Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the \textbf{Volume Control Scalar (VCS)}, a standardized residual that decouples organ size from body habitus, enabling interpretable volume modulation. Organ masks are synthesized sequentially, conditioning on the body mask and previously generated structures to preserve global anatomical coherence while supporting independent, multi-organ control. Across 11 abdominal organs, the proposed framework achieves strong geometric fidelity (e.g., liver dice $0.83 \pm 0.05$), stable single-organ calibration over $[-3,+3]$ VCS, and disentangled multi-organ modulation. To showcase clinical utility with a hepatomegaly cohort selected from MERLIN, Wasserstein-based VCS selection reduces distributional distance of training data by 73.6\% . These results demonstrate calibrated, distribution-aware anatomical generation suitable for controllable abdominal phantom construction and simulation studies.

翻译：计算体模在医学影像研究中被广泛应用，然而当前能够生成可控且具有临床意义的解剖结构变异系统仍然有限。我们提出AbdomenGen——一种用于可控腹部解剖结构生成的序列化体积条件扩散框架。我们引入**体积控制标量(Volume Control Scalar, VCS)**，这是一种标准化的残差量，可解耦器官尺寸与体型特征，从而实现可解释的体积调控。器官掩膜按顺序合成，以身体掩膜和先前生成的结构为条件，在保持全局解剖结构连贯性的同时支持独立的多器官控制。在11个腹部器官上，所提出的框架实现了高几何保真度（例如肝脏Dice系数$0.83 \pm 0.05$）、在$[-3,+3]$ VCS范围内的稳定单器官校准以及解耦的多器官调控。为展示临床实用性，从MERLIN数据集中选取肝肿大队列，基于Wasserstein距离的VCS选择将训练数据的分布距离降低了73.6%。这些结果表明，该生成方法具有校准性及分布感知能力，适用于可控腹部体模构建与仿真研究。