TY - GEN
T1 - Trustworthy Longitudinal Brain MRI Completion: A Deformation-Based Approach with KAN-Enhanced Diffusion Model
AU - Tao, Tianli
AU - Wang, Ziyang
AU - Yang, Delong
AU - Zhang, Han
AU - Zhang, Le
N1 - This is an accepted manuscript of a paper published in: T. Tao, Z. Wang, D. Yang, H. Zhang and L. Zhang, "Trustworthy Longitudinal Brain MRI Completion: A Deformation-Based Approach with KAN-Enhanced Diffusion Model," 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), London, United Kingdom, 2026, pp. 1-4, doi: 10.1109/ISBI61048.2026.11515615 . For the purposes of open access the author/s has/ve applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript (AAM) version arising from this submission.
PY - 2026/5/20
Y1 - 2026/5/20
N2 - Longitudinal brain MRI is essential for lifespan study, yet high attrition rates often lead to missing data, complicating analysis. Deep generative models have been explored, but most rely solely on image intensity, leading to two key limitations: 1) the fidelity or trustworthiness of the generated brain images are limited, making downstream studies questionable; 2) the usage flexibility is restricted due to fixed guidance rooted in the model structure, restricting full ability to versatile application scenarios. To address these challenges, we introduce DF-DiffCom, a Kolmogorov-Arnold Networks (KAN)-enhanced diffusion model that smartly leverages deformation fields for trustworthy longitudinal brain image completion. Trained on OASIS-3, DF-DiffCom outperforms state-of-the-art methods, improving PSNR by 5.6% and SSIM by 0.12. More importantly, its modality-agnostic nature allows smooth extension to varied MRI modalities, even to attribute maps such as brain tissue segmentation results.
AB - Longitudinal brain MRI is essential for lifespan study, yet high attrition rates often lead to missing data, complicating analysis. Deep generative models have been explored, but most rely solely on image intensity, leading to two key limitations: 1) the fidelity or trustworthiness of the generated brain images are limited, making downstream studies questionable; 2) the usage flexibility is restricted due to fixed guidance rooted in the model structure, restricting full ability to versatile application scenarios. To address these challenges, we introduce DF-DiffCom, a Kolmogorov-Arnold Networks (KAN)-enhanced diffusion model that smartly leverages deformation fields for trustworthy longitudinal brain image completion. Trained on OASIS-3, DF-DiffCom outperforms state-of-the-art methods, improving PSNR by 5.6% and SSIM by 0.12. More importantly, its modality-agnostic nature allows smooth extension to varied MRI modalities, even to attribute maps such as brain tissue segmentation results.
KW - Brain MRI
KW - Deformation Field
KW - Diffusion Model
KW - Longitudinal Generation
KW - Trustworthy Medicine
UR - https://ieeexplore.ieee.org/document/11515615
UR - https://www.scopus.com/pages/publications/105041628462
U2 - 10.1109/ISBI61048.2026.11515615
DO - 10.1109/ISBI61048.2026.11515615
M3 - Conference publication
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)
PB - IEEE
ER -