TY - GEN
T1 - UltraTwin
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
AU - Yu, Junxuan
AU - Duan, Yaofei
AU - Huang, Yuhao
AU - Wang, Yu
AU - Ling, Rongbo
AU - Luo, Weihao
AU - Zhang, Ang
AU - Xu, Jingxian
AU - Ni, Qiongying
AU - Zhou, Yongsong
AU - Li, Binghan
AU - Dou, Haoran
AU - Liu, Liping
AU - Chu, Yanfen
AU - Geng, Feng
AU - Sheng, Zhe
AU - Ding, Zhifeng
AU - Zhang, Dingxin
AU - Huang, Rui
AU - Zhang, Yuhang
AU - Xu, Xiaowei
AU - Tan, Tao
AU - Ni, Dong
AU - Gou, Zhongshan
AU - Yang, Xin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreover, 3D US is limited by low resolution, small field of view and scarce availability in practice. Constructing the cardiac anatomical twin from 2D images is promising to provide precise treatment planning and clinical quantification. However, it remains challenging due to the rare paired data, complex structures, and US noises. In this study, we introduce a novel generative framework UltraTwin, to obtain cardiac anatomical twin from sparse multi-view 2D US. Our contribution is three-fold. First, pioneered the construction of a real-world and high-quality dataset containing strictly paired multi-view 2D US and CT, and pseudo-paired data. Second, we propose a coarse-to-fine scheme to achieve hierarchical reconstruction optimization. Last, we introduce an implicit autoencoder for topology-aware constraints. Extensive experiments show that UltraTwin reconstructs high-quality anatomical twins versus strong competitors. We believe it advances anatomical twin modeling for potential applications in personalized cardiac care.
AB - Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreover, 3D US is limited by low resolution, small field of view and scarce availability in practice. Constructing the cardiac anatomical twin from 2D images is promising to provide precise treatment planning and clinical quantification. However, it remains challenging due to the rare paired data, complex structures, and US noises. In this study, we introduce a novel generative framework UltraTwin, to obtain cardiac anatomical twin from sparse multi-view 2D US. Our contribution is three-fold. First, pioneered the construction of a real-world and high-quality dataset containing strictly paired multi-view 2D US and CT, and pseudo-paired data. Second, we propose a coarse-to-fine scheme to achieve hierarchical reconstruction optimization. Last, we introduce an implicit autoencoder for topology-aware constraints. Extensive experiments show that UltraTwin reconstructs high-quality anatomical twins versus strong competitors. We believe it advances anatomical twin modeling for potential applications in personalized cardiac care.
KW - 3D Cardiac Reconstruction
KW - Anatomical Twins
KW - Diffusion Transformer
KW - Multi-view 2D Ultrasound
UR - https://www.scopus.com/pages/publications/105018116729
U2 - 10.1007/978-3-032-05325-1_58
DO - 10.1007/978-3-032-05325-1_58
M3 - Conference contribution
AN - SCOPUS:105018116729
SN - 9783032053244
T3 - Lecture Notes in Computer Science
SP - 608
EP - 617
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Park, Jinah
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 September 2025 through 27 September 2025
ER -