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: this https URL.
Abstract (translated)
三维医学图像生成对于数据增强和保护患者隐私至关重要，因此需要可靠且高效的模型以适用于临床实践。然而，当前的方法存在解剖学精确度有限、轴向长度受限以及计算成本高昂等问题，这使得这些方法在资源和技术基础设施相对匮乏的地区难以实施。我们在此介绍TRACE框架，它利用二维多模态条件下的扩散方法生成与时空对齐的三维医学图像。TRACE将连续的2D切片视为视频帧对，并结合分割先验和放射学报告来实现解剖结构对齐；同时，通过引入光流技术以维持时间一致性。在推理阶段，采用重叠帧策略将这些帧对连接成一个可变长度序列，并最终重建为时空及解剖学上均一致的三维体积图像。实验结果表明，TRACE能够有效地平衡计算效率与保持解剖精确度和时空连续性的需求。代码可在以下链接获取：[此 URL]（请使用实际提供的URL）。
URL
https://arxiv.org/abs/2507.00802
