teng, Xinzhi
AU - Zhi, Shaohua
AU - Ren, Ge
AU - Lee, Francis Kar ho
AU - Au, Kwok hung
AU - Lee, Victor Ho fun
AU - Chang, Amy Tien Yee
AU - Cai, Jing
N1 - Funding Information:
Acknowledgment. This work was partly supported by funding GRF 151022/19M and ITS/080/19.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/9/17
Y1 - 2022/9/17
N2 - The purpose of this study is to investigate the model generalizability using multi-institutional data for virtual contrast-enhanced MRI (VCE-MRI) synthesis. This study presented a retrospective analysis of contrast-free T1-weighted (T1w), T2-weighted (T2w), and gadolinium-based contrast-enhanced T1w MRI (CE-MRI) images of 231 NPC patients enrolled from four institutions. Data from three of the participating institutions were employed to generate a training and an internal testing set, while data from the remaining institution was employed as an independent external testing set. The multi-institutional data were trained separately (single-institutional model) and jointly (joint-institutional model) and tested using the internal and external sets. The synthetic VCE-MRI was quantitatively evaluated using MAE and SSIM. In addition, visual qualitative evaluation was performed to assess the quality of synthetic VCE-MRI compared to the ground-truth CE-MRI. Quantitative analyses showed that the joint-institutional models outperformed single-institutional models in both internal and external testing sets, and demonstrated high model generalizability, yielding top-ranked MAE, and SSIM of 71.69 ± 21.09 and 0.81 ± 0.04 respectively on the external testing set. Qualitative evaluation indicated that the joint-institutional model gave a closer visual approximation between the synthetic VCE-MRI and ground-truth CE-MRI on the external testing set, compared with single-institutional models. The model generalizability for VCE-MRI synthesis was enhanced, both quantitatively and qualitatively, when data from more institutions was involved during model development.
AB - The purpose of this study is to investigate the model generalizability using multi-institutional data for virtual contrast-enhanced MRI (VCE-MRI) synthesis. This study presented a retrospective analysis of contrast-free T1-weighted (T1w), T2-weighted (T2w), and gadolinium-based contrast-enhanced T1w MRI (CE-MRI) images of 231 NPC patients enrolled from four institutions. Data from three of the participating institutions were employed to generate a training and an internal testing set, while data from the remaining institution was employed as an independent external testing set. The multi-institutional data were trained separately (single-institutional model) and jointly (joint-institutional model) and tested using the internal and external sets. The synthetic VCE-MRI was quantitatively evaluated using MAE and SSIM. In addition, visual qualitative evaluation was performed to assess the quality of synthetic VCE-MRI compared to the ground-truth CE-MRI. Quantitative analyses showed that the joint-institutional models outperformed single-institutional models in both internal and external testing sets, and demonstrated high model generalizability, yielding top-ranked MAE, and SSIM of 71.69 ± 21.09 and 0.81 ± 0.04 respectively on the external testing set. Qualitative evaluation indicated that the joint-institutional model gave a closer visual approximation between the synthetic VCE-MRI and ground-truth CE-MRI on the external testing set, compared with single-institutional models. The model generalizability for VCE-MRI synthesis was enhanced, both quantitatively and qualitatively, when data from more institutions was involved during model development.
KW - Contrast-enhanced MRI
KW - Model generalizability
KW - Nasopharyngeal carcinoma
UR - http://www.scopus.com/inward/record.url?scp=85139011040&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16449-1_73
DO - 10.1007/978-3-031-16449-1_73
M3 - Conference article published in proceeding or book
AN - SCOPUS:85139011040
SN - 9783031164484
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 765
EP - 773
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -