Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model

• URL: http://arxiv.org/abs/2505.04522v1
• Date: Wed, 07 May 2025 15:53:56 GMT
• Title: Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model
• Authors: Pengfei Guo, Can Zhao, Dong Yang, Yufan He, Vishwesh Nath, Ziyue Xu, Pedro R. A. S. Bassi, Zongwei Zhou, Benjamin D. Simon, Stephanie Anne Harmon, Baris Turkbey, Daguang Xu,
• Abstract summary: We introduce Text2CT, a novel approach for synthesizing 3D CT volumes from textual descriptions.<n>The proposed framework encodes medical text into latent representations and decodes them into high-resolution 3D CT scans.
• Score: 17.778156861799207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating 3D CT volumes from descriptive free-text inputs presents a transformative opportunity in diagnostics and research. In this paper, we introduce Text2CT, a novel approach for synthesizing 3D CT volumes from textual descriptions using the diffusion model. Unlike previous methods that rely on fixed-format text input, Text2CT employs a novel prompt formulation that enables generation from diverse, free-text descriptions. The proposed framework encodes medical text into latent representations and decodes them into high-resolution 3D CT scans, effectively bridging the gap between semantic text inputs and detailed volumetric representations in a unified 3D framework. Our method demonstrates superior performance in preserving anatomical fidelity and capturing intricate structures as described in the input text. Extensive evaluations show that our approach achieves state-of-the-art results, offering promising potential applications in diagnostics, and data augmentation.

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