用因果模型优化CT图像质量，减少辐射暴露
CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomography
- 融合图像与扫描参数建模因果关系
- 可预测信噪比并支持假设推演（如改造影剂）
- 帮助医生设计更优扫描方案，无需反复试扫
在计算机断层扫描（CT）中，如何在保证图像质量的同时降低辐射剂量仍是临床关键挑战。本文提出CAPRI-CT——一种用于CT图像质量优化的因果分析与预测推理框架。该框架结合图像数据与采集元数据（如管电压、管电流、对比剂类型），建模影响图像质量的潜在因果关系。采用变分自编码器（VAE）集成模型从观测数据（包括CT图像和成像参数）中提取有意义特征并生成因果表示。通过特征融合，模型可预测信噪比（SNR），并支持反事实推断，实现对对比剂类型或浓度、扫描参数等变化的“如果……会怎样”模拟。CAPRI-CT基于集成学习方法训练与验证，具备强预测性能。其兼具预测能力与可解释性，为放射科医生和技术人员提供可行动的洞察，助力制定高效扫描方案，避免重复物理扫描。源代码与数据集已公开于https://github.com/SnehaGeorge22/capri-ct。
原文摘要 · Abstract (English)
In computed tomography (CT), achieving high image quality while minimizing radiation exposure remains a key clinical challenge. This paper presents CAPRI-CT, a novel causal-aware deep learning framework for Causal Analysis and Predictive Reasoning for Image Quality Optimization in CT imaging. CAPRI-CT integrates image data with acquisition metadata (such as tube voltage, tube current, and contrast agent types) to model the underlying causal relationships that influence image quality. An ensemble of Variational Autoencoders (VAEs) is employed to extract meaningful features and generate causal representations from observational data, including CT images and associated imaging parameters. These input features are fused to predict the Signal-to-Noise Ratio (SNR) and support counterfactual inference, enabling what-if simulations, such as changes in contrast agents (types and concentrations) or scan parameters. CAPRI-CT is trained and validated using an ensemble learning approach, achieving strong predictive performance. By facilitating both prediction and interpretability, CAPRI-CT provides actionable insights that could help radiologists and technicians design more efficient CT protocols without repeated physical scans. The source code and dataset are publicly available at https://github.com/SnehaGeorge22/capri-ct.
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