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

TL;DR

CAPRI-CT is a causal-aware deep learning framework that models causal relationships in CT imaging to optimize image quality and reduce radiation exposure through predictive reasoning and counterfactual analysis.

Contribution

It introduces a novel ensemble VAE-based method for causal feature extraction and supports counterfactual inference in CT image quality optimization.

Findings

High predictive accuracy for SNR from imaging parameters

Effective counterfactual simulations for protocol optimization

Enhanced interpretability of causal relationships in CT imaging

Abstract

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.