Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

2026-08-07

AI Summary

This work addresses the absence of a unified classification framework for conditional 3D CT generation methods, which hinders systematic comparison and identification of critical design choices. To resolve this, the paper proposes a taxonomy centered on conditioning mechanisms, structured around three orthogonal dimensions: external knowledge type (Knowledge), knowledge integration paradigm (Integration), and generative architecture (Architecture), thereby defining a unified design space denoted as K×I×A. Through a comprehensive review of existing approaches, this framework not only clarifies prevailing technical pathways but also uncovers underexplored research directions. The resulting taxonomy offers theoretical guidance and a foundation for innovation in the design of future conditional 3D medical image generation methods.

Abstract

Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constraints on semantic content, structural properties, and variability. In 3D Computed Tomography (CT), such control is essential for clinical applications, including data augmentation, privacy-preserving data sharing, and the simulation of specific anatomical or pathological scenarios. While research on conditional 3D CT generation has expanded rapidly, the diversity of existing approaches makes systematic comparison difficult and obscures fundamental design choices. In this survey, we propose a conditioning-centric taxonomy that organizes the literature along three orthogonal dimensions: the type of external knowledge (K), the knowledge integration paradigm (I), and the generative architecture (A). This factorization defines an explicit design space (K x I x A) that provides a unified perspective on prior work. Using this framework, we systematize existing methods, identify dominant trends and recurring design patterns, and highlight underexplored regions of the design space that point toward promising directions for future research.

Problem

• 3D CT generation
• conditional generation
• knowledge-guided synthesis
• taxonomy
• generative models

Innovation

• conditioning-centric taxonomy
• knowledge-guided generation
• 3D CT synthesis
• generative design space
• external knowledge integration