Abstract
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.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention : MICCAI 2022 : 25th International Conference, Proceedings |
Editors | Linwei WANG, Qi DOU, P. Thomas FLETCHER, Stefanie SPEIDEL, Shuo LI |
Publisher | Springer, Cham |
Pages | 765-773 |
Number of pages | 9 |
ISBN (Electronic) | 9783031164491 |
ISBN (Print) | 9783031164484 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sept 2022 → 22 Sept 2022 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 13437 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 |
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Country/Territory | Singapore |
City | Singapore |
Period | 18/09/22 → 22/09/22 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Funding
This work was partly supported by funding GRF 151022/19M and ITS/080/19.
Keywords
- Contrast-enhanced MRI
- Model generalizability
- Nasopharyngeal carcinoma