This paper introduces an innovative methodology for producing high-quality 3D
lung CT images guided by textual information. While diffusion-based generative
models are increasingly used in medical imaging, current state-of-the-art
approaches are limited to low-resolution outputs and underutilize radiology
reports' abundant information. The radiology reports can enhance the generation
process by providing additional guidance and offering fine-grained control over
the synthesis of images. Nevertheless, expanding text-guided generation to
high-resolution 3D images poses significant memory and anatomical
detail-preserving challenges. Addressing the memory issue, we introduce a
hierarchical scheme that uses a modified UNet architecture. We start by
synthesizing low-resolution images conditioned on the text, serving as a
foundation for subsequent generators for complete volumetric data. To ensure
the anatomical plausibility of the generated samples, we provide further
guidance by generating vascular, airway, and lobular segmentation masks in
conjunction with the CT images. The model demonstrates the capability to use
textual input and segmentation tasks to generate synthesized images. The
results of comparative assessments indicate that our approach exhibits superior
performance compared to the most advanced models based on GAN and diffusion
techniques, especially in accurately retaining crucial anatomical features such
as fissure lines, airways, and vascular structures. This innovation introduces
novel possibilities. This study focuses on two main objectives: (1) the
development of a method for creating images based on textual prompts and
anatomical components, and (2) the capability to generate new images
conditioning on anatomical elements. The advancements in image generation can
be applied to enhance numerous downstream tasks.

基于文本信息和解剖组成的方法生成基于文本提示的图像，并应用于多种下游任务。