  The limited availability of 3D medical image datasets, due to privacy
concerns and high collection or annotation costs, poses significant challenges
in the field of medical imaging. While a promising alternative is the use of
synthesized medical data, there are few solutions for realistic 3D medical
image synthesis due to difficulties in backbone design and fewer 3D training
samples compared to 2D counterparts. In this paper, we propose GEM-3D, a novel
generative approach to the synthesis of 3D medical images and the enhancement
of existing datasets using conditional diffusion models. Our method begins with
a 2D slice, noted as the informed slice to serve the patient prior, and
propagates the generation process using a 3D segmentation mask. By decomposing
the 3D medical images into masks and patient prior information, GEM-3D offers a
flexible yet effective solution for generating versatile 3D images from
existing datasets. GEM-3D can enable dataset enhancement by combining informed
slice selection and generation at random positions, along with editable mask
volumes to introduce large variations in diffusion sampling. Moreover, as the
informed slice contains patient-wise information, GEM-3D can also facilitate
counterfactual image synthesis and dataset-level de-enhancement with desired
control. Experiments on brain MRI and abdomen CT images demonstrate that GEM-3D
is capable of synthesizing high-quality 3D medical images with volumetric
consistency, offering a straightforward solution for dataset enhancement during
inference. The code is available at https://github.com/HKU-MedAI/GEM-3D.
