TRACE: 基于时间可靠性的解剖条件3D CT生成与增强效率
TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
AI总结：
TRACE通过2D多模态条件扩散方法生成具有时空对齐的3D CT图像，提升生成效率和解剖保真度。
摘要：
3D医学图像生成对于数据增强和患者隐私至关重要，需要可靠且高效的模型以适应临床实践。然而，当前方法存在解剖保真度有限、轴向长度受限以及计算成本高的问题，使其难以在资源和基础设施有限的地区应用。我们引入TRACE框架，通过2D多模态条件扩散方法生成具有时空对齐的3D医学图像。TRACE将顺序的2D切片视为视频帧对，结合分割先验和放射科报告实现解剖对齐，并利用光流保持时间一致性。在推理过程中，重叠帧策略将帧对连接成灵活长度的序列，重建为具有时空和解剖对齐的3D体积。实验结果表明，TRACE在保持解剖保真度和时空一致性的同时，有效平衡了计算效率。代码可在：https://github.com/VinyehShaw/TRACE获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
