TY - GEN
T1 - Contrast Flow Pattern and Cross-Phase Specificity-Aware Diffusion Model for NCCT-to-Multiphase CECT Synthesis
AU - Zheng, Kaiyi
AU - Huang, Mu
AU - Li, Xinming
AU - Ma, Jianhua
AU - Feng, Qianjin
AU - Yang, Wei
AU - Zhong, Liming
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Multiphase contrast-enhanced computed tomography (CT) is clinically significant in providing vascular structure and lesion phase-specific enhancements. Yet, its clinical utility is constrained by intrinsic contrast agent-associated risks (e.g., nephrotoxicity, allergic reactions) and multiphase cumulative radiation exposure. To tackle this, synthesizing contrast-enhanced CT (CECT) using non-contrast CT (NCCT) offers a potential alternative. However, achieving a high-quality synthesis of multiphase CECT remains challenging due to the contrast agent (CA)-induced complex contrast flow dynamics and the specific variations across phases. Therefore, this paper proposes a contrast flow pattern and cross-phase specificity-aware diffusion model for NCCT-to-multiphase CECT synthesis. Specifically, a contrast flow pattern learning mechanism is integrated into the conditional diffusion model, which enables orderly phase transitions while ensuring anatomically and temporally coherent enhancement synthesis. Furthermore, a phase distinction network is introduced to align cross-phase specificity features with the contrast features in synthesized CECT images. Experimental results on multicenter abdomen CT datasets have demonstrated the superiority of our method compared to state-of-the-art methods.
AB - Multiphase contrast-enhanced computed tomography (CT) is clinically significant in providing vascular structure and lesion phase-specific enhancements. Yet, its clinical utility is constrained by intrinsic contrast agent-associated risks (e.g., nephrotoxicity, allergic reactions) and multiphase cumulative radiation exposure. To tackle this, synthesizing contrast-enhanced CT (CECT) using non-contrast CT (NCCT) offers a potential alternative. However, achieving a high-quality synthesis of multiphase CECT remains challenging due to the contrast agent (CA)-induced complex contrast flow dynamics and the specific variations across phases. Therefore, this paper proposes a contrast flow pattern and cross-phase specificity-aware diffusion model for NCCT-to-multiphase CECT synthesis. Specifically, a contrast flow pattern learning mechanism is integrated into the conditional diffusion model, which enables orderly phase transitions while ensuring anatomically and temporally coherent enhancement synthesis. Furthermore, a phase distinction network is introduced to align cross-phase specificity features with the contrast features in synthesized CECT images. Experimental results on multicenter abdomen CT datasets have demonstrated the superiority of our method compared to state-of-the-art methods.
KW - Contrast agent-free
KW - Diffusion model
KW - Image synthesis
KW - Multiphase CECT
UR - https://www.scopus.com/pages/publications/105017844964
U2 - 10.1007/978-3-032-04965-0_9
DO - 10.1007/978-3-032-04965-0_9
M3 - 会议稿件
AN - SCOPUS:105017844964
SN - 9783032049643
T3 - Lecture Notes in Computer Science
SP - 89
EP - 99
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Y2 - 23 September 2025 through 27 September 2025
ER -