Clip-Driven Universal Model for Multi-Material Decomposition in Dual-Energy CT

Abstract

Dual-energy computed tomography (DECT) offers quantitative insights and facilitates material decomposition, aiding in precise diagnosis and treatment planning. However, existing methods for material decomposition, often tailored to specific material types, need more generalizability and increase computational load with each additional material. We propose a CLIP-Driven Universal Model for adaptive Multi-Material Decomposition (MMD) to tackle this challenge. This model utilizes the semantic capabilities of text embeddings from Contrastive Language-Image Pre-training (CLIP), allowing a single network to manage structured feature embedding for multiple materials. A novel Siamese encoder and differential map fusion technique have also been integrated to enhance the decomposition accuracy while maintaining robustness across various conditions. Experiments on the simulated and physical patient studies have evidenced our model's superiority over traditional methods. Notably, it has significantly improved the Dice Similarity Coefficient—4.1%. These results underscore the potential of our network in clinical MMD applications, suggesting a promising avenue for enhancing DECT imaging analysis.