TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency

• URL: http://arxiv.org/abs/2507.00802v1
• Date: Tue, 01 Jul 2025 14:35:39 GMT
• Title: TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
• Authors: Minye Shao, Xingyu Miao, Haoran Duan, Zeyu Wang, Jingkun Chen, Yawen Huang, Xian Wu, Jingjing Deng, Yang Long, Yefeng Zheng,
• Abstract summary: TRACE is a framework that generates 3D medical images with temporal alignment.<n>An overlapping-frame frame pairs pairs into a flexible length sequence, reconstructed into atemporally and anatomically aligned 3D volume.
• Score: 40.82927972746919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D medical image generation is essential for data augmentation and patient privacy, calling for reliable and efficient models suited for clinical practice. However, current methods suffer from limited anatomical fidelity, restricted axial length, and substantial computational cost, placing them beyond reach for regions with limited resources and infrastructure. We introduce TRACE, a framework that generates 3D medical images with spatiotemporal alignment using a 2D multimodal-conditioned diffusion approach. TRACE models sequential 2D slices as video frame pairs, combining segmentation priors and radiology reports for anatomical alignment, incorporating optical flow to sustain temporal coherence. During inference, an overlapping-frame strategy links frame pairs into a flexible length sequence, reconstructed into a spatiotemporally and anatomically aligned 3D volume. Experimental results demonstrate that TRACE effectively balances computational efficiency with preserving anatomical fidelity and spatiotemporal consistency. Code is available at: https://github.com/VinyehShaw/TRACE.

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