标题： CAPRI-CT：计算机断层扫描中图像质量优化的因果分析与预测推理 显示英文标题

标题： CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomography

摘要： 在计算机断层扫描（CT）中，实现高质量图像同时最小化辐射暴露仍然是一个关键的临床挑战。 本文介绍了 CAPRI-CT，一种用于CT成像中图像质量优化的因果分析和预测推理的新颖因果感知深度学习框架。 CAPRI-CT将图像数据与采集元数据（如管电压、管电流和对比剂类型）相结合，以建模影响图像质量的潜在因果关系。 采用变分自编码器（VAEs）的集成方法来从观察数据中提取有意义的特征并生成因果表示，包括CT图像和相关的成像参数。 这些输入特征被融合以预测信噪比（SNR）并支持反事实推断，从而实现假设性模拟，例如对比剂（类型和浓度）或扫描参数的变化。 CAPRI-CT使用集成学习方法进行训练和验证，实现了强大的预测性能。 通过促进预测和可解释性， CAPRI-CT提供了可操作的见解，可以帮助放射科医生和技术人员设计更高效的CT协议，而无需重复的物理扫描。 源代码和数据集可在 https://github.com/SnehaGeorge22/capri-ct 公开获取。 显示英文摘要

摘要： 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.