Conditional medical image generation plays an important role in many clinically relevant imaging tasks. However, existing methods still face a fundamental challenge in balancing inference efficiency, patient-specific fidelity, and distribution-level plausibility, particularly in high-dimensional 3D medical imaging. In this work, we propose GDM, a generative drifting framework that reformulates deterministic medical image prediction as a multi-objective learning problem to jointly promote distribution-level plausibility and patient-specific fidelity while retaining one-step inference. GDM extends drifting to 3D medical imaging through an attractive-repulsive drift that minimizes the discrepancy between the generator pushforward and the target distribution. To enable stable drifting-based learning in 3D volumetric data, GDM constructs a multi-level feature bank from a medical foundation encoder to support reliable affinity estimation and drifting field computation across complementary global, local, and spatial representations. In addition, a gradient coordination strategy in the shared output space improves optimization balance under competing distribution-level and fidelity-oriented objectives. We evaluate the proposed framework on two representative tasks, MRI-to-CT synthesis and sparse-view CT reconstruction. Experimental results show that GDM consistently outperforms a wide range of baselines, including GAN-based, flow-matching-based, and SDE-based generative models, as well as supervised regression methods, while improving the balance among anatomical fidelity, quantitative reliability, perceptual realism, and inference efficiency. These findings suggest that GDM provides a practical and effective framework for conditional 3D medical image generation.

翻译：条件性医学图像生成在许多临床相关成像任务中扮演着重要角色。然而，现有方法仍面临一个根本性挑战：如何在推理效率、患者特异性保真度和分布层面合理性之间取得平衡，尤其是在高维度三维医学成像领域。本文提出了一种生成性漂移框架GDM，它将确定性医学图像预测重新定义为多目标学习问题，在保持单步推理的同时协同提升分布层面合理性和患者特异性保真度。GDM通过吸引-排斥漂移机制将漂移扩展至三维医学成像，最小化生成器前向分布与目标分布之间的差异。为实现在三维体数据上的稳定漂移学习，GDM从医学基础编码器构建多级特征库，支持跨互补的全局、局部和空间表征的可靠亲和性估计与漂移场计算。此外，共享输出空间中的梯度协调策略改善了分布层面与保真度导向目标间竞争下的优化平衡。我们在两个代表性任务（MRI到CT合成及稀疏视图CT重建）上评估了所提框架。实验结果表明，GDM在解剖保真度、定量可靠性、感知真实性与推理效率间的平衡上，持续优于包括基于GAN、基于流匹配、基于SDE的生成模型以及有监督回归方法在内的广泛基线。这些发现表明GDM为条件性三维医学图像生成提供了实用有效的框架。