标题： TRACE：具有增强效率的时空可靠解剖条件3D CT生成 显示英文标题

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

摘要： 三维医学图像生成对于数据增强和患者隐私至关重要，需要适用于临床实践的可靠且高效的模型。 然而，当前的方法在解剖精度方面存在限制，轴向长度受限，并且计算成本较高，使得它们难以在资源和基础设施有限的地区使用。 我们引入了TRACE，这是一种框架，利用二维多模态条件扩散方法生成具有时空对齐的三维医学图像。 TRACE将连续的二维切片建模为视频帧对，结合分割先验和放射科报告以实现解剖对齐，并利用光流来保持时间一致性。 在推理过程中，重叠帧策略将帧对连接成一个灵活长度的序列，并重建为具有时空和解剖对齐的三维体积。 实验结果表明，TRACE能够有效平衡计算效率与保持解剖精度和时空一致性的需求。 代码可在以下链接获取：https://github.com/VinyehShaw/TRACE. 显示英文摘要

摘要： 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.